Science, Faculty of
DSpace
UBCV
Li, Chenkai
Doctor of Philosophy - PhD
University of British Columbia
The growing global health concern of antibiotic resistance is prompting researchers to seek substitutes for conventional antibiotics. Antimicrobial peptides (AMPs), a diverse class of short and often cationic biological molecules, are gaining attention as promising candidates. While direct, large-scale wet lab screening is time-consuming and costly, using high-throughput bioinformatics tools to discover and design novel AMPs is an attractive approach. In this thesis, I introduce in silico tools for AMP discovery and design with machine learning models, and present the novel AMPs revealed by those methods. The in silico discovery of AMPs typically involves the investigation of huge genomics, transcriptomics, or protein datasets, and accurate methods to sift through such large volumes of candidate sequences are required. In this thesis, I introduce AMPlify, a deep learning based tool for AMP prediction, improving upon the state-of-the-art methods by incorporating attention mechanisms. By integrating AMPlify into bioinformatics pipelines or workflows, four novel AMPs with proven antimicrobial activity have been identified from the Rana [Lithobates] catesbeiana (bullfrog) genome, as well as 13 other novel AMPs mined from the UniProtKB/Swiss-Prot database. On the other hand, the potential sequence space of amino acids is combinatorially vast, allowing for the exploration of more AMPs that may not exist in nature to further expand the current arsenal of peptide-based therapeutics. However, manual design of novel synthetic AMPs requires prior field knowledge, restricting its throughput. In silico sequence generation methods for de novo AMP design stand out to be a high-throughput way to unearth novel synthetic AMPs. In this thesis, I introduce a recurrent neural network based tool, named AMPd-Up, for AMP sequence generation, and demonstrate its performance over existing methods. With AMPd-Up, 40 novel synthetic AMPs have been designed with proven antimicrobial activity against the bacterial strains tested in vitro. I demonstrate the utility of AMPlify and AMPd-Up in the discovery and design of novel AMPs, and I expect these tools to play an important role in our fight against antibiotic resistance.
MACHINE LEARNING FOR ANTIMICROBIAL PEPTIDE DISCOVERY AND DESIGN by Chenkai Li M.Sc., University College London, 2016 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Bioinformatics) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) February 2024 © Chenkai Li, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Machine learning for antimicrobial peptide discovery and design submitted by Chenkai Li in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Bioinformatics Examining Committee: Inanç Birol, Professor, Medical Genetics, University of British Columbia Supervisor Raymond Ng, Professor, Computer Science, University of British Columbia Supervisory Committee Member Linda M.N. Hoang, Clinical Professor, Pathology and Laboratory Medicine, University of British Columbia Supervisory Committee Member Leonard J. Foster, Professor, Biochemistry and Molecular Biology, University of British Columbia University Examiner Colin Ross, Professor, Pharmaceutical Sciences, University of British Columbia University Examiner Additional Supervisory Committee Members: Sara Mostafavi, Associate Professor, Computer Science and Engineering, University of Washington Supervisory Committee Member iii Abstract The growing global health concern of antibiotic resistance is prompting researchers to seek substitutes for conventional antibiotics. Antimicrobial peptides (AMPs), a diverse class of short and often cationic biological molecules, are gaining attention as promising candidates. While direct, large-scale wet lab screening is time-consuming and costly, using high-throughput bioinformatics tools to discover and design novel AMPs is an attractive approach. In this thesis, I introduce in silico tools for AMP discovery and design with machine learning models, and present the novel AMPs revealed by those methods. The in silico discovery of AMPs typically involves the investigation of huge genomics, transcriptomics, or protein datasets, and accurate methods to sift through such large volumes of candidate sequences are required. In this thesis, I introduce AMPlify, a deep learning based tool for AMP prediction, improving upon the state-of-the-art methods by incorporating attention mechanisms. By integrating AMPlify into bioinformatics pipelines or workflows, four novel AMPs with proven antimicrobial activity have been identified from the Rana [Lithobates] catesbeiana (bullfrog) genome, as well as 13 other novel AMPs mined from the UniProtKB/Swiss-Prot database. On the other hand, the potential sequence space of amino acids is combinatorially vast, allowing for the exploration of more AMPs that may not exist in nature to further expand the current arsenal of peptide-based therapeutics. However, manual design of novel synthetic AMPs requires prior field knowledge, restricting its throughput. In silico sequence generation methods for de iv novo AMP design stand out to be a high-throughput way to unearth novel synthetic AMPs. In this thesis, I introduce a recurrent neural network based tool, named AMPd-Up, for AMP sequence generation, and demonstrate its performance over existing methods. With AMPd-Up, 40 novel synthetic AMPs have been designed with proven antimicrobial activity against the bacterial strains tested in vitro. I demonstrate the utility of AMPlify and AMPd-Up in the discovery and design of novel AMPs, and I expect these tools to play an important role in our fight against antibiotic resistance. v Lay Summary Antimicrobial peptides (AMPs) are a type of biological molecules attracting increasing attention as potential substitutes for conventional antibiotics in the treatment of multidrug-resistant bacterial infections. High-throughput in silico strategies are gaining interest to accelerate the exploration of novel AMPs. The goal of this thesis is to develop novel computational methods for the discovery and design of novel AMPs. In this thesis, I present two bioinformatics tools for AMP prediction and AMP sequence generation, both of which improve upon existing methods and rely on machine learning techniques. I demonstrate the utility of these tools by showcasing their success in the discovery of novel AMPs from the bullfrog genome and a comprehensive protein sequence database, as well as in the de novo design of novel synthetic AMPs, respectively. I expect such tools to make significant contributions to this field of research. vi Preface Under the supervision of Dr. Inanç Birol, I performed the majority of dry lab work and data analysis, with all in vitro validations conducted by my wet lab collaborators. Chapter 1 and Chapter 5: These two chapters are my original work. A version of these materials is part of the publications or manuscripts described in Chapters 2, Chapters 3, and Chapters 4. Chapter 2: A version of this material has been published as Chenkai Li, Darcy Sutherland, S. Austin Hammond, Chen Yang, Figali Taho, Lauren Bergman, Simon Houston, René L. Warren, Titus Wong, Linda M.N. Hoang, Caroline E. Cameron, Caren C. Helbing, and Inanç Birol. “AMPlify: attentive deep learning model for discovery of novel antimicrobial peptides effective against WHO priority pathogens” BMC Genomics, 2022;23:77 (https://doi.org/10.1186/s12864-022-08310-4). IB, CCH, CEC, LMNH, and TW conceived the presented work. IB and CL designed the model with help from CY. CL implemented the model with help from CY, FT, and RLW. IB and SAH designed the AMP discovery pipeline with help from CL. CEC, LMNH, and TW provided the bacterial strains tested. SH, CCH, CEC, LMNH, and TW developed the antimicrobial susceptibility testing protocol with input from DS, LB, and SAH. DS, LB, SAH, and SH conducted antimicrobial susceptibility testing. CL, DS, and SAH drafted the manuscript, and all authors were involved in its revision. vii Chapter 3: Part of this material has been published as Chenkai Li, René L. Warren, and Inanç Birol. “Models and data of AMPlify: a deep learning tool for antimicrobial peptide prediction” BMC Research Notes, 2023;16:11 (https://doi.org/10.1186/s13104-023-06279-1). IB conceived the presented work. CL implemented the model. CL collected the data with help from RLW. CL drafted the manuscript, and all authors were involved in its revision. The other part of this material has been described in a draft manuscript, which is about to be submitted to a journal for peer-review as Chenkai Li, Darcy Sutherland, Ali Salehi, Amelia Richter, Diana Lin, Sambina Islam Aninta, Hossein Ebrahimikondori, Anat Yanai, Lauren Coombe, René L. Warren, Monica Kotkoff, Linda M.N. Hoang, Caren C. Helbing, and Inanç Birol. “Mining the UniProtKB/Swiss-Prot protein sequence database for antimicrobials”. Please note that the manuscript title is preliminary and is subject to change according to different journal requirements. IB, RLW, LMNH, and CCH conceived the presented work with help from CL. MK conducted project administration. CL and IB designed the AMP mining workflow. CL collected and curated the datasets. CL implemented the AMP mining workflow with help from DS, DL, SIA, LC, and RLW. CL conducted peptide structure prediction with help from HE and AR. CL, DS, AY, RLW, and IB performed peptide selection. AS, DS, AR, and AY conducted antimicrobial susceptibility and hemolysis testing. CL, DS, AS, AR, AY, LC, RLW, and IB analyzed the results. CL drafted the manuscript, and all authors were involved in its revision. Chapter 4: A version of this material has been submitted to a journal for peer-review as Chenkai Li, Darcy Sutherland, Amelia Richter, Lauren Coombe, Anat Yanai, René L. Warren, Monica Kotkoff, Fraser Hof, Linda M.N. Hoang, Caren C. Helbing, and Inanç Birol. “De novo synthetic antimicrobial peptide design with a recurrent neural network”. Please note that the manuscript viii title is preliminary and is subject to change according to different journal requirements. IB, CL, LMNH, CCH, and FH conceived the presented work. MK conducted project administration. CL and IB designed the AMP sequence generation method. CL implemented the model. CL, DS, AY, RLW, and IB performed peptide selection. DS, AY, and AR conducted antimicrobial susceptibility and hemolysis testing. CL, RLW, LC, DS, AY, AR, and IB analyzed the results. CL drafted the manuscript, and all authors were involved in its revision. ix Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ......................................................................................................................... ix List of Tables .............................................................................................................................. xiv List of Figures ............................................................................................................................. xvi List of Abbreviations ............................................................................................................... xviii Acknowledgements .................................................................................................................... xxi Chapter 1: Introduction ................................................................................................................1 1.1 Background ..................................................................................................................... 1 1.1.1 Antibiotic resistance .................................................................................................... 1 1.1.2 Antimicrobial peptides (AMPs) .................................................................................. 2 1.1.2.1 Basic facts of AMPs ............................................................................................ 2 1.1.2.2 AMP structures ................................................................................................... 3 1.1.2.3 Sources of naturally-occurring AMPs ................................................................ 4 1.1.2.4 AMP precursors .................................................................................................. 6 1.1.2.5 AMP modes of action ......................................................................................... 6 1.1.3 Machine learning for sequence classification and generation .................................... 8 1.1.4 AMP discovery and design ....................................................................................... 11 1.1.4.1 AMP discovery ................................................................................................. 12 1.1.4.2 AMP design ...................................................................................................... 13 x 1.2 Motivation ..................................................................................................................... 15 1.3 Research objectives ....................................................................................................... 16 Chapter 2: AMP discovery from genomic resources ................................................................17 2.1 Publication note ............................................................................................................ 17 2.2 Author summary ........................................................................................................... 17 2.3 Introduction ................................................................................................................... 18 2.4 Methods......................................................................................................................... 20 2.4.1 Generation of the datasets ......................................................................................... 20 2.4.2 Model implementation .............................................................................................. 22 2.4.3 Hyperparameter tuning and model architecture ........................................................ 23 2.4.4 Model evaluation ...................................................................................................... 27 2.4.5 AMP discovery pipeline ........................................................................................... 29 2.4.6 Antimicrobial susceptibility testing .......................................................................... 31 2.4.6.1 Bacterial isolates ............................................................................................... 31 2.4.6.2 Determination of minimum inhibitory concentrations ..................................... 32 2.4.6.3 Determination of minimum bactericidal concentrations ................................... 32 2.5 Results ........................................................................................................................... 33 2.5.1 Evaluation of model architecture .............................................................................. 33 2.5.2 Comparison with state-of-the-art methods ................................................................ 35 2.5.3 AMP discovery ......................................................................................................... 39 2.5.4 Antimicrobial susceptibility testing .......................................................................... 42 2.6 Discussion and conclusions .......................................................................................... 46 Chapter 3: AMP discovery from protein sequence databases .................................................49 xi 3.1 Publication note ............................................................................................................ 49 3.2 Author summary ........................................................................................................... 50 3.3 Introduction ................................................................................................................... 50 3.4 Methods......................................................................................................................... 52 3.4.1 Generation of the datasets ......................................................................................... 52 3.4.2 Model training and evaluation .................................................................................. 54 3.4.3 AMP mining workflow ............................................................................................. 55 3.4.4 Sequence and structural similarities between peptides ............................................. 57 3.4.5 Selecting putative AMPs for validation .................................................................... 58 3.4.6 Antimicrobial susceptibility testing .......................................................................... 59 3.4.7 Hemolysis assay ........................................................................................................ 60 3.5 Results ........................................................................................................................... 61 3.5.1 Evaluation of model performance ............................................................................. 61 3.5.2 Predicted AMPs ........................................................................................................ 64 3.5.3 In vitro validation results .......................................................................................... 69 3.6 Discussion and conclusions .......................................................................................... 73 Chapter 4: De novo AMP design ................................................................................................79 4.1 Publication note ............................................................................................................ 79 4.2 Author summary ........................................................................................................... 79 4.3 Introduction ................................................................................................................... 79 4.4 Methods......................................................................................................................... 80 4.4.1 Training set ............................................................................................................... 80 4.4.2 Model architecture and implementation ................................................................... 81 xii 4.4.3 Model evaluation ...................................................................................................... 83 4.4.4 Selecting putative AMPs for validation .................................................................... 85 4.4.5 Antimicrobial susceptibility testing .......................................................................... 87 4.4.6 Hemolysis assay ........................................................................................................ 87 4.5 Results ........................................................................................................................... 87 4.5.1 Performance comparison with state-of-the-art methods ........................................... 87 4.5.2 Quality analyses of de novo generated sequences .................................................... 88 4.5.3 In vitro validation results .......................................................................................... 92 4.6 Discussion and conclusions .......................................................................................... 97 Chapter 5: Conclusions and future works ...............................................................................100 5.1 Conclusions ................................................................................................................. 100 5.2 Future works ............................................................................................................... 102 Bibliography ...............................................................................................................................105 Appendices ..................................................................................................................................125 Appendix A Chapter 2 supplementary materials ................................................................. 125 A.1 Supplementary notes ............................................................................................... 125 A.2 Supplementary tables .............................................................................................. 128 A.3 Supplementary figures ............................................................................................ 132 Appendix B Chapter 3 supplementary materials ................................................................. 136 B.1 Supplementary tables .............................................................................................. 136 B.2 Supplementary figures ............................................................................................ 145 Appendix C Chapter 4 supplementary materials ................................................................. 149 C.1 Supplementary tables .............................................................................................. 149 xiii C.2 Supplementary figures ............................................................................................ 156 xiv List of Tables Table 2.1 Performance comparison of the AMPlify balanced model and other AMP prediction tools on the balanced test set ......................................................................................................... 35 Table 2.2 Putative and reported AMP sequences discovered from the bullfrog genome ............. 41 Table 2.3 Minimum inhibitory concentrations and minimum bactericidal concentrations of selected AMP candidates mined from the bullfrog genome following antimicrobial susceptibility testing in vitro ............................................................................................................................... 44 Table 3.1 Performance comparison of AMPlify balanced/imbalanced models and other AMP prediction tools on the balanced test set ....................................................................................... 63 Table 3.2 Performance comparison of AMPlify balanced/imbalanced models and other AMP prediction tools on the imbalanced test set ................................................................................... 63 Table 4.1 Performance comparison of different AMP sequence generation methods ................. 88 Table A.1 Ablation study results of AMPlify model architecture using stratified 5-fold cross-validation on the balanced training set ........................................................................................ 128 Table A.2 Comparison between AMP Scanner Vr.2 and AMPlify cross-validated on all data provided by AMP Scanner Vr.2 .................................................................................................. 128 Table A.3 Performance comparison between AMP Scanner Vr.2 and AMPlify re-trained on the AMP Scanner Vr.2 “Train+Tune” partitions and tested on their “Test” partition ...................... 129 Table A.4 Prediction results of GGN5 and its analogues by different AMP prediction tools .... 130 Table B.1 Characteristics of the 40 putative AMP sequences mined from the UniProtKB/Swiss-Prot database that have been prioritized for synthesis ................................................................ 136 xv Table B.2 Overview of the 40 putative AMP sequences mined from the UniProtKB/Swiss-Prot database that have been prioritized for synthesis regarding their corresponding parent sequence information in the database ......................................................................................................... 140 Table B.3 Seven reference chicken AMP sequences for the 40 putative AMPs mined from the UniProtKB/Swiss-Prot database and prioritized for synthesis ................................................... 142 Table B.4 Antimicrobial susceptibility testing and hemolysis experiment results of the 38 successfully synthesized putative AMPs mined from the UniProtKB/Swiss-Prot database and tested in vitro ............................................................................................................................... 143 Table C.1 Putative AMP sequences generated by AMPd-Up that have been prioritized for synthesis ...................................................................................................................................... 149 Table C.2 Antimicrobial susceptibility testing and hemolysis experiment results of the 58 selected de novo designed putative AMPs in vitro ..................................................................... 153 xvi List of Figures Figure 2.1 Model architecture of AMPlify ................................................................................... 19 Figure 2.2 Performance comparison of different AMP prediction tools based on the test sequence similarities to their corresponding training sets ............................................................................ 37 Figure 2.3 Visualization of the AMPlify balanced model performance and the AMP discovery pipeline application results ........................................................................................................... 39 Figure 3.1 Sequence similarity distributions of the predicted AMPs from the UniProtKB/Swiss-Prot database to known AMPs ...................................................................................................... 64 Figure 3.2 Distribution for the number of predicted mature AMP sequences found within each putative AMP precursor sequence mined from the UniProtKB/Swiss-Prot database .................. 66 Figure 3.3 Categorization of the putative AMP entries identified from the UniProtKB/Swiss-Prot database based on source organisms ............................................................................................. 68 Figure 3.4 Antimicrobial and hemolytic activities of the 13 novel AMPs mined from the UniProtKB/Swiss-Prot database that were active against at least one bacterial strain of Escherichia coli ATCC 25922 and Staphylococcus aureus ATCC 29213 ................................... 73 Figure 4.1 Architecture of the recurrent neural network language model in AMPd-Up .............. 82 Figure 4.2 Length and net charge distributions of the sequences generated by AMPd-Up .......... 90 Figure 4.3 Sequence similarity distribution of the de novo generated sequences to the AMPd-Up training set ..................................................................................................................................... 91 Figure 4.4 In vitro validation results of the 58 selected de novo designed putative AMPs .......... 96 Figure A.1 Workflow of selecting the non-AMP sequences from the UniProtKB/Swiss-Prot database ....................................................................................................................................... 132 xvii Figure A.2 Workflow of the AMP discovery pipeline ............................................................... 133 Figure A.3 Learning curve comparison of single sub-models of AMPlify trained on two different datasets ........................................................................................................................................ 134 Figure A.4 UpSet plot of the 101 candidate mature peptide sequences mined from the bullfrog genome and fed into AMPlify for prediction with regard to the three filters ............................. 135 Figure B.1 AMP mining workflow ............................................................................................. 145 Figure B.2 Length and net charge distributions of the AMPs predicted by AMPlify from the UniProtKB/Swiss-Prot database ................................................................................................. 146 Figure B.3 Structural similarity distributions of the short cationic novel putative AMPs mined from the UniProtKB/Swiss-Prot database to known chicken AMPs .......................................... 147 Figure B.4 Visualization of antimicrobial activity of the 38 tested putative AMPs mined from the UniProtKB/Swiss-Prot database with respect to AMPlify scores and associated structural similarities to known chicken AMPs .......................................................................................... 148 Figure C.1 Sequence similarity distribution of the de novo generated sequences to known AMPs..................................................................................................................................................... 156 Figure C.2 Distributions of pairwise sequence similarities between different sequence sets in the de novo AMP design study ......................................................................................................... 157 xviii List of Abbreviations aa amino acid ABP antibacterial peptide acc accuracy AFP antifungal peptide AMP antimicrobial peptide ANN artificial neural network APD Antimicrobial Peptide Database AST antimicrobial susceptibility testing ATCC American Type Culture Collection AUROC area under the receiver operating characteristic curve AVP antiviral peptide BART Bullfrog Annotation Reference for the Transcriptome BiCGAN bidirectional conditional generative adversarial network Bi-LSTM bidirectional long short-term memory CA context attention CAMP Collection of Antimicrobial Peptides cDNA complementary deoxyribonucleic acid CFU colony forming unit CLSI Clinical and Laboratory Standards Institute CNN convolutional neural network CPO carbapenemase-producing organism xix cVAE conditional variational autoencoder DADP Database of Anuran Defense Peptides DNA deoxyribonucleic acid EOS end-of-sequence F1 F1 score GAN generative adversarial network GRU gated recurrent unit LSTM long short-term memory MBC minimum bactericidal concentration MDR multidrug-resistant MHB Mueller-Hinton Broth MHSDPA multi-head scaled dot-product attention MIC minimum inhibitory concentration MITRAC mitochondrial translation regulation assembly intermediate of cytochrome c oxidase complex MLP multilayer perceptron MRSA methicillin-resistant Staphylococcus aureus NCBI National Center for Biotechnology Information NDM New-Delhi metallo-beta-lactamase RBC red blood cell ReLU rectified linear unit RNA ribonucleic acid RNN recurrent neural network xx ROC receiver operating characteristic RPMI Roswell Park Memorial Institute sens sensitivity spec specificity VAE variational autoencoder VRSA vancomycin-resistant Staphylococcus aureus WHO World Health Organization 3D three-dimensional xxi Acknowledgements I would like to express my sincerest gratitude to my supervisor, Dr. Inanç Birol, for his patient and selfless guidance throughout my Ph.D. study. I wish to convey my appreciation to my thesis committee members, Dr. Raymond Ng, Dr. Linda M.N. Hoang, and Dr. Sara Mostafavi for their kind support and valuable feedback on my work. I thank Dr. Caren C. Helbing, Dr. Fraser Hof, and Dr. Caroline E. Cameron from the University of Victoria, for their helpful discussions. I thank all my collaborators for their contributions, especially Darcy Sutherland and Anat Yanai for their continuous wet lab support to my work. I thank all my lab mates from the Birol Lab for their generous help at work. I am also grateful for all my friends for their moral support. Special thanks are owed to my parents for their unconditional love and unwavering support throughout my life. Finally, I would like to thank the University of British Columbia for the financial support (the Four Year Doctoral Fellowship), and the following funding agencies that funded each of my research projects: Chapter 2: This work was supported by Genome BC and Genome Canada [281ANV; 291PEP], and the National Institutes of Health [2R01HG007182-04A1]. Additional support was provided by the Investment Agriculture Foundation of BC. Chapter 3: The model development part of this work was supported by Genome BC and Genome Canada [281ANV; 291PEP], and the National Institutes of Health [2R01HG007182- xxii 04A1]. The antimicrobial peptide discovery and validation part of this work was supported by Genome BC and Genome Canada [291PEP]. Additional support was provided by the Investment Agriculture Foundation of BC. Chapter 4: This work was supported by Genome BC and Genome Canada [291PEP]. Additional support was provided by the Investment Agriculture Foundation of BC. 1 Chapter 1: Introduction In this chapter, I first discuss about the background of the research. This includes an overview of antibiotic resistance, an introduction to antimicrobial peptides (AMPs), a summary of machine learning methods for sequence classification and generation, as well as a review of existing methods in AMP discovery and design. Then I talk about the motivation of this work. Lastly, I present the research objectives. 1.1 Background 1.1.1 Antibiotic resistance Antibiotics are medicines that have long been used to prevent and treat bacterial infections [1]. The worldwide overuse of antibiotics has led to an alarming increase in the number of bacteria reported to possess antibiotic resistance, resulting in conventional antibiotics to be less effective [2]. Despite the extensive use of antibiotics in clinical settings, antibiotics are also widely used in the agriculture industry [3]. It has been reported that certain drug-resistant bacteria can be transmitted between humans and other animals, which further exacerbates the situation [3]. It is estimated that 1.27 million people died due to antibiotic resistance in 2019 [4], and the fast speed of the bacterial evolution of antibiotic resistance is expected to greatly increase this death toll in the next few decades [3,5]. The foundation of antibiotic resistance lies in genetic mutations within the bacterial genome, as well as the horizontal transfer of mobile genetic elements such as plasmids [6]. Antibiotic resistance arises as a consequence of the selective pressure exerted by antibiotic usage, favoring 2 the survival and growth of bacterial strains that have undergone genetic mutations enabling them to withstand the effects of antibiotics and/or have acquired resistance from others [3] while inhibiting the growth of the susceptible bacterial strains [7]. Multidrug-resistant (MDR) bacteria are bacteria that are resistant to at least one antimicrobial agent in three or more antimicrobial categories [8]. The increasing threat of MDR bacterial strains has prompted the World Health Organization (WHO) to release a list of priority pathogens for which new antibiotics are urgently needed [9]. This list includes bacterial species that are increasingly resistant to multiple antibiotics, with three bacterial strains with critical priority, six with high priority, and three with median priority [9]. Moreover, the sluggish pace of discovery and development of new antibiotics is exacerbating this public health crisis [10]. Starting from the 1990s, there has been a significant decline in the discovery of new antimicrobial agents, while the occurrence of antibiotic resistance is increasing [11]. This further suggests a pressing demand for novel therapeutics as substitutes for conventional antibiotics. 1.1.2 Antimicrobial peptides (AMPs) 1.1.2.1 Basic facts of AMPs Antimicrobial peptides (AMPs) are a diverse class of short (typically 10 to 50 amino acid residues in length) and often cationic peptides, found in all forms of life as part of the innate immunity [12]. Many of these AMPs are also amphipathic, with hydrophilic and hydrophobic residues distributed on opposing faces of the peptide structure [12,13]. Naturally-occurring AMPs are considered to be evolutionarily conserved molecules [12]. Within their respective hosts, eukaryotic AMPs have co-evolved with microorganisms to serve as a defense against 3 bacterial [14], fungal [15] and even viral infections [16]. The majority of currently known AMPs reported in public databases are antibacterial, while some AMPs are active or additionally active against other microbes (e.g., fungi, viruses) [17]. 1.1.2.2 AMP structures Based on the three-dimensional (3D) structures, AMPs can be classified into four major groups, as described below [18]. 1) α-helical AMPs. Examples of α-helical AMPs include the human cathelicidin LL-37 [19] and frog magainins [20]. Some of the α-helical peptides have limited stable formation of secondary structures in aqueous solution but exhibit α-helical structures when interacting with bacterial membranes [12] (e.g., frog magainins [20]). 2) β-sheet-containing AMPs. The β-sheet-containing AMPs typically have β-sheet structures stabilized by disulfide bonds [12], such as the rabbit defensin RK-1 [21]. 3) AMPs containing both α-helices and β-sheets, such as heliomicin [22]. 4) AMPs with linear extended structures. These types of AMPs do not exhibit any particular secondary structures in either aqueous solution or non-polar environment (e.g., bacterial membranes). However, these AMPs are typically rich in some specific types of amino acids, such as arginine, histidine, glycine, or tryptophan [12]. A good example of AMP in this family is indolicidin [23]. Other than these four major groups, a few AMPs with other more complex topologies have also been reported [18], such as the lasso peptide Microcin J25 [24]. 4 1.1.2.3 Sources of naturally-occurring AMPs In nature, AMPs can be produced by all forms of life, not limited to higher eukaryotic organisms [12]. Microbes can also produce AMPs for competitive purposes, modulating the growth of other microbes [12]. According to the statistics made by the Antimicrobial Peptide Database (APD3, https://aps.unmc.edu/AP) [17] web server on January 2023, out of all 3,569 sequences in the database, amphibians are the largest organism source of naturally-occurring AMPs (1,196 sequences), followed by bacteria (380 sequences), plants (371 sequences), insects (367 sequences), and mammals (363 sequences), making them the five major source organism groups of currently known AMPs. Amphibian AMPs, the largest source of currently known AMPs, account for 33.51% (1,196/3,569) of all records in APD3 [17], with 93.39% (1,117/1,196) of them from frogs. The skin of adult frogs, their first barrier of defense to fight against the environmental pathogens [25], is considered as a rich source of AMPs [26]. These AMPs are synthesized and stored in granular glands of the dermis, and are secreted onto the skin surface when experiencing injury or facing microbial challenge [27]. The frog skin has evolved to be thin and permeable, allowing for multiple physiological functions such as respiration and heat transfer [25]. However, these characteristics of the frog skin are making it relatively vulnerable to injuries [25]. On the other hand, the living environments of frogs are typically wet and muddy, abundant with pathogens [25,26]. As a result, a rich repertoire of AMPs in frog skin is a product of evolution to help them survive in such living conditions [26]. 5 Plants are also a rich source of AMPs, constituting 10.40% (371/3,569) of APD3 [17]. The evolution of a diversity of AMPs in plants is a beneficial adaptation to their microbe- and pest-rich living environments [28]. These AMPs can be found in all parts of the plants to serve as a defense against a variety of microbes such as bacteria and fungi, as well as to protect themselves from damage from insects [28,29]. Insects are another promising source of AMPs, making up 10.28% (367/3,569) of APD3 [17]. While insects do not have antibody-mediated adaptive immune response as seen in vertebrates [30], they demonstrate strong ability in defense against bacterial infections [31]. One possible explanation for the paradox is that insects can produce of a wide range of AMPs, typically in venom or salivary glands, as their first line of defense against infections from various pathogens [31,32]. Mammalian AMPs, on the other hand, represent a proportion of 10.17% (363/3,569) in APD3 [17]. Animals in the class Mammalia are evolutionarily closer to humans, hence the innate immune system of mammals have been widely investigated compared with other vertebrates in support of clinical research [33]. As a result, mammalian AMPs, an important part of their innate immunity, have also been extensively studied. Mammalian AMPs can be found in many parts of the body that are exposed to various pathogens, such as skin and lung. I also note that 42.15% (153/363) of the mammalian AMP records in APD3 are from humans. Bacteria also produce a large number of AMPs, with a total of 10.65% (380/3,569) in APD3. Microbes, like bacteria, produce AMPs to gain an advantage in survival in their respective 6 environments by inhibiting the growth of other microbes [12]. Unlike vertebrate AMPs, which are mostly ribosomally synthesized, many of the microbe-derived AMPs can be synthesized by non-ribosomal peptide synthetases, independent of messenger RNAs [12,34]. These non-ribosomal AMPs are often with cyclic and/or branched structures [12]. 1.1.2.4 AMP precursors Many ribosomally synthesized AMPs are produced in an inactive precursor form with a signal peptide, an acidic pro-sequence, and the bioactive mature peptide [34,35]. The signal peptide, usually presenting at the N-terminus of the precursor sequence, is responsible for directing the newly synthesized peptide to the secretory pathway [36]. On the other hand, the acidic pro-sequence neutralizes the activity of the mature peptide before it’s ready to be released and activated when needed [12], and it may also help with the folding of the mature AMP [37,38]. The bioactive mature AMPs are released by proteolytic cleavage of their precursors [12]. Within the same AMP family, the signal peptide sequences are typically conserved, while the bioactive sequences are quite variable between different AMPs [26,32]. In addition to the classical AMP precursor sequence structure described above, there are AMP precursors with other sequence structures (e.g., some AMPs do not have acidic pro-sequences presented in their precursor sequences) [35]. 1.1.2.5 AMP modes of action One common mode of action of many AMPs is through direct interaction with the bacterial membranes [12]. Such mode of action typically relies on the net positive charge and the amphipathic properties of AMPs [39]. The net positive charge allows AMPs to be attracted and 7 attached to the negatively charged surfaces of bacteria [39,40]. Once AMPs reach a certain concentration on the bacterial surfaces, they start to penetrate the membranes, forming transmembrane pores and leading to non-enzymatic disruption [12,40]. For many AMPs, especially those with α-helical structures, their amphipathicity plays an important role in the membrane permeation process [41]. Theoretically, AMPs tend to target bacteria rather than normal mammalian cells, as the surfaces of the former are more negatively charged than the latter [42]. However, the membrane permeation ability of AMPs indicate that they may be toxic to mammalian cells, especially when bacteria are not presented [42]. As a result, toxicity to host cells is an important factor to consider when validating the effectiveness of an AMP. Some eukaryotic AMPs perform additional modulation of the host immune responses, such as attracting leukocytes and controlling the production of pro-inflammatory cytokines [12,39]. In comparison with most conventional small-molecule antibiotics, which have specific functional or structural targets, the distinct modes of action of AMPs may hold an advantage in being able to better overcome bacterial resistance [43]. Nevertheless, if bacteria are exposed to AMPs for extended periods of time, they can and do develop resistance, even to peptide-based drugs like the last resort and life-saving drug, colistin [43,44]. Besides the major modes of action described previously, those of many AMPs are still not well-understood [41], and they may vary based on their physicochemical properties and their target microbes. Moreover, I note that there do exist a few AMPs having more specific targets by entering the cell and inhibiting intracellular functions (e.g., inhibiting DNA, RNA, or protein synthesis). It has also been observed that some AMPs utilize multiple modes of action at the same time [39], potentially enhancing their bioactivity. 8 1.1.3 Machine learning for sequence classification and generation The term “machine learning” refers to programming computers to simulate the learning activities of human beings and learn from example data or past experience (i.e., the training data) to solve a given problem [45–48]. Machine learning enables computers to recognize patterns from the training data by themselves (typically through mathematical optimizations), and then to perform tasks without explicit human instructions based on the knowledge learned during the training process (i.e., the trained models) [45,47]. This characteristic of machine learning enables efficient in silico approaches to many complex tasks such as large-scale language translation and image recognition [49,50], which previously relied heavily on human knowledge and required substantial manual labor before the breakthrough in modern machine learning techniques. While machine learning brings convenience to many aspects of our daily lives, it also makes significant contributions to scientific research [48]. In biological sequence analysis, machine learning is successfully applied to mine useful information from biological data, such as nucleotide sequences and protein sequences [47]. Popular domains in biological sequence analysis include biological sequence classification [47] and generation [51]. The former covers tasks such as gene prediction and protein function prediction [47], while the latter typically refers to biological sequence design (e.g., de novo protein design) [51]. For sequence classification tasks, popular traditional machine learning methods include, but are not limited to, logistic regression [52], support vector machines [53], k-nearest neighbors [54,55], and random forests [56]. While most of the traditional machine learning methods require huge amounts of efforts in feature engineering to ensure considerable performance, deep learning 9 methods can automatically learn high-level features and usually outperform traditional methods in various fields, including bioinformatics [57]. Deep learning, a broad class of machine learning methods, extracts different levels of feature representations using multiple layers or stages [58]. In deep learning, artificial neural networks (ANNs) are one of the most commonly applied techniques [58], which simulate the biological neural systems by layers of artificial neurons that mathematically process and convey the information within the networks [59–61]. In most of the standard classification tasks, the architecture of an ANN typically include one input layer that takes the input features, one or more hidden layers encoding the representations of the inputs, and one output layer that predicts the classification labels for the input data [62]. One of the earliest and simplest architectures of ANN is the multilayer perceptron (MLP) model, with all neurons in each layer fully connected to the neurons in the subsequent layer [62]. Later, recurrent neural networks (RNNs) were designed specifically for sequential data, which encode the elements of a sequence in a recurrent manner [63]. Variants of RNNs with broad applications include long short-term memory (LSTM) [64] and gated recurrent unit (GRU) [65] based networks that improve upon classical RNNs for encoding longer sequences by utilizing gating mechanisms that partially solve the gradient vanishing problem. Convolutional neural networks (CNNs) are another class of ANN architectures that apply a set of convolution kernels sliding over the input data to extract different underlying features [66]. Although CNNs were originally designed for image data [66], they show great advantages in dealing with sequential data as well [67]. However, RNNs and CNNs have their own limitations: 1) RNNs can learn remote dependencies inside a sequence, but suffer from vanishing gradients [68]; 2) while CNNs can extract local information well, they ignore long-term dependencies [69]. Recently, deep neural 10 networks with attention mechanisms have gained interest, notably for natural language processing [49,70,71] and computer vision [72] applications. Attention mechanisms, as the name suggests, are inspired by our brains’ ability to prioritize segments of information when processing textual or visual input. In sequence analysis, attention mechanisms are modeled by weights assigned to different positions in a sequence. These weights amplify or attenuate information from a given position to help encode the global information of the sequence. In practice, different types of architectures can be merged into one single ANN, serving as multiple hidden layers, to improve the performance [67]. In sequence generation, the most commonly applied traditional machine learning approach is probabilistic n-gram language models, typically based on Markov models [73,74]. While the traditional n-gram language models make Markov assumption that the probability of the n-th element is only dependent on the previous n-1 elements of a sequence [74], RNN language models also generate a sequence element by element but consider long-term dependencies by encoding the entire history (i.e., all previously generated elements) [75], which allows RNN language models to outperform the traditional models in many sequence generation tasks. Besides the RNN language models, other popular neural network based sequence generation techniques include variational autoencoders (VAEs) [76] and generative adversarial networks (GANs) [77]. In a VAE framework, the encoder maps the input data to a probability distribution in a latent space, while the decoder maps the latent space back to the input space [76]. Once a VAE is trained, new data can be generated utilizing the decoder by sampling latent vectors from the latent space distribution [76]. On the other hand, a standard GAN framework includes two models being trained interactively: a generator model, and a discriminator model [77]. The 11 generator learns the underlying data distribution, while the discriminator distinguishes between the generated and real samples [77]. The training process of the framework corresponds a two-player game between the generator and the discriminator: the goal of the generator is to generate samples that are able to fool the discriminator into identifying them as real, while the goal of the discriminator is to distinguish between the generated and real samples accurately [77]. When the training is converged at the balance point, the generator is able to generate samples very close to the real ones, and the discriminator cannot distinguish between the real and generated samples anymore [77]. In practice, the major components of VAEs (i.e., encoders and decoders) and GANs (i.e., generators and discriminators) are usually neural network based models, the architectures of which are highly variable depending on the specific tasks. 1.1.4 AMP discovery and design AMPs are not only derived from living creatures, but they can also be synthetic peptides that do not exist in nature [78]. In this thesis, I refer to AMP discovery as the process of unearthing naturally-occurring AMPs from source organisms, and AMP design as the procedure of constructing novel peptides that exhibit antimicrobial activities but may not exist in nature. Recently, machine learning has been attracting increasing attention and is playing a vital role in the high-throughput discovery and design of novel AMPs. Here, I review some existing AMP discovery and design methods, covering both traditional strategies and high-throughput machine learning based approaches. 12 1.1.4.1 AMP discovery The most conventional approach for discovery of naturally-occurring AMPs is utilizing direct wet lab screening protocols, with AMPs extracted, isolated, and purified from tissue samples [32]. These methods are time-consuming, labor-intensive and costly [79], especially when the screening is large-scale. To tackle this problem, in silico homology-based methods for AMP discovery have been developed to mine for novel AMPs from genomic or transcriptomic resources [26,32,80]. Such methods aim at looking for coding regions of potential AMP precursor sequences from the source genome or transcriptome, based on the fact that the N-terminal sequences of AMP precursors are usually conserved within the same family, and they have successfully revealed a considerable number of novel AMP sequences from multiple source organisms [26,32]. While homology-based methods provide a high-throughput solution to the problem, they have their own limitations: 1) high similarity between an identified sequence and known AMP precursor sequences cannot guarantee that the identified sequence is a real AMP precursor; 2) novel AMPs with relatively distinct precursor sequences from the known ones may go unidentified. Alternatively, machine learning enables fast and accurate identification of AMPs from their sequences, and various machine learning based tools have been developed in the past few years to streamline in silico AMP prediction [79]. Such methods can sift through large volumes of candidate sequences and reduce lab screening costs. The AMP prediction module in the Collection of Antimicrobial Peptides (CAMP) database [81] includes four different models: random forest, support vector machine, discriminant analysis, and a single-hidden-layer MLP with 64 designed features [82]. The iAMP-2L online server adopts fuzzy k-nearest neighbor 13 algorithm, taking pseudo amino acid compositions with five physicochemical properties as input features to predict AMPs as well as their potential microorganism targets [83]. The iAMPpred online server for AMP prediction and classification is based on support vector machine and uses pseudo amino acid compositions with compositional, physicochemical, and structural features [84]. Youmans and co-workers demonstrated the feasibility of using a bidirectional LSTM (Bi-LSTM) [64,85,86] for AMP prediction [87], but the authors did not offer any public code or tool that implements their model. The Deep-AmPEP30 online server applies a CNN for AMP prediction [88], though the tool is restricted to working with short peptides up to 30 amino acids (aa) in length. The Deep-ABPpred online server adopts Bi-LSTM with word2vec [89], also for short (£ 30 aa) peptides [90]. The Bi-LSTM model from Wang and co-workers is designed for even shorter peptides (£ 20 aa) and specializes to predicting AMPs against Escherichia coli [91]. They also provided a workflow for designing novel AMPs. Veltri and co-workers introduced a deep neural network classifier with embedding, convolutional, max pooling, and LSTM recurrent layers which is available as an online server, AMP Scanner Vr.2, as its user interface [92]. While machine learning based AMP prediction tools allow for efficient AMP identification purely based on the peptide sequences, they are usually embedded into bioinformatics pipelines for large-scale AMP mining, obtaining a highly curated set of candidate AMPs for lab validation [32,93]. 1.1.4.2 AMP design Conventional approaches for AMP design include 1) modifying known AMP sequences to generate their congeners, fragments, or hybrids; 2) minimalist approaches where AMPs are designed de novo purely based on structural requirements (e.g., amphipathic α-helical structures) 14 but with limited types (e.g., physicochemical properties) of residues used; 3) sequence-template-guided approaches that create sequence templates by comparing structurally homologous fragments from known AMPs for conserved patterns in terms of residue types; and 4) utilizing combinatorial peptide libraries for AMP variants [94,95]. However, these methods require prior expertise in AMPs for more accurate designs, which restricts the throughput. Recently, a series of machine learning models based on neural networks have been proposed for the automatic de novo design of AMP sequences [96–102]. They make it possible for users to sample novel AMP sequences directly from the models without any artificial design. Nagarajan et al. developed an LSTM language model, and embedded it into a framework with multiple filtering steps for the generation of novel AMPs with strong antibacterial activity [96]. Dean et al. proposed a VAE based AMP sequence generation framework, named PepVAE, for generation of highly active AMPs [97]. Das et al. further adapted VAE and introduced CLaSS for controlled AMP sequence generation with attributes of interest [98]. HydrAMP, another VAE based model [99], incorporates two pre-trained classifiers monitoring the quality of the generated peptides during training, improving upon a conditional VAE (cVAE) [103]. Gupta et al. proposed Feedback GAN for generating DNA sequences encoding proteins with optimized properties, and applied it to AMP sequence generation as an example [100]. Tucs et al. adapted an activity-aware LeakGAN [104] to generate highly active AMPs [101], while Van Oort et al. introduced AMPGAN v.2 based on a bidirectional conditional GAN (BiCGAN) [105,106] to generate AMP sequences of different types and properties [102]. The flurry of activities represented by these methods illustrate a strong interest in the field for de novo AMP design and explore expertise-free approaches. 15 1.2 Motivation The growing global health concern of antibiotic resistance is prompting researchers to seek alternatives to conventional antibiotics, and AMPs are once again attracting attention as therapeutic agents with promising utility in this domain regarding their distinct modes of action as described in Chapter 1, Section 1.1.2.5. However, resistance to AMPs can still be observed if bacteria are exposed to AMPs for extended periods of time [43,44], highlighting that the problem of antibiotic resistance persists. Thus, high-throughput methods for the rapid discovery and design of novel AMPs would be instrumental in our fight against MDR bacteria [32]. The potential sequence space of amino acids is combinatorially large, making it possible to extend the current arsenal of antimicrobial agents with a huge number of new peptide-based candidates. While there have been considerable amounts of success in the discovery of naturally-occurring AMPs, it can be limited by availability of organism sources, such as tissue samples for direct wet lab screening or sequencing data for in silico mining. Therefore, design of synthetic peptides that may not exist in nature but still possess desirable antimicrobial properties provides alternative opportunities to further expand the pool of potential AMP candidates. From the perspective of drug discovery, looking into both naturally-occurring and synthetic AMPs is a two-pronged approach to maximize the number of candidates for strict clinical selections, increasing the chances of getting more safe and effective therapeutics for clinical use. Though machine learning accelerates the discovery of naturally-occurring AMPs as well as the design of synthetic AMPs (see Chapter 1, Section 1.1.4), currently available models still have substantial room for improvement. 16 1.3 Research objectives The overall research goal of this thesis is to develop state-of-the-art machine learning based tools for the discovery of novel naturally-occurring AMPs from genomic resources and protein sequence databases, as well as the design of novel synthetic AMPs that may not exist in nature. There are two main research objectives in this thesis: 1) Explore machine learning based sequence classification methods for discovery of naturally-occurring AMPs from genomic resources and protein sequence databases. 2) Explore machine learning based sequence generation methods for de novo AMP design. 17 Chapter 2: AMP discovery from genomic resources 2.1 Publication note A version of this chapter was previously published in BMC Genomics in 2022 [107]. This publication describes the AMP prediction tool AMPlify of version 1.0 and shows its application in genome mining. AMPlify v1.0 adopts an attention mechanism based deep learning model trained on a balanced training set with the same number of AMP and non-AMP sequences (i.e., the balanced model). I am the lead author of this work, under the supervision of Dr. Inanç Birol. I designed and implemented the model, helped design the AMP discovery pipeline, and drafted the manuscript, in collaboration with my colleagues. All in vitro validations were conducted by my wet lab collaborators. 2.2 Author summary We introduce AMPlify, a deep learning based tool for AMP prediction utilizing attention mechanisms, and demonstrate the utility of its balanced model in prioritizing peptide sequences derived from the Rana [Lithobates] catesbeiana (bullfrog) genome [108]. We tested the bioactivity of our predicted peptides against a panel of bacterial species. Four of our novel AMPs were active against multiple species of bacteria, including an MDR isolate of carbapenemase-producing (CPO) Escherichia coli. We demonstrate the utility of deep learning based tools like AMPlify in our fight against antibiotic resistance. We expect such tools to play a significant role in discovering novel candidates of peptide-based alternatives to classical antibiotics. 18 2.3 Introduction To improve in silico AMP prediction, we developed AMPlify, a deep learning based tool that applies two types of attention mechanisms layered on a Bi-LSTM [64,85,86] layer (Figure 2.1). The Bi-LSTM layer in the model architecture, as a variant of RNN, encodes positional information from the input sequence in a recurrent manner. Subsequently, the multi-head scaled dot-product attention (MHSDPA) [70] layer computes a refined representation of the sequence using multiple weight vectors. The last hidden layer of context attention (CA) [71] generates a single summary vector using weighted average, learning contextual information gained from the previous layer. Introduced in version 1.0, the balanced model of AMPlify was trained on a balanced training set containing the same number of AMP and non-AMP sequences, and adopts ensemble learning to further improve its performance. To the best of our knowledge, AMPlify is the first machine learning application that applies attention mechanisms for in silico AMP prediction. We note that non-standard amino acids are not taken into consideration in this study. To illustrate the utility of our model, a discovery pipeline based on AMPlify v1.0 was used to mine the AMP-rich North American bullfrog (Rana [Lithobates] catesbeiana) genome [108] for novel natural AMPs. Previously, the North American bullfrog has been described as a rich source for natural AMPs, yielding potent classes of bioactive molecules such as ranateurins, ranacyclins, temporins, and palustrins [26,108]. In our tests, AMPlify successfully identified these previously reported AMPs, along with four novel AMPs with biological activity in vitro. We tested the efficacy of our discovered, putative AMPs against selected priority pathogens listed by WHO [9], including: 1) Pseudomonas aeruginosa and Escherichia coli strains, 19 including an MDR CPO strain of E. coli reflective of WHO’s “Priority 1” pathogens; and 2) a Staphylococcus aureus strain reflective of WHO’s “Priority 2” methicillin-resistant (MRSA) and vancomycin-resistant (VRSA) strains. A Streptococcus pyogenes strain was included as an additional Gram-positive bacterial species that causes human disease, while this bacterial species has demonstrated antibiotic resistance in some earlier works [109]. In our tests, four of the 16 novel AMPs discovered showed considerable antimicrobial potency against one or more of the organisms examined, including the clinical MDR isolate of CPO E. coli. These results highlight the potential of AMPlify to accelerate AMP discovery, the first step towards facilitating peptide-based therapeutics. Figure 2.1 Model architecture of AMPlify. Residues of a peptide sequence are one-hot encoded and passed to three hidden layers in order: the bidirectional long short-term memory (Bi-LSTM) layer, the multi-head scaled dot-product attention (MHSDPA) layer and the context attention (CA) layer. The output layer generates the probability that the input sequence is an AMP. zi1zi1h1 h2 hL…...Bi-LSTM Layer…...Input LayerAttention (CA)zi1 …...Attention (MHSDPA)Peptide sequencexLx2x1zi1zi1zi2 zi1zi1ziLm1 m2 mLsOutput probability…...Output Layer !" 20 2.4 Methods 2.4.1 Generation of the datasets We used publicly available AMP sequences to train and test AMP predictors. In order to build a non-redundant AMP dataset, we first downloaded all available sequences from two manually curated databases: Antimicrobial Peptide Database (APD3, https://aps.unmc.edu/AP) [17] and Database of Anuran Defense Peptides (DADP, http://split4.pmfst.hr/dadp) [110]. Since APD3 is being frequently updated, we used a static version that was scraped from the website on March 20, 2019 comprising 3,061 sequences. Version 1.6 of DADP contains 1,923 distinct mature AMPs. We concatenated these two sets and removed duplicate sequences, producing a non-redundant (positive) set of 4,173 distinct, mature AMP sequences, all 200 aa in length or shorter. AMPs that are highly similar to each other at the sequence level were kept as separate entries, since small changes in amino acid compositions may lead to large changes in AMP activity [111]. Also, it is important to maintain as big a dataset as possible for better training of a deep learning model [57]. Training and testing binary classification models require a negative set, a collection of peptides known not to have any antimicrobial activity. Since there are no sequence catalogs for peptides devoid of antimicrobial activity, studies in the field typically select their non-AMP sequences from UniProt (https://www.uniprot.org) [112]. This may involve excluding several simple keywords (e.g., antimicrobial, antibiotic) to filter out potential AMPs [82,83], or additionally removing all secretory proteins [92] as AMPs are characteristically secreted peptides [113]. The former proposition is not sufficiently rigorous, because AMP annotation is not consistent and varies between sources. While keyword filtering may leave in the set some differently annotated 21 AMPs, filtering of secretory proteins creates a learning gap for the model regarding such proteins without antimicrobial activities. Thus, it is important to balance these two strategies when selecting non-AMP sequences. We designed a rigorous selection strategy for our non-AMP sequences (Figure A.1), using sequences from the UniProtKB/Swiss-Prot database (2019_02 release) [112], which exclusively comprises manually annotated and reviewed records from the UniProt database. First, we downloaded sequences that are 200 aa or shorter in length (matching the maximum peptide length in the AMP set), excluding those with annotations containing any of the 16 following keywords related to antimicrobial activities: {antimicrobial, antibiotic, antibacterial, antiviral, antifungal, antimalarial, antiparasitic, anti-protist, anticancer, defense, defensin, cathelicidin, histatin, bacteriocin, microbicidal, fungicide}. Second, duplicates and sequences with residues other than the 20 standard amino acids were removed. Third, a set of potential AMP sequences annotated with any of the 16 selected keywords were downloaded and compared with our candidate negative set. We noted instances where a sequence with multiple functions was annotated separately in multiple records within the database, and removed sequences in common between candidate non-AMPs and potential AMPs. The candidate non-AMP sequences were also checked against the positive set to remove AMP sequences that lack the annotation in UniProtKB/Swiss-Prot. Finally, 4,173 sequences were sampled from the remaining set of 128,445 non-AMPs, matching the number and length distribution of sequences in the positive set. An exception to the length distribution matching occurred when the length of a particular AMP sequence did not have a perfect match in the set of non-AMP sequences. In these instances, 22 we chose the non-AMP sequence with the closest length. The matched length distributions were selected so that the model did not learn to distinguish classes based on sequence lengths. The positive and negative sets were both split 80%/20% to build the final training and test sets, respectively. In details, the training set contains 3,338 AMP sequences and 3,338 non-AMP sequences, and the test set contains 835 AMP sequences and 835 non-AMP sequences. We refer to the training and test sets built above as balanced training and test sets, as the numbers of AMP and non-AMP sequences were kept the same in each set. 2.4.2 Model implementation AMPlify was implemented in Python 3.6.7, using Keras library 2.2.4 [114] with Tensorflow 1.12.0 [115] as the backend. The raw output of the model can be interpreted as a probability score !!"#$%&'. For a better visualization of the predictions, we introduce “AMPlify score”, which is the log transformation of the AMPlify probability score. This transformation is calculated as: −10 log()(1 − !!"#$%&'), which we practically cap at 80. Sequences with AMPlify scores > 3.01 (AMPlify probability scores > 0.5) are considered as AMPs, and those with AMPlify scores ≤ 3.01 (AMPlify probability scores ≤ 0.5) as non-AMPs. We used binary cross-entropy as the loss function, and the Adam algorithm [116] for optimizing weights. Dropout technique [117] was applied during training to prevent the model from over-fitting. The original balanced training set was split evenly into five subsets of sequences, keeping the ratio of AMP and non-AMP sequence numbers the same as the original set. Then five training and validation set pairs were constructed by leaving one set out for validation to build 23 five single sub-models. To optimize computational time and avoid overfitting, we applied early stopping during the training of each single sub-model. The validation accuracy was monitored at each training epoch, and the training process was stopped if there appeared to be no improvement for the next 50 epochs. The model weights from the epoch with the best validation accuracy were selected as the optimal weights. The output probabilities from the five single sub-models were averaged to create an ensemble model. Reflecting the composition of the sequences in the training and test sets, AMPlify only considers sequence lengths of 200 aa or shorter containing the 20 standard amino acid residues. 2.4.3 Hyperparameter tuning and model architecture In neural networks, the optimal hyperparameters, unlike model weights, cannot be learned directly from the training process, but they do play an important role in model performance. Thus, various combinations of hyperparameters must be compared in order to select the best set. Here we applied stratified 5-fold cross-validation on the entire training set to tune the model and find the best set of hyperparameters for the model architecture, as well as for training settings, including dropout rates and optimizer settings. For a fair comparison, we kept the same splits of sequences within cross-validation across all hyperparameter combinations. During hyperparameter tuning, we monitored the average performance on the validation partitions of cross-validation. Note that these validation partitions within cross-validation are different from the validation sets for early stopping, while the latter being additionally derived from the training partitions during the cross-validation process. The set of hyperparameters with the highest average cross-validation accuracy was chosen to train the final prediction model. 24 The AMPlify architecture includes three main components: 1) a Bi-LSTM layer, 2) a MHSDPA layer, and 3) a CA layer (Figure 2.1). To convert the original peptides into a mathematically processable format, each sequence is represented by a series of one-hot encoded vectors over an alphabet of 20 amino acids, yielding **, *+, … , *,, where - is the length of the sequence and each *- is a 20-dimensional vector of zeros and ones with ||*-||* = 1 (0 = 1,2, … , -). The Bi-LSTM layer takes those one-hot encoded vectors as input and encodes positional information for each residue from both forward and backward directions, and the output vector for each residue is represented as a concatenation of the vectors from both directions. The best tuned dimensionality for each direction of Bi-LSTM layer was 512, resulting in the entire Bi-LSTM layer to be 2. = 512 × 2 = 1024 dimensional. Outputs from all residue positions of the Bi-LSTM layer are returned as the input for the next layer. The best tuned dropout rate of 0.5 was applied to the input of the Bi-LSTM layer. Encoding from the Bi-LSTM layer for residues within a given sequence can be represented as a series of vectors 6- ∈ ℝ/! (0 = 1,2, … , -), and the entire sequence can be represented as a matrix with all 6-s packed as 9 = (6*, 6+, … , 6,)0 ∈ ℝ1×/!. Next, the MHSDPA layer searches for relations between different residues in : different representation subspaces [70] (i.e., different attention heads) to further encode the sequence, where : is a hyperparameter to be tuned. Each residue first gets an intermediate representation within each head by calculating a weighted average over transformed vectors of all residues derived from their Bi-LSTM representations. The results from each head are then concatenated and mapped back to the original dimensionality. We adapted Vaswani and co-workers’ approach 25 [70] to calculate the attention weights and outputs for the MHSDPA layer. The implementation was adapted from the GitHub repository at https://github.com/CyberZHG/keras-multi-head [118], where rectified linear unit (ReLU) activation functions and bias terms were added to all linear transformations. In further detail, to obtain attention weights for different residues of a sequence within a head ;, we calculate a set of queries <% ∈ ℝ1×/", keys =% ∈ ℝ1×/" , and values ?% ∈ ℝ1×/# by transforming 9 as follows: <% = ReLU(9D3$ + F3$) =% = ReLU(9D4$ + F4$) ?% = ReLU(9D5$ + F5$) where D3$ ,D4$ ∈ ℝ/!×/" and D5$ ∈ ℝ/!×/# are weight matrices, and F3$ =GH6% , H6% , … , H6%I0 ∈ ℝ1×/", F4$ = GH7% , H7% , … , H7%I0 ∈ ℝ1×/" and F5$ =GH8% , H8% , … , H8%I0 ∈ ℝ1×/# are bias matrices. We set transformation dimensions as :29 =:2: = 2. following what has been previously proposed [70]. A square matrix J% ∈ ℝ1×1, which contains weight vectors to calculate intermediate representations of all residues within head ;, is computed as: J% = softmaxrow(<%=%0Q29 ) 26 where dot-product of queries and keys are scaled by a factor (;/", and the softmax function is applied to each row of the matrix for normalization. The intermediate representation of the sequence within head ; is then computed by: R% = (S*< , S+< , … , S,< )0 = J%?% ∈ ℝ1×/# where each single vector S-< ∈ ℝ=# (0 = 1,2, … , -) denotes the intermediate representation of each residue of the sequence with dimensionality 2:. The concatenated matrix R =GR1×/#( , R1×/#> , … , R1×/#? I ∈ ℝ1×?/# is further transformed to get the final output of the current layer as follows: T = (U*, U+, … ,U,)0 = ReLU[RD@ + F@] ∈ ℝ1×/! where D@ ∈ ℝ?/#×/! is a weight matrix and F@ = (HA, HA, … , HA)0 ∈ ℝ1×/! is a bias matrix. Each vector U- ∈ ℝ=! (0 = 1,2, … , -) denotes the new representation of the corresponding residue of the sequence with dimensionality 2.. The best number of heads tuned for this layer was : = 32, with 29 = 2: = 32. Finally, the CA layer gathers information from the MHSDPA layer by reducing - encoded vectors into a single weighted average summary vector Y. We followed Yang and co-workers’ approach [71] to perform this operation, and adapted the implementation from the GitHub repository at https://github.com/lzfelix/keras_attention [119]. The weight vector Z ∈ ℝ1 is calculated using Z = softmaxG(tanh(TD + F))]I 27 where D ∈ ℝ/!×/! is a weight matrix, F = (H, H, … , H)0 ∈ ℝ1×/! is a bias matrix, ] ∈ ℝ/! is a context vector, and the softmax function provides weight normalization. The summary vector Y ∈ ℝ/! is then computed as: Y = T0Z =^_BU-1BC( where _B denotes each component in the weight vector. Vector Y summarizes information of the entire sequence into a single vector, and it is passed through the output layer of a single neuron with a sigmoid activation function for classification. The best tuned dropout rate of 0.2 was applied to the input of the CA layer during training. In addition to the hyperparameters of the model architecture, the hyperparameters of the optimizer were optimized through cross-validation. A batch size of 32 and a default learning rate of 0.001 were found to be the best for the AMP prediction task. 2.4.4 Model evaluation The performance of AMPlify was evaluated using five metrics: accuracy, sensitivity, specificity, F1 score and area under the receiver operating characteristic curve (AUROC). The architecture of AMPlify was compared with its simpler variations with fewer hidden layers using stratified 5-fold cross-validation on the balanced training set to measure the value added by each layer as the architecture grew more complex. The final version of the AMPlify balanced model, trained on the entire balanced training set, along with its five single sub-models, were compared with three other state-of-the-art tools: iAMP-2L [83], iAMPpred [84] and AMP 28 Scanner Vr.2 [92], on the balanced test set we built. All comparators were evaluated using their original models online. We note that AMP sequences in our balanced test set have no overlap with the training sets of iAMPpred and iAMP-2L, but they share 196 sequences with the training set of AMP Scanner Vr.2. In addition, as the only comparator with methods for re-training, AMP Scanner Vr.2 was cross-validated and re-trained on our balanced training set to ensure a fair comparison. We note that, since our dataset is slightly different from those used by other methods, the number of epochs required to get a deep learning model well trained on different datasets might differ. Keeping all other hyperparameters the same as the original model, we cross-validated and re-trained AMP Scanner Vr.2 with two different stopping settings: using the optimal fixed number of epochs as reported [92], and using early stopping. To assess how sequence similarities to the training peptides affect the model performance on the test sequences, subsets of our original testing AMP/non-AMP sequences were generated by choosing different sequence similarity thresholds to the corresponding training AMP/non-AMP sequences of the respective tools. The similarity between two peptide sequences was calculated as (1 − /$,'DEFG$$,$'I) × 100%, where 2%,J is the edit distance and a% , aJ are lengths of the peptide sequences regarding the numbers of amino acid residues. The similarity of a sequence to a set of sequences was defined as the maximum of all similarity values calculated between that sequence and the sequences in the target set for comparison (i.e., the similarity of that sequence to its most similar sequence in the target set). Note that different tools may have different test subsets under 29 the same sequence similarity threshold, since they were trained on different training sets. Each AMP/non-AMP test subset contained test sequences that shared sequence similarities less than or equal to a given threshold to the corresponding AMP/non-AMP training set. Combining these sets, we measured the F1 scores of the tools. 2.4.5 AMP discovery pipeline A primarily homology-based approach was used to feed novel candidate AMP sequences to AMPlify for further evaluation. The pipeline and its results are summarized in Figure A.2 and are detailed below. Sequences matching the search phrase “((antimicrobial) AND precursor) AND amphibian” were downloaded from the NCBI Nucleotide database on January 4th, 2016 and aligned to the draft bullfrog genome [108] (version 3) using GMAP [120] (version 20170424) with the following parameters: -A --max-intronlength-ends=200000 -O -n20 --nofails. To refine the putative AMP loci, the gene prediction pipeline MAKER2 [121] (version 2.31.8 running under PERL version 5.24.0 with augustus [122] version 3.2.1, exonerate [123] version 2.2.0, genemark [124] version 2.3c, and snap [125] version 2006-07-28) was applied to the 231 genomic scaffolds with alignment hits from GMAP using default settings. The MAKER2 pipeline can use orthogonal evidence from related protein or transcript sequences when available to generate a list of high confidence genes. Protein evidence consisted of three sets of sequences: sequences matching the search phrase “((antimicrobial) AND precursor) AND amphibian” from the NCBI protein database that were downloaded on December 31, 2015; experimentally 30 validated non-synthetic amphibian antibacterial peptide sequences downloaded from CAMP [81] on March 4th, 2016; and sequences from APD3 [17] downloaded on September 29, 2017. For transcript evidence, the set of cDNA sequences supplied to GMAP above was supplemented with selected bullfrog transcript sequences from the Bullfrog Annotation Reference for the Transcriptome (BART) [108]. Blastn [126] (version 2.31.1) was used to align the initial cDNA sequences from NCBI to BART, and BART sequences with an alignment of greater than 90% identity and 100% coverage were selected. A custom repeat element library was constructed from predicted repeats previously identified in the bullfrog genome [108] and supplied to MAKER2 for use by RepeatMasker [127]. The annotation pipeline was run with the snap hidden Markov model that produced the version 2 bullfrog gene predictions [108]. The MAKER2 gene predictions were filtered in two stages. First, sequences containing the highly conserved lysine-arginine enzymatic cleavage motif were selected, and the sequence of the putative mature peptide, produced via in silico cleavage at the C-terminal side of the cleavage motif, was extracted. Second, only putative mature sequences of 200 amino acid residues or less were included. Sequences with non-standard amino acid residues were excluded. The resulting peptide sequences from these filters were fed into the AMPlify balanced model for prediction. From the predicted putative AMPs, only short cationic sequences with lengths between 5 and 35 aa were chosen for synthesis and validation in vitro. We prioritized short cationic sequences as shorter peptides are more cost-effective to synthesize [32]. 31 2.4.6 Antimicrobial susceptibility testing From the novel candidate AMP sequences predicted by AMPlify, 11 were selected for validation in vitro. We determined the minimum inhibitory concentrations (MICs) and minimum bactericidal concentrations (MBCs) using the antimicrobial susceptibility testing (AST) procedures outlined by the Clinical and Laboratory Standards Institute (CLSI) [128], with some adaptations for testing cationic AMPs as described previously [129]. A known AMP, LL-37, was used as the positive control peptide [19,26]. We used Tp0751, a non-functional truncated section of a Treponema pallidum protein with similar characteristics to AMPs [130], as the negative control peptide. 2.4.6.1 Bacterial isolates A panel of two Gram-positive and four Gram-negative bacterial isolates was generated to test predicted AMPs. S. aureus ATCC 6538P, S. pyogenes (hospital isolate, unknown strain), P. aeruginosa ATCC 10148, and E. coli ATCC 9723H were obtained and tested at the University of Victoria. Additionally, an MDR, carbapenemase-producing New-Delhi metallo-beta-lactamase (CPO-NDM) clinical isolate of E. coli was obtained from the BC Centre for Disease Control. E. coli ATCC 29522 was purchased from Cedarlane Laboratories (Burlington, Ontario, Canada) for comparison of AMP activity between a wild type, drug-susceptible control and the MDR strain. The latter two strains were tested at the BC Centre for Disease Control using identical AST procedures. 32 2.4.6.2 Determination of minimum inhibitory concentrations Bacteria were streaked onto nonselective nutrient agar from frozen stocks and incubated for 18–24 hours at 37℃. To prepare a standardized bacterial inoculum, isolated colonies were suspended in Mueller-Hinton Broth (MHB; Sigma-Aldrich, St. Louis, MO, USA) and adjusted to an optical density of 0.08–0.1 at 600 nm, equivalent to a 0.5 McFarland standard and representing approximately 1–2 × 108 CFU/mL (CFU: colony forming unit). The inoculum was diluted 1:250 in MHB to the target concentration of 5 ± 3 × 105 CFU/mL. Total viability counts from the final inoculum were examined to confirm the target bacterial density was obtained. Selected candidate AMPs were purchased from Genscript (Piscataway, NJ, USA), where they were synthesized using the vendor’s Flexpeptide platform. Lyophilized peptides were suspended in sterile ultrapure water or filter-sterilized 0.2% acetic acid as recommended by solubility testing reports provided with the GenScript synthesis. Candidate AMPs were diluted from 2,560 to 5 µg/mL by a two-fold serial dilution in a 96-well polypropylene microtitre plate (Greiner Bio-One #650261, Kremsmünster, Austria) before 100 µl of the standardized bacterial inoculum of 5 ± 3 × 105 CFU/mL was added to each well. This generated a final test range of 256 to 0.5 µg/mL. MIC values were reported as the peptide concentration that produced no visible bacterial growth after a 16–24 hour incubation at 37℃. 2.4.6.3 Determination of minimum bactericidal concentrations For each AMP dilution series, the contents of the MIC well and the two adjacent wells containing two- and four-fold higher peptide concentrations were plated onto nonselective 33 nutrient agar and incubated for 24 hours at 37℃. The concentration in which 99.9% of the initial inoculum were killed was determined to be the MBC. 2.5 Results 2.5.1 Evaluation of model architecture To demonstrate the effectiveness of each component within our model, we evaluated the model architecture starting from a single Bi-LSTM layer and then gradually adding attention layers over it. Table A.1 summarizes the results of our ablation study, comparing different model architectures using stratified 5-fold cross-validation on the balanced training set with regard to five different measures of accuracy, sensitivity, specificity, F1 score, and AUROC. The first section of the table compares the performance of the complete architecture of AMPlify, with and without ensemble learning, with simpler variations, which include fewer hidden layers. The architecture of the only deep learning based comparator, AMP Scanner Vr.2, was cross-validated on our balanced training set for comparison using two different stopping settings: the optimal fixed number of epochs as stated in their manuscript [92] and early stopping as described in this paper (Table A.1, second section). Although overall performance of AMP Scanner Vr.2 is not strongly influenced by early stopping, it does lead to smaller performance variability as measured by standard deviation values in tests, indicating that the model trained using early stopping is more robust than using a default of 10 epochs. When a single CA layer is added atop the Bi-LSTM layer, the model performs similarly to AMP Scanner Vr.2 based on cross-validation results, with differences smaller than 1% in all metrics except specificity (< 1.4%). After inserting an MHSDPA layer in the middle, the cross-validation 34 results for our model reach 91.70% in accuracy, 91.40% in sensitivity, 92.00% in specificity, 91.68% in F1 score, and 96.92% in AUROC – an overall improvement compared with the architecture without this layer. This indicates that the attention layer learns discriminating features of sequences processed by the Bi-LSTM layer. We note that the final AMPlify architecture already outperforms the AMP Scanner Vr.2 architecture in all metrics in our cross-validation tests. After applying ensemble learning to the proposed architecture, the cross-validation performance is further improved to 92.79% for accuracy, 92.12% for sensitivity, 93.47% for specificity, 92.74% for F1 score and 97.44% for AUROC. To test whether the improvement of our model is statistically significant, we performed paired Student t-tests based on cross-validation results. These tests indicate statistically significant increase in performance of AMPlify over AMP Scanner Vr.2 (early stopped) with regard to all five metrics (p < 0.05). The better performance of AMPlify without ensemble learning (i.e., Bi-LSTM+MHSDPA+CA) over the simple Bi-LSTM model is also statistically significant in all metrics (p < 0.05), suggesting that the attention layers play an important role in the model’s performance. Further, we cross-validated AMPlify on the dataset provided by AMP Scanner Vr.2 and observed that the deep neural network architecture chosen in AMPlify is overall better for the AMP prediction task compared with the architecture of AMP Scanner Vr.2 (Appendix A.1.1, Table A.2). 35 2.5.2 Comparison with state-of-the-art methods With the set of hyperparameters tuned through stratified 5-fold cross-validation, the final version of the AMPlify balanced model was trained using the entire balanced training set, with each of the five single sub-models trained on five different subsets. Here, single sub-model refers to the model with full architecture (Bi-LSTM+MHSDPA+CA) before ensemble learning. AMPlify, along with its single sub-models, were compared on our balanced test set with three other state-of-the-art tools: iAMP-2L [83], iAMPpred [84] and AMP Scanner Vr.2 [92] (Table 2.1). All the tools were evaluated with their original trained models reported. In this list of comparators, AMP Scanner Vr.2 could be trained using third party datasets through a utility provided by the authors (personal communication with Daniel Veltri), and was re-trained on our balanced training set with two different stopping conditions, as previously stated. Table 2.1 Performance comparison of the AMPlify balanced model and other AMP prediction tools on the balanced test set. Performance of different tools are presented with five metrics in percentage: accuracy (acc), sensitivity (sens), specificity (spec), F1 score (F1) and area under the receiver operating characteristic curve (AUROC). Tool Model Acc Sens Spec F1 AUROC iAMPpred originala 74.01 87.90 60.12 77.18 80.70 iAMP-2L originala 77.96 88.26 67.66 80.02 — AMP Scanner Vr.2 originala 78.50 90.66 66.35 80.83 88.33 re-trained, 10 epochsb 90.66 91.14 90.18 90.70 97.40 re-trained, early stoppedc 91.20 90.42 91.98 91.13 97.03 AMPlify balanced single sub-model 1 92.40 90.90 93.89 92.28 97.54 single sub-model 2 91.98 91.02 92.93 91.90 97.40 single sub-model 3 92.51 92.69 92.34 92.53 97.82 single sub-model 4 92.10 90.90 93.29 92.00 97.27 single sub-model 5 92.57 92.57 92.57 92.57 97.98 ensemble 93.71 92.93 94.49 93.66 98.37 a Models presented in the referenced papers, which are available through online servers [83,84,92]. b The best hyperparameter as stated in the referenced paper [92]. c The optimal number of training epochs determined by early stopping is 16. 36 Among the original models of the three comparators, AMP Scanner Vr.2 performs the best on our balanced test set in general, except for its specificity, which is 1.31% lower than iAMP-2L. The accuracy, specificity, F1 score, and AUROC of AMP Scanner Vr.2 were all improved after re-training, with only small changes in sensitivity (< 0.5%). Still, in our benchmarks, the AMPlify balanced model outperforms the comparators tested, including the two re-trained versions of AMP Scanner Vr.2. The AMPlify balanced model achieves the highest accuracy (93.71%), F1 score (93.66%) and AUROC (98.37%), improving upon the performance of the next-best, the re-trained versions of AMP Scanner Vr.2, by 2.51%, 2.53% and 0.97% respectively. The AMPlify balanced model also shows the highest sensitivity (92.93%) and specificity (94.49%) in our tests, suggesting that the model can concurrently reduce false negative and false positive predictions. We have also analyzed the performance of different tools by stratifying the test set based on sequence similarities to their training sets (Figure 2.2). Here, the AMPlify balanced model obtains the highest F1 scores regardless of the sequence similarity threshold chosen, indicating that AMPlify captures other high-level features besides the sequence similarity information. 37 Figure 2.2 Performance comparison of different AMP prediction tools based on the test sequence similarities to their corresponding training sets. F1 scores of AMP prediction tools were calculated on test subsets based on similarities to sequences in the training sets. All the AMP/non-AMP test subsets were derived from the AMPlify balanced test data, with subsets containing 10 or fewer sequences removed. The size of each round marker indicates the number of sequences remaining in the test subset given the sequence similarity threshold. Further, all five single sub-models of the AMPlify balanced model yield favourable performance in accuracy (91.98%–92.57%), specificity (92.34%–93.89%) and F1 score (91.90%–92.57%), despite each single sub-model being trained on 80% of the entire balanced training set (see Chapter 2, Section 2.4.2). The sensitivity values of the five single sub-models range from 90.90% to 92.69%, with two of them outperforming all comparators, while the remaining three performing slightly below the “re-trained, 10 epochs” model of AMP Scanner Vr.2 (< 0.25%). Still, the lower standard deviation values from cross-validation analysis indicate that those single 38 sub-models of the AMPlify balanced model are more robust compared with the “re-trained, 10 epochs” model of AMP Scanner Vr.2 (Table A.1). Similarly, our single sub-models mostly outperform the comparators in AUROC, except one that is on par with the best AMP Scanner Vr.2 model and another that scores lower by 0.13%. The specificity values of the original models of the three comparators are relatively low (< 70%), likely due to their less stringent selection criteria when building their non-AMP sets. After re-training on our balanced training set, we observed substantial improvement in the specificity of AMP Scanner Vr.2 (reaching 90.18% or 91.98%, depending on the number of epochs trained, Table 2.1). We have also conducted a cross-comparison of AMPlify with AMP Scanner Vr.2, re-training our tool on the dataset provided by the AMP Scanner Vr.2 publication [92], illustrating the improved learning capability of our chosen architecture for the AMP prediction task (Appendix A.1.1, Table A.3, Figure A.3). For a comparison of the classification performance of each tool with regard to different classification thresholds, Figure 2.3a presents a series of receiver operating characteristic (ROC) curves for the models compared. The AUROC results shown in Table 2.1 correspond to these ROC curves. Note that the iAMP-2L online server does not allow for parameterization, hence the tool is represented by a single data point and no AUROC value. The ROC curves indicate that the AMPlify balanced model is Pareto-optimal in our tests for any classification threshold. 39 Figure 2.3 Visualization of the AMPlify balanced model performance and the AMP discovery pipeline application results. (a) Receiver operating characteristic (ROC) curves of the AMPlify balanced model and comparators are plotted, with round dots marking the performance at the threshold of 0.5. The iAMP-2L online server only output labels of AMP/non-AMP without the corresponding probabilities, so it appears as a single point on the plot. (b) AMPlify scores from the balanced model vs. peptide lengths of 101 sequences analyzed by AMPlify. 2.5.3 AMP discovery Previous studies have shown that the skin secretions of amphibians are rich in AMPs, which help the animals prevent infection by harmful microorganisms [131]. For this reason, mining the genomes of various frog species for novel AMPs is an attractive proposition. To demonstrate AMPlify’s practical application, it was embedded into a bioinformatics pipeline to find novel AMPs from the North American bullfrog (Rana [Lithobates] catesbeiana) genome [26,108]. For AST, we focused on cationic AMPs acting directly on biological membranes, the activities of which can be directly observed in vitro. Most amphibian AMP precursors possess highly conserved N-terminal prepro regions and hypervariable C-terminal antimicrobial domains [131]. The prepro regions usually end with a lysine-arginine signal for cleavage to produce bioactive a bRaCa-1RaCa-2RaCa-3RaCa-7 40 AMPs [131]. Based on this, we identified candidate precursors from the bullfrog genome using homology search and genome annotation tools. We then derived candidate mature sequences from those precursors to use as input for AMPlify (see Chapter 2, Section 2.4.5 for pipeline details). As a result, we obtained 101 candidate mature sequences, of which 75 were predicted as putative AMPs using the AMPlify balanced model. We selected peptides between 5 and 35 aa in length with a positive charge for further analysis, yielding a final list of 16 peptides (Table 2.2), five of which were previously reported sequences [26,110,132]. The remaining 11 peptides were synthesized and evaluated in vitro. The UpSet plot in Figure A.4 summarizes the results obtained by applying different combinations of the three aforementioned filters (AMPlify predictions, length, and charge) to the 101 candidate mature sequences. Figure 2.3b shows AMPlify prediction results for the 101 candidate mature sequences. 41 Table 2.2 Putative and reported AMP sequences discovered from the bullfrog genome. The genome of Rana [Lithobates] catesbeiana [108] was mined using the AMP discovery pipeline based on AMPlify. Top-scoring peptide sequences were selected for synthesis and validation in vitro. Peptide name Sequence # aa Net chargea MW (Da) AMPlify scoreb RaCa-1 GLLDIIKTTGKDFAVKILDNLKCKLAGGCPP 31 2 3242.93 59.69 RaCa-2 FFPIIARLAAKVIPSLVCAVTKKC 24 4 2589.28 56.45 Ranatuerin-2PRc* AFLSTVKNTLTNVAGTMIDTFKCKITGVC 29 2 3077.66 54.19 Temporin-1Cb*+ FLFPLITSFLSKFLGK 16 2 1858.30 54.19 Palustrin-Ca* GFLDIIKDTGKEFAVKILNNLKCKLAGGCPP 31 2 3303.97 53.99 Ranatuerin-2RC* GLFLDTLKGAAKDVAGKLLEGLKCKITGCKP 31 3 3188.88 52.90 RaCa-3 GLWETIKTTGKSIALNLLDKIKCKIAGGCPP 31 3 3269.95 45.76 Ranatuerin-2C* GVFLDTLKGLAGKMLESLKCKIAGCKP 27 3 2821.49 39.16 RaCa-4 FLTFPGMTFGKLLGK 15 2 1657.05 35.18 RaCa-5 GLLDIIKDTGKTTGILMDTLKCQMTGRCPPSS 32 1 3395.02 34.52 RaCa-6 ATAWRIPPPGMQPIIPIRIRPLCGKQ 26 4 2910.58 32.57 RaCa-7 FFPRVLPLANKFLPTIYCALPKSVGN 26 3 2906.52 28.23 RaCa-8 FPAIICKVSKNC 12 2 1322.65 24.09 RaCa-9 FYFPVSRKFGGK 12 3 1432.69 12.31 RaCa-10 ALVAKIQKFPVFNTLKLCKLELEII 25 2 2872.59 4.05 RaCa-11 SNRDFFKVNIFRLCG 15 2 1816.11 4.04 * Previously reported amphibian peptide sequences [26,110,132]. + Previously reported as a full-length AMP precursor sequence. Uniprot ID: C5IB07. a Net charge at pH = 7. b AMPlify scores from the balanced model. AMPlify scores range from 0 to 80; sequences with AMPlify scores > 3.01 (i.e., AMPlify probability scores > 0.5) are predicted as AMPs. 42 2.5.4 Antimicrobial susceptibility testing A panel composed of six bacteria was selected to test candidate AMP sequences identified using AMPlify: S. aureus ATCC 6538P, S. pyogenes (unknown strain; hospital isolate), P. aeruginosa ATCC 10148, E. coli ATCC 9723H and ATCC 29522, and an MDR CPO-NDM E. coli clinical isolate. E. coli ATCC 29522 was used as a wild-type drug susceptible control strain. Results from AST are presented in Table 2.3. The 11 putative AMP sequences were selected for in vitro AST experiments, and four of them displayed antimicrobial activity against the targets tested: RaCa-1, RaCa-2, RaCa-3, and RaCa-7. RaCa-1 was antibacterial against all E. coli strains tested (MIC = 32–128 µg/mL, MBC = 32–256 µg/mL). RaCa-1 also showed minimal antimicrobial activity against S. pyogenes (MIC = 256 µg/mL, MBC ≥ 256 µg/mL) with no observed inhibition against the S. aureus and P. aeruginosa isolates. RaCa-2 and RaCa-3 inhibited all bacterial strains tested. RaCa-2 exhibited the strongest antibacterial activity against S. aureus and E. coli isolates, inhibiting growth of both species of bacteria at concentrations of 2–4 µg/mL and 4–16 µg/mL, respectively. Specifically, this peptide was bactericidal against E. coli ATCC 9723H (MIC/MBC = 8–16 µg/mL), with similar activity observed against E. coli ATCC 25922 and the MDR E. coli CPO-NDM isolates (MIC/MBC = 4–16 µg/mL). RaCa-2 was also the only AMP tested to have robust bactericidal action against both S. aureus (MIC/MBC = 2–4 µg/mL) and S. pyogenes (MIC/MBC = 64–128 µg/mL). Comparably, RaCa-3 was considerably potent in vitro against S. pyogenes (MIC = 128 µg/mL, MBC = 128–>256 µg/mL), P. aeruginosa (MIC = 64–>256 µg/mL, MBC = 128–>256 µg/mL), E. coli (MIC = 8–32 µg/mL, MBC = 8–64 µg/mL), and to a lesser extent S. aureus (MIC ≥ 256 µg/mL, MBC > 256 µg/mL). RaCa-7 was active against all strains of E. coli (MIC = 43 16–128 µg/mL, MBC = 16–256 µg/mL), with minimal inhibition of S. aureus (MIC ≥ 256 µg/mL, MBC > 256 µg/mL), and no activity against the other two species. Overall, the four novel AMP sequences displayed the strongest activity against the tested E. coli strains. RaCa-2 and RaCa-3 each had potent antibacterial action against the MDR E. coli (CPO-NDM) inhibiting bacterial growth at ≤ 32 µg/mL. Of particular note, there was little or no observed shift in MIC and MBC values when comparing the CPO-NDM E. coli isolate with the ATCC 25922 wild-type control strain. 44 Table 2.3 Minimum inhibitory concentrations and minimum bactericidal concentrations of selected AMP candidates mined from the bullfrog genome following antimicrobial susceptibility testing in vitro. Candidate AMPs were synthesized and purchased from Genscript. Antimicrobial susceptibility testing (AST), with minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) determination, was performed as outlined by the Clinical and Laboratory Standards Institute (CLSI) [128], with some adaptations for testing cationic AMPs as described previously [129]. Data is presented as the lowest effective peptide concentration range (µg/mL) observed in three independent experiments. LL-37, human cathelicidin [19,26], and Tp0751, a non-functional truncated section of a Treponema pallidum protein with similar characteristics to AMPs [130], were used as the positive and negative control peptides, respectively. Peptide name S. aureusa ATCC 6538P Gram-positive S. pyogenesb Gram-positive P. aeruginosaa ATCC 10148 Gram-negative E. colia ATCC 9723H Gram-negative E. colic ATCC 25922 Gram-negative MDR E. colid (CPO-NDM) Gram-negative MIC (µg/mL) MBC (µg/mL) MIC (µg/mL) MBC (µg/mL) MIC (µg/mL) MBC (µg/mL) MIC (µg/mL) MBC (µg/mL) MIC (µg/mL) MBC (µg/mL) MIC (µg/mL) MBC (µg/mL) RaCa-1 >256 >256 256 ≥256 >256 >256 64 – 128 128 – 256 32 – 64 32 – 128 64 – 128 64 – 128 RaCa-2 2 – 4 2 – 4 64 – 128 64 – 128 64 – 128 128 – >256 8 – 16 8 – 16 4 – 16 4 – 16 4 – 16 4 – 16 RaCa-3 ≥256 >256 128 128 – >256 64 – >256 128 – >256 16 – 32 16 – 32 8 – 16 8 – 16 16 – 32 16 – 64 RaCa-4 >256 >256 >256 >256 >256 >256 >256 >256 — — — — RaCa-5 >256 >256 >256 >256 >256 >256 >256 >256 >256 >256 >256 >256 RaCa-6 >256 >256 >256 >256 >256 >256 >256 >256 >256 >256 >256 >256 RaCa-7 ≥256 >256 >256 >256 >256 >256 32 – 64 32 – 256 16 – 128 16 – 128 16 – 128 16 – 128 RaCa-8 >256 >256 >256 >256 >256 >256 >256 >256 >256 >256 >256 >256 RaCa-9 >256 >256 >256 >256 >256 >256 >256 >256 — — — — RaCa-10 >256 >256 >256 >256 >256 >256 >256 >256 >256 >256 >256 >256 RaCa-11 >256 >256 >256 >256 >256 >256 >256 >256 — — — — LL-37 >256 >256 >256 >256 32 – >256 32 – >256 8 – 16 16 – 32 8 – 16 8 – 16 8 – 16 8 – 16 Tp0751 >256 >256 >256 >256 >256 >256 >256 >256 >256 >256 >256 >256 a Bacteria obtained and tested at the University of Victoria. b Unknown strain; hospital isolate. c ATCC quality control strain #25922 purchased from Cedarlane Laboratories (Burlington, Ontario, Canada). d Clinical isolate obtained and tested at the British Columbia Centre for Disease Control. 45 ‘—’ = not tested. Abbreviations: Staphylococcus aureus, Streptococcus pyogenes, Pseudomonas aeruginosa, Escherichia coli; ATCC, American Type Culture Collection; CPO, carbapenemase-producing organism; MDR, multidrug-resistant; NDM, New-Delhi metallo-beta-lactamase. 46 2.6 Discussion and conclusions Here we present AMPlify, a robust deep learning based tool for AMP prediction, and demonstrate the utility of its balanced model in identifying novel AMPs with broad antimicrobial activities. It implements ensemble learning by partitioning its training set – a novel approach – and outperforms existing machine learning methods, including a leading deep learning based model. AMPlify incorporates two attention mechanisms inspired by how humans perceive natural language, paying closer attention to certain regions or words of interest in sentences. We have observed that single sub-models of the AMPlify balanced model were able to outperform the state-of-the-art methods without ensemble learning, and we were able to trace the source of this favourable performance to the inclusion of attention layers. AMPlify is released as an open source tool (https://github.com/bcgsc/AMPlify) under the GPL-3.0 license. Although machine learning methods in general, and AMPlify in particular, perform well in predicting AMPs, their performance can be limited by a paucity of detailed AMP sequence data available for training. First, the models do not usually consider the potential target microorganisms for the predicted AMPs. Although some methods report success at that level of granularity using public data [83,84], incomplete and incorrect annotations in AMP databases are confounding. Second, the models cannot distinguish whether an AMP acts directly on biological membranes and/or by modulating the host immunity, since there is no consistently available data on these features. AMPs that act solely through the latter mode require separate assays and may differ in activity within different organisms. Third, the size of the training data is still small relative to the data typically employed in most deep learning applications. Specially, having more similar sequences with different bioactivities (i.e., non-AMPs that are similar to known 47 AMPs in their sequences) in the training set might help the model to be more sensitive to small changes in the sequences for prediction. However, availability of such information is limited. As a result, all the publicly available AMP prediction tools face difficulty in differentiating between AMPs and non-AMPs that are highly similar in their sequences (Appendix A.1.2, Table A.4). We expect this limitation to be gradually alleviated as more AMPs are discovered and more AMP mutation and truncation studies are done, inspired by tools like AMPlify. Although the size of the training data is unlikely to ever match what is available in natural language processing, image classification, and social network analysis domains, to name a few, AMP prediction tools can still find practical applications as demonstrated here. Using AMPlify, four novel AMPs were identified with proven activity against a variety of bacterial isolates. Promisingly, two of the four presented AMPs demonstrate potent antibacterial activity against the MDR E. coli tested, and there was little or no observed shift in MIC when comparing the MDR and drug-susceptible strains. This suggests that the mechanism-of-action of these AMPs is unlike those used by conventional antibiotics. Thus, AMPs, such as those presented in the current study, have the potential to be used in future drug and clinical development studies as peptide-based substitutes for classical antibiotics. Although several candidates identified using this pipeline did not show any in vitro activity against the bacteria tested, we speculate that they still may possess activity against other bacterial species or other microorganisms (e.g., fungi, viruses), or may demonstrate activity in vivo via host immune response modulation. Further, the structures of these peptides are highly dynamic and susceptible to change in response to the surrounding microenvironment [133], a characteristic often observed in amphipathic α-helices [12]. These AMPs may act as monomers or form multimeric 48 complexes, with their secondary structure flexibly changing in response to interaction with membranes [12,134]. Further studies are required to interrogate AMP mechanisms as these phenomena are not readily observed using classical in vitro methods. Of course, the utility of tools like AMPlify is not limited to discovering AMPs from the bullfrog genome; they can be generically applied to any input sequence. As such, they have the potential to contribute to AMP design or enhancement. In conclusion, with their various use cases, we foresee tools like AMPlify as being instrumental in expanding the current arsenal of antimicrobial agents effective against WHO priority pathogens. 49 Chapter 3: AMP discovery from protein sequence databases 3.1 Publication note The first half of the contents in this chapter was previously published in BMC Research Notes in 2023 [135]. This publication describes the datasets and models in AMPlify v1.1. It extends AMPlify v1.0 by adding an auxiliary model (i.e., the imbalanced model) that was trained on an imbalanced training set in which non-AMP sequences far outnumber AMP sequences. The balanced and imbalanced models together form AMPlify v1.1. I am the lead author of this work, under the supervision of Dr. Inanç Birol. I implemented the model, collected the data, and drafted the manuscript, in collaboration with my colleagues. A manuscript describing the second half of the contents in this chapter has been developed, and is about to be submitted to a journal for peer-review. The manuscript shows the application of AMPlify in AMP mining from protein sequence databases with both the balanced and imbalanced models utilized. The scripts for the AMP mining workflow have been incorporated into AMPlify v2.0. I am the lead author of this work, under the supervision of Dr. Inanç Birol. I designed and implemented the AMP mining workflow, collected and curated the datasets, conducted peptide structure prediction, performed peptide selection, analyzed the results, and drafted the manuscript, in collaboration with my colleagues. All in vitro validations were conducted by my wet lab collaborators. 50 3.2 Author summary Public protein sequence databases, comprising over 200 million records and growing [112], serve as comprehensive compendia of sequences from a broad range of source organisms, and large-scale in silico probing of those databases for novel AMPs has never been reported. In this study, we updated AMPlify v1.0 to a new version by introducing an auxiliary model (the imbalanced model) alongside the balanced model described in Chapter 2. We demonstrate the utility of the two models in different use cases, and propose an AMP mining workflow to predict novel AMPs from the UniProtKB/Swiss-Prot [112] database using AMPlify as its discovery engine. AMPlify v2.0 has been released to provide users with scripts for the AMP mining workflow, along with the balanced and imbalanced models. Using the AMP mining workflow, we identified 8,008 novel putative AMPs from all eukaryotic sequences in the database. Focusing on the practical use of AMPs as suitable antimicrobial agents with applications in the poultry industry, we prioritized 40 of those AMPs based on their similarities to known chicken AMPs in predicted 3D structures. In our tests, 13 out of the 38 successfully synthesized peptides showed antimicrobial activity against Escherichia coli and/or Staphylococcus aureus. 3.3 Introduction Current AMP mining studies mostly focus on genomic [80,136,137], transcriptomic [32,93], or proteomic [138] resources, with specific organisms chosen for analysis. AMP mining by large-scale scanning of public protein sequence databases, such as UniProt (https://www.uniprot.org) [112], in which the majority of the sequences have already been annotated with their functions, has never been reported. Still, mining AMPs using comprehensive protein sequence databases offers another valuable strategy for discovering novel AMPs. First, these large protein sequence 51 databases allow researchers to discover novel AMPs from a vast number of available organisms, rather than biasing on popular source organisms such as amphibians [26], providing the community with a broad source of AMPs. Second, there are many uncharacterized sequences awaiting annotation in public databases, which serves as an untapped source of AMPs. Further, it would be of interest to uncover possible antimicrobial properties of characterized proteins or peptides, where only non-antimicrobial functions have been reported to date. Studies have found that some proteins or peptides with other biological functions exhibit antimicrobial properties [139,140]. Histatins, a series of peptides found in saliva, belong to this category. Histatins harbor multiple functions beneficial to oral health besides antimicrobial activity, including oral hemostasis, development of acquired tooth pellicle, and assistance in bonding of some metal ions [140]. Protein sequence databases represent a valuable resource for novel AMP mining, which we demonstrate in our study. AMP mining tasks as such typically take less curated candidate sequence sets where AMPs comprise only a small proportion as their inputs, and keeping the number of false positives to a minimum would be essential. Here, we introduce an auxiliary model (i.e., the imbalanced model) in AMPlify, which gives the flexibility to predict AMPs from larger sequence databases. Accordingly, AMPlify has been updated to a new version, providing an additional option to choose between the balanced and imbalanced models. In this work, we also present an AMP mining workflow with AMPlify as the core AMP prediction module, to predict novel AMPs from all eukaryotic sequences in the UniProtKB/Swiss-Prot database [112], which contains only manually annotated and reviewed records from the entire UniProt database. The scripts for the proposed AMP mining workflow, which utilizes both the balanced and imbalanced models, have 52 been incorporated into AMPlify v2.0. With this workflow, we identified 8,008 novel putative AMPs. Motivated by the growing global concern of other animals passing antibiotic-resistant bacteria to humans as a direct consequence of antibiotics overuse in animal agriculture, including poultry farming [141], we tested a total of 38 predicted peptides in vitro based on their structural similarities to known chicken AMPs. We conducted preliminary tests on the synthesized peptides using two lab strains of Escherichia coli ATCC 25922 and Staphylococcus aureus ATCC 29213, and 13 demonstrated antimicrobial activity against at least one bacterial strain. Chicken AMPs have theoretically been evolved to fight against pathogens in their environment [12], hence exogenous AMPs with high structural similarities to known chicken AMPs may help them cope with bacterial challenges in the farm environment. We note that all peptide structural similarity comparisons in this work were conducted based on in silico predicted structures. 3.4 Methods 3.4.1 Generation of the datasets This study comprises two collections of data: 1) the AMPlify imbalanced training and test datasets, and 2) the datasets used in the AMP mining workflow. In contrast to the balanced training and test sets that were used to train and test the AMPlify balanced model as described in Chapter 2, Section 2.4.1, we built imbalanced training and test sets for the AMPlify imbalanced model proposed in this work. The positive samples (AMP sequences) of the imbalanced training/test sets were kept the same as the balanced training/test sets. Unlike the balanced training and test sets, we took all the 128,445 available non-AMP sequences selected through keyword filtering (Figure A.1) described in Chapter 2, Section 53 2.4.1 to form the imbalanced training and test sets without selecting the same number of samples as the positive sets. The ratio of the number of non-AMPs to AMPs was kept around 30.8 across both the imbalanced training and test sets; the imbalanced training set contains 3,338 AMP sequences and 102,756 non-AMP sequences, and the imbalanced test set contains 835 AMP sequences and 25,689 non-AMP sequences. Regarding both balanced and imbalanced training/test sets, it is worth noting that there is no overlap between any of the training and test set pairs. On the other hand, the AMP mining workflow involves four main datasets: 1) all eukaryotic entries from UniProtKB/Swiss-Prot [112], 2) entries annotated with AMP-related keywords in UniProtKB/Swiss-Prot [112], 3) known AMP sequences in AMP databases, and 4) known chicken AMP sequences. We note that all UniProtKB/Swiss-Prot sequences were downloaded from the 2022_02 release. All eukaryotic entries from the UniProtKB/Swiss-Prot database [112] were downloaded by using the query “(taxonomy_id:2759) AND (reviewed:true)”. This set of sequences include 186,302 distinct sequences from 195,188 UniProt entries. The set of entries annotated with AMP-related keywords in the UniProtKB/Swiss-Prot database [112] includes 18,470 distinct sequences from 19,845 UniProt entries. UniProt entries with their annotations containing any of the 16 AMP-related keywords stated in Chapter 2, Section 2.4.1 were downloaded. We note that 11,925 sequences from 12,229 UniProt entries in this set belong to the above eukaryotic sequence set. 54 The known AMP sequence set comprises 4,538 distinct AMP sequences. All AMP sequences were downloaded from two curated AMP databases: APD3 [17] on July 11, 2022, and DADP [110] on December 6, 2018. The known chicken AMP sequence set includes 22 sequences in total. All those sequences were downloaded on October 14, 2022 from the APD3 database [17] by searching for source organism “Gallus gallus”. 3.4.2 Model training and evaluation The AMPlify imbalanced model was of the same architecture as the balanced model (see Chapter 2, Section 2.4.3), but was re-trained on the imbalanced training set. The training process was similar to that of the balanced model (see Chapter 2, Section 2.4.2), with some adaptations as described below. In contrast to the training of the single sub-models of the balanced model, which involved early stopping, each single sub-model of the imbalanced model was trained using a fixed number of epochs considering the long training time on a huge training set. The best tuned number of epochs was 50. Furthermore, we optimized a set of class weights (AMP: 0.8333, non-AMP: 0.1667) when calculating the loss function during training to ensure that the model would not bias too much on the majority class, while the loss calculations of the two classes were treated equally for the balanced model (in other words, with a weight ratio of 1:1). Both the optimal epoch number and class weights were tuned based on accuracy scores from 5-fold cross- 55 validation. This set of five single sub-models forms a new ensemble model of AMPlify (the imbalanced model). The performance of both AMPlify balanced and imbalanced models was evaluated using five metrics: accuracy, sensitivity, specificity, F1 score and AUROC, on both the balanced and imbalanced test sets. Both AMPlify models were also compared with other state-of-the-art-tools: iAMP-2L [83], iAMPpred [84] and AMP Scanner Vr.2 [92] on the two test sets we built. We additionally compared the performance of an integration of AMPlify balanced and imbalanced models, where only sequences predicted as AMPs by both models are determined to be AMPs, on the two test sets. We note that the online server of iAMP-2L [83] was down at the time this analysis was done (November 11, 2022), so its results were not compared on the imbalanced test set. 3.4.3 AMP mining workflow The AMP mining workflow we report in this study includes five steps, as shown in Figure B.1. In Step 1, all eukaryotic protein sequences from the UniProtKB/Swiss-Prot database [112] were cleaved to generate mature sequences using the cleavage module of the AMP discovery pipeline rAMPage v1.0.1 [32]. The cleavage module of rAMPage is based on ProP v1.0c [142], a machine learning based tool for cleavage site prediction for eukaryotic protein sequences. The rAMPage cleavage module also provides post-processing of the ProP output [32]. ProP predicts all the cleavage sites but only annotates the corresponding signal peptide sequence – it does not provide labels to mature peptide sequences and pro-sequences [32,142]. As a result, for each 56 putative precursor sequence, the rAMPage cleavage module takes all cleaved pieces, excluding the predicted signal peptide, as well as all possible recombinations of non-adjacent cleaved pieces (with a maximum of three pieces within each recombination) as candidate mature sequences [32]. Additionally, the rAMPage cleavage module removes candidate mature sequences with lengths < 2 or > 200 aa, as AMPs are typically short sequences [12,143]. The redundancy removal function of the rAMPage cleavage module was turned off in this study, as the same putative AMP sequence from different UniProt entries were kept as different records (Figure B.1) for convenience of future investigations into their source organisms. However, we did consider them as the same putative AMP sequence when calculating the sequence counts. In Step 2, sequences predicted to lack signal peptides were removed, as AMPs are mostly secreted peptides [113]. In Step 3, the remaining candidate mature sequences from Step 2 were passed through the imbalanced model of AMPlify v2.0 for a preliminary filtering. Candidate mature sequences predicted as AMPs by the AMPlify imbalanced model were passed on to the next step. Sequences with non-standard amino acids were also filtered out by AMPlify, as AMPlify does not assign those sequences with any predictions. In Step 4, the predicted AMPs from Step 3 were then passed through the balanced model of AMPlify v2.0 for a more precise filtering. Sequences that were predicted as AMPs by both AMPlify imbalanced and balanced models were collected as the final set of predicted AMPs. 57 In Step 5, all the predicted AMPs from Step 4 were checked against the known AMP sequence set as well as the annotations of the corresponding UniProt entries for novel putative AMPs. Predicted AMPs were only considered as novel putative AMPs if they were not in the known AMP sequence set and if their corresponding UniProt entry annotation did not have AMP-related keywords. 3.4.4 Sequence and structural similarities between peptides We used two measures of peptide similarity: sequence similarity and structural similarity. The sequence similarity was calculated as described in Chapter 2, Section 2.4.4. The structural similarity between two peptides was measured with TM-scores normalized by the average peptide sequence length [144] using TM-align [145]. TM-scores are between 0 and 1, with higher TM-scores indicating higher similarity between the two structures [144]. The 3D structures of peptides were predicted using ColabFold [146], which improves the prediction speed of AlphaFold2 [147] by combining it with MMseqs2 [148] homology search. The sequence/structural similarity of a single peptide to a set of peptides was defined as the maximum of all sequence/structural similarity values calculated between that peptide and the peptides in the target set for comparison (i.e., the sequence/structural similarity of that peptide to its most similar target set peptide in their sequences/structures). 58 3.4.5 Selecting putative AMPs for validation Considering the large number of putative AMPs identified by the AMP mining workflow, we prioritized a small subset of the sequences for synthesis and in vitro validation. In this work, we primarily focused on the applications of AMPs in poultry farming. While 3D structures of proteins or peptides may serve as a critical factor in determining their biological functions [149] and eukaryotic AMPs have evolved to fight against the pathogens that infect the respective hosts [12], we prioritized putative AMPs by their structural similarities to known chicken AMPs, foreseeing their potentials in chicken farming against pathogens that affect chickens. Among all the novel putative AMPs discovered by the AMP mining workflow, we prioritized 397 short cationic peptides with AMPlify scores ≥ 10 (i.e., AMPlify probability scores ≥ 0.9) for selection, as shorter peptides are more cost-effective to synthesize [32]. We define short cationic peptides as those with lengths between 5 and 35 aa and net charges greater than 0. We note that AMPlify score is a prediction score reported by AMPlify as described in Chapter 2, Section 2.4.2, and here the AMPlify scores from the balanced model were taken for analysis. First, the 3D structures of the 397 putative AMPs, together with the 22 known chicken AMPs, were predicted by ColabFold [146]. Next, the structural similarity (TM-score) of each putative AMP to the known chicken AMPs (i.e., structural similarity of the putative AMP to the most structurally similar known chicken AMP) was calculated using TM-align [145]. The reference chicken AMP for a putative AMP was defined as the most structurally similar known chicken AMP to that putative AMP. Finally, we ranked the peptides by their structural similarities to known chicken AMPs. The top 30 putative AMPs with highest TM-scores were prioritized for 59 synthesis, as well as the bottom 10 with lowest TM-scores for comparison (Table B.1, Table B.2). Furthermore, the three reference chicken AMPs for the top 30 putative AMPs: Chicken CATH-2 [150], Chicken CATH-3 [151], and Ovipin [152], were also sent for synthesis for comparison (Table B.3). We note that two of the top 30 putative AMPs could not be successfully synthesized, resulting in a final number of 38 putative AMPs in total proceeding to further in vitro validation. 3.4.6 Antimicrobial susceptibility testing The procedures are similar to what have been described in Chapter 2, Section 2.4.6, except for some adaptations as described in this section. All the tests were done at the BC Centre for Disease Control. Laboratory isolates of E. coli 25922 and S. aureus 29213 were purchased from the American Type Culture Collection (ATCC; Manassas, VA, USA) and were used to test the selected putative AMPs. Instead of LL-37 and Tp0751, we used a known AMP Ranatuerin-4 [153] from the American bullfrog and an in-house peptide [TKPKG]3 (OT15) as the positive and negative control peptides, respectively. We note that OT15 was truncated and derived from a negative control peptide [TKPKG]4 (OT20), which, while not antimicrobial, shares similar characteristics with AMPs and has been used in previous studies [154]. 60 A two-fold serial dilution from 1,280 down to 2.5 μg/mL (instead of the previously used range of 2,560 down to 5 µg/mL) was prepared in sterile 96-well polypropylene microtitre plates (Greiner Bio-One #650261, Kremsmünster, Austria) before the addition of 100 μL of the standardized bacterial inoculum, providing a final AMP testing range of 128 down to 0.25 μg/mL. 3.4.7 Hemolysis assay The toxicity to red blood cells (RBCs) of the selected peptides was evaluated by hemolysis experiments. Whole blood from healthy donor pigs was purchased from Lampire Biological Laboratories (Pipersville, PA, USA). RBCs were washed and isolated by centrifugation using Roswell Park Memorial Institute (RPMI) medium (Life Technologies, Grand Island, NY, USA; Gibco cat# 11835-030). All centrifugation steps were performed at 500× g for five minutes in an Allegra-6R centrifuge (Beckman Coulter, CA, USA). Peptides were suspended and serially diluted from 1,280 to 10 μg/mL using RPMI medium in a 96-well polypropylene microtitre plate, and then they were combined with 100 μL of the 1% RBC solution. This resulted in a final AMP testing range of 128 to 1 μg/mL. Following an incubation at 37℃ for 30–45 minutes, plates were centrifuged and a 1/2 volume from each supernatant was transferred to a new 96-well plate. The absorbance of the wells was measured at 415 nm utilizing the Cytation 5 Cell Imaging Multimode Reader (BioTek, CA, USA); the peptide concentration that lysed 50% of the RBCs (HC50) was used to report the hemolytic activity. Absorbance readings from wells containing RBCs treated with 11 μL of a 2% Triton-X100 solution or RPMI medium (AMP solvent-only) were used to define 100% and 0% hemolysis, respectively. 61 3.5 Results 3.5.1 Evaluation of model performance Table 3.1 compares the performance of the AMPlify balanced and imbalanced models, which were trained on the balanced and imbalanced training sets respectively and assessed on the balanced test set. The imbalanced model of AMPlify only outperforms the balanced model in sensitivity (94.37% vs. 92.93%). The balanced model of AMPlify outperforms the imbalanced model in accuracy (93.71% vs. 89.22%), specificity (94.49% vs. 84.07%), F1 score (93.66% vs. 89.75%), and AUROC (98.37% vs. 96.88%). Even though our imbalanced model does not perform as well as the balanced model on the balanced test set in general, it still surpasses our three comparators by at least 10.72% in accuracy, 3.71% in sensitivity, 16.41% in specificity, 8.92% in F1 score, and 8.55% in AUROC. Table 3.2 shows the same comparison but assessing the performance of all the models on the imbalanced test set. We note that the online server of iAMP-2L [83] was down at the time this analysis was done (November 11, 2022), so its results were not compared here. In this comparison, the AMPlify imbalanced model outperforms the balanced model in all five metrics of accuracy (98.94% vs. 95.97%), sensitivity (94.37% vs. 92.93%), specificity (99.09% vs. 96.06%), F1 score (84.87% vs. 59.19%), and AUROC (99.54% vs. 98.06%). However, our balanced model still beats the best comparator, AMP Scanner Vr.2, by 24.71% in accuracy, 2.27% in sensitivity, 25.43% in specificity, 42.62% in F1 score, and 6.62% in AUROC. Generally speaking, the AMPlify balanced model performs better on the balanced test set, while the AMPlify imbalanced model performs better on the imbalanced test set. The difference in balanced and imbalanced model performance mainly lies in the change in the distribution of the training data. 62 Based on the performance of the two models in their respective advantageous application scenarios, the AMP prediction module of the AMP mining workflow utilized both the AMPlify balanced and imbalanced models to obtain a curated set of candidate AMP sequences. As shown in Figure B.1, the prediction module (Step 3 and Step 4) performed a two-stage filtering by applying the imbalanced and balanced models in turn. The integration of the two models (noted as “balanced + imbalanced” in Table 3.1 and Table 3.2) can also be considered as a filtering scheme of only selecting sequences that are predicted by both models as AMPs. On the balanced test set, where the AMPlify balanced model performs better than the imbalanced model except for sensitivity, the integrated model does not show much improvement when compared with using the two models independently, with only an increase in specificity by 1.2% (95.69% vs. 94.49%) (Table 3.1). On the imbalanced test set, where the AMPlify imbalanced model performs better than the balanced model in all five metrics, the integrated model achieves improvement in accuracy (99.31% vs. 98.94%), specificity (99.56% vs. 99.09%), and F1 score (89.25% vs. 84.87%) (Table 3.2). For highly imbalanced candidate sequence sets where non-AMP sequences far outnumber AMP sequences, it is crucial to minimize the number of false positives to prevent an excessive number of non-AMP sequences from being forwarded to downstream in vitro validation. Although there is a slight decrease in sensitivity (91.50% vs. 94.37%) as shown in Table 3.2, the improvement in other metrics, particularly specificity, suggests that the integrated model performs better than the imbalanced model alone when facing prediction scenarios where only a small portion of a large candidate sequence set are AMPs. Additionally, all AMPlify models (balanced, imbalanced, and their integration) outperform other existing AMP prediction methods for comparison on the two test sets (Table 3.1, Table 3.2). 63 Considering that our source for AMP mining is a large protein sequence database [112], in which most of the sequences are not expected to be AMPs, filtering for candidate AMP sequences by applying the integration of balanced and imbalanced models is a sensible way to reduce the number of false positives. Table 3.1 Performance comparison of AMPlify balanced/imbalanced models and other AMP prediction tools on the balanced test set. Values of accuracy (acc), sensitivity (sens), specificity (spec), F1 score (F1) and area under the receiver operating characteristic curve (AUROC) are presented in percentage. Tool Model Acc Sens Spec F1 AUROC iAMPpred originala 74.01 87.90 60.12 77.18 80.70 iAMP-2L originala 77.96 88.26 67.66 80.02 —* AMP Scanner Vr.2 originala 78.50 90.66 66.35 80.83 88.33 AMPlify balanced 93.71 92.93 94.49 93.66 98.37 imbalanced 89.22 94.37 84.07 89.75 96.88 balanced + imbalancedb 93.59 91.50 95.69 93.46 —* a Models presented in the referenced papers, which are available through online servers [83,84,92]. b Only sequences predicted as AMPs by both models are determined to be AMPs. * AUROC values were not calculated for models which only output predicted labels instead of probabilities. Table 3.2 Performance comparison of AMPlify balanced/imbalanced models and other AMP prediction tools on the imbalanced test set. Values of accuracy (acc), sensitivity (sens), specificity (spec), F1 score (F1) and area under the receiver operating characteristic curve (AUROC) are presented in percentage. We note that the online server of iAMP-2L [83] was down at the time this analysis was done (November 11, 2022), so its results were not compared here. Tool Model Acc Sens Spec F1 AUROC iAMPpred originala 14.29 87.90 11.90 6.07 55.54 AMP Scanner Vr.2 originala 71.26 90.66 70.63 16.57 91.44 AMPlify balanced 95.97 92.93 96.06 59.19 98.06 imbalanced 98.94 94.37 99.09 84.87 99.54 balanced + imbalancedb 99.31 91.50 99.56 89.25 —* a Models presented in the referenced papers, which are available through online servers [84,92]. b Only sequences predicted as AMPs by both models are determined to be AMPs. * AUROC values were not calculated for models which only output predicted labels instead of probabilities. 64 3.5.2 Predicted AMPs By applying the AMP mining workflow to all eukaryotic sequences in UniProtKB/Swiss-Prot [112], 10,720 distinct candidate mature sequences were predicted as AMPs, of which 8,008 (74.70%) were novel putative AMPs (Figure B.1). Sequences without predicted signal peptides were filtered out in Step 2 of the workflow. Figure 3.1 Sequence similarity distributions of the predicted AMPs from the UniProtKB/Swiss-Prot database to known AMPs. The sequence similarity distribution of all 10,720 predicted AMPs to known AMPs from Antimicrobial Peptide Database (APD3) [17] and Database of Anuran Defense Peptides (DADP) [110] was visualized, along with that of the 8,008 novel putative AMPs from all predicted AMPs. The former distribution holds a mean of 38.27% and a standard deviation of 17.28%, while the latter holds a mean of 32.71% and a standard deviation of 7.21%. The sequence similarity of each predicted AMP to known AMPs was considered as the sequence similarity of that predicted AMP sequence to its most similar sequence in the known AMP set, based on which the distributions were plotted. 65 The 10,720 predicted AMPs have an average length of 52.83 aa and an average net charge of 3.02, while the 8,008 novel putative AMPs have an average length of 52.49 aa and an average net charge of 3.04 (Figure B.2). Compared with the 4,538 known AMP sequences, which have an average length of 30.21 aa and an average net charge of 3.05 (Figure B.2), the average lengths of both the predicted AMP set and the novel putative AMP set mined from UniProtKB/Swiss-Prot are at least 20 aa larger. However, researchers tend to prioritize shorter sequences for validation due to their lower synthesis costs for in vitro validation [32], which could explain the difference in average lengths. Furthermore, the novel putative AMP sequences exhibit a remarkable level of novelty, sharing a low sequence similarity level of 32.71% on average to the known AMP sequences, as shown in Figure 3.1. Tracing back to the original source entries in the protein sequence database, the 10,720 predicted AMPs corresponded to 8,481 parent sequences from a total of 8,862 UniProt entries, while the 8,008 novel putative AMPs corresponded to 6,349 parent sequences from 6,654 UniProt entries (Figure B.1). In our analysis, we define parent sequences with AMPs predicted from their cleaved mature sequences to be putative AMP precursor sequences, and UniProt entries of those putative AMP precursor sequences to be putative AMP entries. Among all 8,481 putative AMP precursor sequences identified, 86.84% (7,365) only had one distinct predicted AMP sequence, as shown in Figure 3.2. However, we did notice cases where multiple AMP sequences were predicted from a single putative precursor sequence. Specifically, 698 putative precursor sequences were predicted with two distinct AMPs from each, 207 66 predicted with three, 74 predicted with four, and 57 predicted with five. There were 80 putative precursor sequences that had more than five distinct AMPs predicted from each of them. Figure 3.2 Distribution for the number of predicted mature AMP sequences found within each putative AMP precursor sequence mined from the UniProtKB/Swiss-Prot database. The bar chart was plotted based on 8,481 distinct putative AMP precursor sequences from the 8,862 putative AMP entries. Each bar shows the number of putative AMP precursor sequences that were predicted with the corresponding number of distinct mature AMPs by AMPlify. The doughnut chart in Figure 3.3 categorizes the 8,862 putative AMP entries identified by the AMP mining workflow by source organisms. According to the largest source organism groups of known AMPs as summarized by APD3 [17] (see Chapter 1, Section 1.1.2.3), all the putative AMP entries we report were classified into the following five categories: amphibian, plant, insect, mammalian, and other entries. The category of bacterial AMPs was not included, as we only considered eukaryotic AMPs in this work. As can be seen from the doughnut chart in 67 Figure 3.3, mammalian entries constitute the largest source organism category for putative AMP entries, comprising 37.93% (3,361/8,862) of all. Out of all 3,361 putative AMP entries from the mammalian category, 2,703 (80.42%) of them had novel AMPs predicted from their cleaved candidate mature sequences, making it the largest source organism category for novel putative AMP entries as well. Putative AMP entries not belonging to any of the four specified source organism categories also make up a large portion, following the mammalian category, and represent 33.78% (2,994/8,862) of all putative AMP entries. In this category of putative AMP entries, 79.93% (2,393/2,994) had novel AMPs predicted from their cleaved candidate mature sequences. Plant entries form the third largest group among all putative AMP entries, contributing 17.42% (1,544/8,862) to the total. Overall, 1,170 out of 1,544 putative plant AMP entries (75.78%) were considered as novel discoveries. Amphibian and insect categories are the two smallest in this analysis, with 509 and 454 putative AMP entries in each. While 59.25% (269/454) of the putative insect AMP entries were determined to be novel discoveries, the amphibian category is the only source organism category with more than half of the putative AMP entries (76.62%) already identified with known mature AMP sequences and/or annotated with AMP-related keywords. We postulate the main reason for this is that amphibians are considered as a rich source of AMPs [26], hence has become a popular source for AMP mining [32,93], resulting in more of the amphibian AMPs having been discovered and annotated than those from other organisms. Moreover, amphibians are also the source organism group with the largest proportion of entries (7.64%) in UniProtKB/Swiss-Prot [112] identified as putative AMP entries by the AMP mining workflow, followed by mammals (4.98%), insects (4.73%), source organisms other than the specified ones (4.27%), and plants (3.74%), as can be seen from the pie charts in Figure 3.3. We did not observe any instances where a single putative AMP precursor 68 sequence had novel putative AMP(s) predicted, while also containing known AMP(s) in its cleaved candidate mature peptides and/or being annotated with AMP-related keywords. We also note that UniProt entries annotated with AMP-related keywords can be proteins or peptides that assist in the defense against microbes rather than possessing antimicrobial activities themselves. However, to ensure a cleaner set of novel putative AMPs, all of the predicted AMPs with their parent sequence entries annotated with AMP-related keywords were not considered as novel discoveries. Figure 3.3 Categorization of the putative AMP entries identified from the UniProtKB/Swiss-Prot database based on source organisms. The 8,862 putative AMP entries with mature AMPs predicted by AMPlify were classified into five categories (mammalian, plant, amphibian, insect, and other entries) based on their source organisms, as shown in the middle doughnut chart. The predicted AMPs were further checked against the known AMP sequences in Antimicrobial Peptide Database (APD3) [17] and Database of Anuran Defense Peptides (DADP) [110] as well as the annotations of the corresponding UniProt entries. Those not found in those two AMP databases and not annotated with the AMP-related keywords were labeled as novel AMP sequences. The five small pie charts surrounding the middle doughnut chart provide additional information on the percentages of entries in UniProtKB/Swiss-Prot identified as putative AMP entries by the AMP mining workflow. 7.64%92.36%4.73%95.27%4.98%95.02%4.27%95.73%3.74%96.26%Mammalian entries(67,547)Other entries(70,095)Insect entries(9,594)Amphibian entries(6,658)Plant entries(41,294) 69 3.5.3 In vitro validation results We selected a total number of 40 short cationic novel putative AMP sequences with AMPlify scores ≥ 10 for synthesis based on their structural similarities, as measured by TM-scores, to known chicken AMPs (Figure B.3), with detailed methods shown in the Chapter 3, Section 3.4.5. Of those selected, 30 had the highest TM-scores, while the remaining 10 had the lowest TM-scores. Table B.1 and Table B.2 list the characteristics of the 40 peptides and their original UniProt entry information, respectively. We note that two of the top 30 sequences were not successfully synthesized, resulting in a final list of 38 putative AMPs for in vitro validation. The three reference chicken AMPs, Chicken CATH-2 [150], Chicken CATH-3 [151], and Ovipin [152], that matched the top 30 sequences were also tested for comparison, with their information shown in Table B.3. All synthesized peptides were tested against two bacterial isolates: the Gram-negative E. coli ATCC 25922, and the Gram-positive S. aureus ATCC 29213. Porcine RBCs were used to assess the hemolytic activity of the peptides. Out of the 38 putative AMPs synthesized, 13 displayed antimicrobial activity against E. coli ATCC 25922, with three of the 13 additionally active against S. aureus ATCC 29213. Figure 3.4 summarizes the antimicrobial and hemolytic activities (in MIC and HC50, respectively) of the 13 active peptides, with the entire in vitro validation results of all peptides shown in Table B.4. The left section of Figure 3.4 shows the results for the 11 active peptides from the set of 30 most structurally similar putative AMPs to known chicken AMPs, with their reference chicken AMPs for comparison. Results of the two active peptides from the set of 10 least structurally similar putative AMPs to known chicken 70 AMPs are shown in the right section of Figure 3.4. Figure B.4 additionally shows the activity of all tested putative AMPs with regard to their AMPlify scores and structural similarities to known chicken AMPs. Among the 10 tested putative AMPs with Chicken CATH-2 as their reference chicken AMP, five showed antimicrobial activity in our tests. Within this group of five peptides, HoSa1, with its original UniProt entry annotated as an olfactory receptor from human [112], possessed the strongest antimicrobial activity against E. coli ATCC 25922 (MIC = 16 μg/mL), and was the only peptide that showed additional activity against S. aureus ATCC 29213 (MIC = 128 μg/mL). DiDi2 showed the same antibacterial activity as HoSa1 against E. coli ATCC 25922 with an MIC of 16 μg/mL, with its original UniProt entry annotated as a putative uncharacterized transmembrane protein from social amoeba [112]. DaRe1, the parent sequence of which is annotated as a GTP-binding protein from zebrafish, inhibited the growth of E. coli ATCC 25922 at an MIC of 32–64 μg/mL. MoIn1 and MuMu1 presented weakest antibacterial activity against E. coli ATCC 25922 in this group, with MICs of 64–>128 μg/mL and ≥128 μg/mL. The parent sequence of MoIn1 is annotated as part of an intermediate translocation complex at the inner envelope membrane of chloroplasts from mulberry, while the parent sequence of MuMu1 is annotated as a mouse surfeit locus protein, a component of the MITRAC (mitochondrial translation regulation assembly intermediate of cytochrome c oxidase complex) [112]. The reference chicken AMP, Chicken CATH-2, possessed stronger antimicrobial activity against E. coli ATCC 25922 (MIC = 8–16 μg/mL) and S. aureus ATCC 29213 (MIC = 32 μg/mL) than all five peptides in this group. 71 Among the 16 tested putative AMPs with Chicken CATH-3 as their reference chicken AMP, five showed antimicrobial activity in our tests. Among the five peptides in the group, PlVi1 had the strongest antimicrobial activity against both E. coli ATCC 25922 (MIC = 4–8 μg/mL) and S. aureus ATCC 29213 (MIC = 32 μg/mL), and it was also the most active peptide among all 38 putative AMPs tested. The parent sequence of PlVi1 is annotated as the precursor of a RxLR effector protein that completely suppresses the host cell death induced by cell death-inducing proteins, which is derived from a type of oomycete that causes the downy mildew disease of grapevines [112]. HoSa4, derived from an IQ domain-containing protein from human without detailed functions annotated in UniProtKB/Swiss-Prot [112], was the second most active AMP in this group, antibacterial against E. coli ATCC 25922 and S. aureus ATCC 29213 with MICs of 32 μg/mL and 64–128 μg/mL. We note that PlVi1 and HoSa4 are the only two peptides that were active against both bacterial strains tested in this group. ArTh11 inhibited the growth of E. coli ATCC 25922 at an MIC of 64–128 μg/mL. The parent sequence of ArTh11 is annotated as an F-box protein from mouse-ear cress in UniProtKB/Swiss-Prot [112] but without detailed functions specified. SuSc1 and HoSa7 were the two least active peptides in the group, with antibacterial activity against E. coli ATCC 25922 at MICs of 128 μg/mL and 64–>128 μg/mL, respectively. SuSc1 was derived from a precursor sequence of adrenomedullin in pigs and cattle, while HoSa7 was cleaved from a human G-protein coupled receptor, as annotated in UniProtKB/Swiss-Prot [112]. The reference chicken AMP, Chicken CATH-3, possessed stronger antimicrobial activity against E. coli ATCC 25922 (MIC = 2–4 μg/mL) and S. aureus ATCC 29213 (MIC = 2 μg/mL) than all five peptides in this group. 72 Two peptides in the top 30 putative AMP list matched Ovipin as their reference chicken AMP, with one (GaGa2) showing antimicrobial activity in our tests. However, it only showed minimal antibacterial activity against E. coli ATCC 25922 (MIC = 128 μg/mL), with no activity against S. aureus ATCC 29213 (MIC > 128 μg/mL). This peptide was derived from a chicken tenascin [112]. The reference chicken AMP, Ovipin, did not show any activity against the two bacterial strains tested in this study (MIC > 128 μg/mL), but was reported to be bioactive against a Gram-positive Micrococcus luteus strain in a previous study [152]. Among the 10 least structurally similar putative AMPs to known chicken AMPs, only two (GoGo1 and UnBi1) showed activity in our tests. GoGo1 was part of the sequence of a transcriptional repressor found in five different primate species of western lowland gorilla, bonobo, Bornean orangutan, chimpanzee, as well as human [112]. It inhibited the growth of E. coli ATCC 25922 with an MIC of 64 μg/mL. The parent peptide of UnBi1 was annotated as a neurotoxin from sea snail in UniProtKB/Swiss-Prot [112]. It only showed minimal activity against E. coli ATCC 25922 (MIC = 128 μg/mL). Neither of these two peptides were active against S. aureus ATCC 29213 (MIC > 128 μg/mL). Except MoIn1, MuMu1, and HoSa7 that were not sent for hemolysis assays (Table B.4), all the other 10 novel AMPs with proven antimicrobial activity shown in this work were not hemolytic to the porcine RBCs (HC50 > 128 μg/mL). 73 Figure 3.4 Antimicrobial and hemolytic activities of the 13 novel AMPs mined from the UniProtKB/Swiss-Prot database that were active against at least one bacterial strain of Escherichia coli ATCC 25922 and Staphylococcus aureus ATCC 29213. Antimicrobial and hemolytic activities were measured by minimum inhibitory concentration (MIC) and concentration that lyses 50% (HC50) of the red blood cells (RBCs), respectively. HC50 was determined using porcine RBCs. Data is presented as the lowest effective peptide concentration range (μg/mL) observed in three independent experiments performed in duplicate, with one maximum data point and one minimum data point dropped for each measurement. The results are divided into two sections, as separated by the solid vertical line. The left section shows results of the 11 active peptides that were most structurally similar to known chicken AMPs as measured by TM-scores, while the right section shows results of the two active peptides that were least structurally similar to known chicken AMPs. These 11 active peptides in the left section are categorized into three sub-sections, as separated by the dashed vertical lines, according to their reference chicken AMPs (i.e., the most structurally similar known chicken AMP of each putative AMP). The results of the three reference chicken AMPs: Chicken CATH-2, Chicken CATH-3, and Ovipin (dark grey background), are listed for comparison in each sub-section. We note that hemolysis experiments were not performed for putative AMPs that showed no antimicrobial activity (MIC > 128 μg/mL) in at least two repeats for each bacterial strain tested. 3.6 Discussion and conclusions In this work, we propose the imbalanced model of AMPlify in addition to the balanced model described in Chapter 2. These two models have their own usages depending on the characteristics of the candidate peptide sequence sets for filtering. In cases where the input consists of a large sequence set with the number of non-AMPs far greater than that of AMPs 74 (e.g., a huge comprehensive protein sequence dataset), the imbalanced model would be a better choice, since it may be desirable to reduce the number of false positives to a minimum in order to cut downstream synthesis and analysis costs (the imbalanced model obtains a high specificity of 99.09% on the imbalanced test set). On the other hand, the balanced model is better at filtering through relatively curated datasets (e.g., those annotated through sequence homology search). AMPlify is released as an open source tool (https://github.com/bcgsc/AMPlify) under the GPL-3.0 license, and its models and datasets can be freely accessed at https://doi.org/10.5281/zenodo.7320306 [155]. Based on the AMPlify balanced and imbalanced models, we introduce an AMP mining workflow for novel AMP discovery from protein sequence databases, and apply the workflow to unearth novel eukaryotic AMPs from the UniProtKB/Swiss-Prot database [112]. Besides adopting AMPlify for AMP prediction, the AMP mining workflow also utilizes the rAMPage cleavage module [32] with ProP [142] for precursor sequence cleavage. Using this workflow, we identified 8,008 distinct novel AMP sequences from all eukaryotic sequences in the UniProtKB/Swiss-Prot database. We have made all predicted AMPs in this study publicly available through a Zenodo repository at https://doi.org/10.5281/zenodo.8133088 [156], providing the community with candidate AMP sequences to validate in the lab and to extend the current arsenal of peptide-based therapeutics. While the presented AMP mining workflow successfully identified a considerable number of putative AMPs, it is essential to acknowledge the limitations inherent in the machine learning based tools we employed. Both AMPlify and ProP, although powerful, are not infallible, as 75 neither of them achieves 100% accuracy in their respective tasks. As is common with most machine learning based bioinformatics tools, their performance can be limited by the quality as well as the size of available data used for training. Despite these limitations, the reported performance of AMPlify and ProP [142] still establishes them as state-of-the-art tools, rendering them highly suitable for integration into an AMP mining workflow. We anticipate that the limitations of these tools will gradually diminish as more training data become available and machine learning techniques continue to advance [57]. Here, we focused on AMPs that have potential utility in poultry farming. As the first step towards a field application, we initially tested our selected putative AMPs against E. coli and S. aureus, and found 13 to be active against at least one of the bacterial strains tested. A total of 11 of these active peptides were relatively similar to chicken AMPs in their predicted 3D structures (TM-scores ranging from 0.6934 to 0.8352), 10 of which were from organisms other than chickens. Based on the fact that the biological function of a protein or peptide can be influenced by the 3D structure it adopts [149] and chicken AMPs have evolved to fight the pathogens that infect their hosts [12], these newly discovered AMPs with high structural similarities to chicken AMPs may target similar pathogens as chicken AMPs do. As a result, we foresee the potential usage of these characterized AMPs as possible substitutes for conventional antibiotics to fight against pathogens in chicken farming. Although some of the putative AMPs were not active against the bacterial strains we tested, they may still be active against other pathogens, especially those that infect chickens. Further tests on 76 a broader range of bacterial species could be done to validate their application as novel antibiotics for chickens. While it is hypothesized that AMPs may not induce antibiotic resistance to the extent of conventional antibiotics [43], cases of bacterial cross-resistance to multiple AMPs have been reported [157], raising a concern regarding the usage of these newly discovered AMPs. The similarity between a newly discovered AMP and a chicken AMP in predicted 3D structures suggests a potential similarity in their modes of action [149], indicating a potential risk that a newly discovered AMP may lose its effect if some pathogens have already developed resistance to its reference chicken AMP [158]. However, the extent to which structural similarity implies similarity in modes of action remains untested. Properties other than the peptide structure itself, such as the distributions of the hydrophobic and positively charged residues, can all affect how an AMP works. Also, we note that none of our newly discovered AMPs have a 100% match in predicted 3D structures to the known chicken AMPs. Additionally, the structures of some AMPs may vary according to the surrounding microenvironment [133]. All of these cannot be observed through our current set of tests, and warrant further study. If cross-resistance to our newly discovered AMPs and known chicken AMPs does not exist in pathogens tested, then the novel AMPs with high structural similarities to known chicken AMPs but originating from other organisms may be prioritized as primary candidates for further translation into applications. These AMPs may have different evolutionary background from chicken AMPs, suggesting that many chicken pathogens may not have had enough opportunity to develop resistance against them. In the worst case, if cross-resistance to the newly discovered 77 AMPs and known chicken AMPs does occur in some pathogens, then this work sets up a warning to the scientific community in the use of those AMPs in chicken farming. Although the AMPs discovered in this study were derived from putative precursor sequences from natural organisms, it still requires further investigation whether those AMPs really exist in nature. Due to the limitations of the in silico cleavage tool used, it is likely that some sequences, which may or may not be real precursors, were incorrectly cleaved, even when the resulting fragments displayed antimicrobial properties in vitro. It has been reported that the degradation products of some non-antimicrobial proteins with other biological functions exhibit antimicrobial activities [159], and some of the aforementioned protein fragments may belong to this category of AMPs. Further, we observed instances where the predicted AMPs originated from non-secreted proteins, such as GoGo1, which is a fragment of a transcriptional repressor. While these AMPs may be less likely to occur naturally, we still consider them as valuable discoveries. AMPs that do not exist in nature may possess unique advantages in that they may be “new” to most extant pathogens. Lastly, exploring the relationship between the antimicrobial properties of the novel AMPs and the originally annotated functions of their parent sequences in the database would be a promising direction for future research. This investigation may provide a better understanding of the full life cycle of these proteins or peptides. As the UniProt database frequently updates with more protein sequence data from additional organisms, we expect the AMP mining workflow we present to continue being a valuable discovery and annotation engine. Future work can also be extended to additional peptide sequence databases (e.g., UniProtKB/TrEMBL, the unreviewed section of the UniProt database 78 [112]). Additional work can be done by loosening the criteria in sequence filtering steps for more sequences. For example, sequences without any predicted signal peptides, which were all removed in Step 2 of our workflow (Figure B.1), can be secreted proteins or peptides reported in mature or incomplete precursor forms. Though it is out of the research scope of the current work, such sequences may hold value for further investigation. We expect bioinformatics workflows like the one reported here to continuously discover novel AMPs and to help us in the fight against MDR bacteria. 79 Chapter 4: De novo AMP design 4.1 Publication note A version of this chapter has been submitted to a journal for peer-review. The work describes the RNN based AMP sequence generation tool AMPd-Up and shows its application in de novo AMP design. I am the lead author of this work, under the supervision of Dr. Inanç Birol. I designed and implemented the model, performed peptide selection, analyzed the results, and drafted the manuscript, in collaboration with my colleagues. All in vitro validations were conducted by my wet lab collaborators. 4.2 Author summary Here, we introduce AMPd-Up, an RNN based tool for de novo AMP design, and demonstrate its utility over existing methods. Validation of candidates designed by AMPd-Up through AST revealed that 40 of the 58 generated sequences possessed antimicrobial activity against Escherichia coli and/or Staphylococcus aureus. These results illustrate that AMPd-Up can be used to design novel synthetic AMPs with potent activities. 4.3 Introduction In the presented work, we introduce AMPd-Up, a novel AMP sequence generation tool that implements a standard RNN language model [75]. AMPd-Up samples candidate AMP sequences from multiple model instances, which are trained with different random initializations. For de novo AMP sequence generation, our RNN language model learns the “grammar” – the arrangement of the amino acids – of the training AMP sequences and estimates the probabilities 80 of amino acid occurrence at each position recurrently starting from the N-terminus. The model generates a putative AMP sequence, one residue at a time, based on the estimated probability distribution at each residue position (or each time step of the process). We expect different model instances to capture the complicated underlying features of AMP sequences from slightly different aspects, thus exploring various localities in the state space represented by a rich repertoire of natural AMPs. With this approach, we generated 40 novel AMPs that have not been reported in public databases but were proven to be active against laboratory strains of Escherichia coli and/or Staphylococcus aureus. Our results illustrate the power of AMPd-Up in contributing to our expanding arsenal of synthetic antimicrobial agents. 4.4 Methods 4.4.1 Training set To get our RNN language model well trained, a curated set of known AMP sequences are required to comprise the training set. Our work primarily focused on AMPs with direct antibacterial activity, a major function of most known AMPs. We also limited the generated AMP sequences to include only standard amino acids with a maximum length of 50 aa, reflecting the fact that most documented AMPs are relatively short [12,143]. All antibacterial peptide sequences were downloaded from the APD3 database [17] on March 20, 2019. This set of sequences contained 2,571 AMP records with antibacterial activity, 2,276 of which were ≤ 50 aa long. After removing duplicates and sequences with non-standard amino acids, we ended up with a non-redundant set of 2,253 antibacterial sequences ≤ 50 aa in length, forming the training set for our RNN language model. 81 4.4.2 Model architecture and implementation The implementation of the RNN language model was adapted from the PyTorch online tutorial by Sean Robertson [160], with PyTorch library 1.7.1 [161] in Python 3.6.7. During the training process, cross-entropy was used as the loss function, and stochastic gradient descent [162] was applied to optimize the model weights. We also adopted dropout technique [117] to prevent overfitting. The hyperparameters, which cannot be learned directly from training, were tuned through stratified 5-fold cross-validation on the training set. The set of hyperparameters for model architecture and training settings with the lowest average cross-validation loss was determined to be the optimal one to train the final model. Figure 4.1 shows the architecture of the RNN language model, represented as a chain of repeating RNN cells. Given the first N-terminal amino acid, the RNN language model generates a peptide sequence residue by residue until reaching the end-of-sequence (EOS) signal. In this specific task of AMP sequence generation, we set the maximum length to be 50 and only the 20 standard amino acids are considered. Amino acids, together with the EOS signal, are encoded as twenty-one distinct one-hot vectors, with !! ∈ ℝ"# representing the $-th residue of a generated sequence. In this task, a time step $ is defined as the process of an RNN cell predicting the ($ +1)-th residue !!$# of a sequence. At each time step $ of the generation process, the RNN cell takes the hidden state )!%# from the previous time step and the predicted amino acid for the $-th residue !! as input, and outputs a set of probabilities *! of amino acid and EOS occurrence at the next position, from which !!$# can be sampled. The hidden state )! ∈ ℝ&! and probability vector *! ∈ ℝ"# at each time step are calculated as: 82 )! = ,' - !!)!%#. + /' *! = softmax(,( 7 )!,) - !!)!%#. + /)8 + /() where ,' ∈ ℝ&!×(&!$"#), ,) ∈ ℝ"#×(&!$"#), and ,( ∈ ℝ"#×(&!$"#) are weight matrices, and /' ∈ ℝ&!, /) ∈ ℝ"#, and /( ∈ ℝ"# are bias vectors. Here, -9#9". denotes the concatenation of two vectors 9# and 9", and the softmax function ensures that the probabilities sum up to 1. The initial hidden state )- is set to be a zero vector. We determined the optimum value of :' to be 128. A dropout rate of 0.1 was applied before the softmax function during training, and the training process was conducted with 100,000 iterations and a learning rate of 0.0005. Figure 4.1 Architecture of the recurrent neural network language model in AMPd-Up. Given a starting amino acid, the recurrent neural network (RNN) language model predicts the next amino acids residue by residue until reaching the end-of-sequence (EOS) signal. Amino acids, including the EOS signal, are one-hot encoded. The output of RNN at each time step is a probability vector of amino acid and EOS occurrence at the next position, to which sampling strategies can be applied. RNN RNN RNN RNN………………SamplingOne-hot encodingRNNL P I CF L P K CF 83 Predictions can be made by sampling from the output probabilities of the RNN cells. The sequence generation process stops if an EOS signal is predicted or if the maximum length is reached without EOS signal predicted. Sequences generated in the former case are annotated as “complete”, while those in the latter case as “incomplete”. We define the “AMPd-Up score” as the geometric mean of probabilities of all predicted symbols in a sequence, including the EOS signal if the sequence is complete. We use this score as a measure of confidence of the RNN language model in generating a sequence. In AMPd-Up, the model is trained multiple times with different random initializations, yielding multiple model instances. Given one of the 20 possible starting amino acids, the symbol with the highest probability estimated at each time step is taken as the next amino acid prediction (including the EOS signal), resulting in a maximum of 20 candidate AMP sequences generated by a single model instance. In a practical use case, the model will be trained ; times and the users would get a candidate AMP list of up to 20; sequences. Assuming we have a non-convex loss function like most neural network based tasks, different initializations may result in different trained models [163], allowing different model instances of AMPd-Up to capture slightly different aspects of the complex but unknown features of AMPs. 4.4.3 Model evaluation In order to measure the performance of AMPd-Up in an efficient and cost-effective way, we used the predictions from three state-of-the-art in silico AMP prediction tools: AMPlify, AMP Scanner Vr.2 [92], and iAMPpred [84], as a proxy for AMP sequence generation accuracy. These AMP prediction tools determine whether an input peptide sequence is an AMP or not. 84 Here, the AMP sequence generation accuracy measured by a selected prediction tool was calculated based on the percentage of peptide sequences predicted as AMPs among a generated sequence set. A default setting of balanced model was chosen for AMPlify in its version 1.1, while the “Orig. Production” model was chosen for AMP Scanner Vr.2 on its online server [92]. Predictions by iAMPpred were obtained through its online server with its trained model as described in the publication [84]. We compared AMPd-Up with three other AMP sequence generation methods with publicly available models or generated sequences: the LSTM language model [96], AMPGAN v2 [102], and HydrAMP [99]. For each method, a total of 2,000 sequences were generated for comparison in five batches. This resulted in five generated sequence sets of 400 sequences for each method. Sequences for the LSTM language model were sampled from the dataset the authors provided [96], while those for HydrAMP were obtained through their online server on November 7, 2022 [99]. While all other methods focus on the generation of antibacterial peptides, AMPGAN v2 additionally allows for generating AMP sequences of other function types (e.g., antifungal, antiviral) and the generated sequences are annotated with their predicted functions in the results [102]. For a fairer comparison, only AMPs targeting bacteria were selected for AMPGAN v2. For each AMP sequence generation method measured by each AMP prediction tool, the average accuracy value of the five generated sets was reported, along with the corresponding standard deviation value. In addition to the sequence generation accuracy, we evaluated the quality of sequences generated by AMPd-Up based on their physicochemical properties as well as their sequence similarities to 85 the training set and all publicly available known AMPs. The same 2,000 sequences generated by AMPd-Up for performance comparison were used in these analyses. The properties that cause a peptide sequence to have antimicrobial activity are complex and the mechanisms are still not well understood [164]. Considering the fact that most known AMPs share common characteristics of short lengths and net positive charges [12], we focused on these two important and easy-to-calculate physicochemical properties. Moreover, sequence similarities of the generated sequences to the training set were calculated to evaluate whether the model instances capture high-level features of AMPs rather than only generating the same or highly similar sequences to the training set. A similar comparison between the generated sequences and all publicly available known AMPs was done to evaluate the novelty of the generated sequences compared with those known AMP sequences. We note that the training AMPs are antibacterial, while the known AMP sequence set additionally includes those targeting microbes other than bacteria. The known AMP sequence set comprises 4,538 distinct sequences downloaded from APD3 [17] and DADP [110] as described in Chapter 3, Section 3.4.1. Calculations of the similarity between two sequences and the similarity of a sequence to a set of sequences were the same as the methods described in Chapter 2, Section 2.4.4. 4.4.4 Selecting putative AMPs for validation To demonstrate the utility of our tool, we trained the model 1,000 times, yielding 1,000 model instances and 20,000 sequences, 14,188 of which were complete, and 8,737 of the complete 86 sequences were distinct. We define the “count” of a sequence as the number of times it appears in the entire generated set. We further filtered for short sequences with lengths ≤ 35 aa and obtained 7,434 peptide sequences, since shorter peptides are more cost-effective for synthesis [32]. We selected 58 of these peptides using different strategies (forming Lists A, B, and C), and validated their bioactivity through in vitro experiments (Table C.1). The peptides comprising Lists A and B were chosen following a strategy that stratifies the AMP-Up score range of 7,434 sequences into same-length score intervals. For > intervals, each interval can be written as a range from ? + (.%#)(/%0)1 to ? + .(/%0)1 , with ; = 1,2, … , > and ?, B being the minimum and maximum AMPd-Up scores investigated in the generated sequence set. In our case, ? = 0.1462 and B = 0.3579. All intervals are of left-open and right-closed, except the first one (; = 1) that is closed. If multiple model instances generated the same sequence, the AMPd-Up score from the first model that generated this sequence was used for stratification. Peptides for List A were sampled by splitting the AMPd-Up score range of [0.1462, 0.3579] into 40 intervals, and the sequence with top AMPd-Up score within each interval was chosen. List B peptides were chosen by splitting the same AMPd-Up score range into 5 intervals, and then selecting one predicted non-AMP (as assessed by the AMPlify) in each interval with the highest count, or with top AMPd-Up score if all sequences have the same count in the interval. We note that some intervals did not have any sequences, resulting in 38 sequences in List A and 4 sequences in List B. Additionally, 16 more peptide sequences that appeared with a high frequency (≥ 40 in sequence counts) in the generated set were selected as List C. All sequences in Lists A and C were predicted as AMPs by AMPlify. In Table C.1, we also present the 87 sequence similarity of each sequence to the known AMPs, showing the novelty of those sequences compared with the known AMP sequences. We note that the balanced model was chosen for AMPlify during the peptide selection process. 4.4.5 Antimicrobial susceptibility testing The procedures are the same as what have been described in Chapter 3, Section 3.4.6. 4.4.6 Hemolysis assay The procedures are the same as what have been described in Chapter 3, Section 3.4.7. 4.5 Results 4.5.1 Performance comparison with state-of-the-art methods We measured the performance of AMPd-Up by assessing the generated sequences using three state-of-the-art AMP prediction tools: AMPlify, AMP Scanner Vr.2 [92], and iAMPpred [84]. The sequence generation accuracy values, expressed as the percentages of sequences predicted as AMPs evaluated by each AMP prediction tool, are reported in Table 4.1 with the results of three other AMP sequence generation methods listed for comparison. Although none of the in silico prediction tools are perfect in identifying AMPs, their reported performance [84,92] would be suitable for evaluating the AMP sequence generation methods. Details of how we calculated the accuracy values can be found in Chapter 4, Section 4.4.3. As measured by AMPlify, AMPd-Up obtains the highest accuracy with 95.50% of the generated sequences predicted as AMPs on average, which outperforms the best comparator AMPGAN v2 88 by 4.60%, followed by HydrAMP (by 8.00%) and then the LSTM language model (by 10.65%). When evaluating using AMP Scanner Vr.2 and iAMPpred, AMPd-Up generates AMP sequences with accuracies of 100.00% and 99.30%, surpassing the best comparator HydrAMP by 5.40% and 1.60%, respectively. Although the rankings of the AMP sequence generation methods evaluated by the three AMP prediction tools are slightly different from each other, AMPd-Up always performs the best compared with its comparators. Table 4.1 Performance comparison of different AMP sequence generation methods. Different methods were evaluated using three in silico AMP prediction tools: AMPlify, AMP Scanner Vr.2 [92], and iAMPpred [84], based on sequences generated by each of the methods. The AMP sequence generation accuracy measured by a selected prediction tool was defined as the percentage of peptide sequences predicted as AMPs among a generated sequence set. For each sequence generation method, five sets of sequences were generated, with 400 in each set. For each AMP sequence generation method, an average accuracy value of the five generated sets was reported when measured by a specific AMP prediction tool, along with the corresponding standard deviation value. AMP sequence generation method Average accuracy evaluated by AMP prediction tools (%) By AMPlify By AMP Scanner Vr.2 By iAMPpred AMPd-Up 95.50 ± 0.35 100.00 ± 0.00 99.30 ± 0.37 LSTMa 84.85 ± 0.75 84.20 ± 1.04 82.80 ± 0.97 AMPGAN v2b 90.90 ± 2.10 87.55 ± 1.29 94.85 ± 1.29 HydrAMPc 87.50 ± 1.15 94.60 ± 0.46 97.70 ± 0.64 a Sequences sampled from the generated sequence set provided by the authors [96]. b Antibacterial peptides were selected for a fairer comparison with other methods [102]. c Sequences generated using online server on November 7, 2022 [99]. 4.5.2 Quality analyses of de novo generated sequences Besides using the outputs of in silico AMP prediction tools as a proxy for performance, we also analyzed the length and net charge distributions of the generated sequences, as well as their sequence similarity levels to the training set and all known AMP sequences. Short lengths and net positive charges are common characteristics for most previously discovered AMPs [12]. Therefore, many AMP studies investigate these key properties [165]. 89 Shorter peptides are also cheaper to synthesize [32], potentially making them a more economical choice for clinical applications. Further, the net positive charges of cationic AMPs are responsible for the electrostatic interaction with the negatively charged bacterial membranes or cell walls [12], with studies illustrating that the antimicrobial activity of some AMPs can be improved by increasing their net charges [166]. The top section of Figure 4.2 compares the length distributions of the generated sequences with those constituting the training set. We note that the model may fail to reach the EOS signals when generating some sequences (referred to as incomplete sequences), we thus additionally compared the generated sequence set with those incomplete sequences removed. The average generated sequence length was 28.90 aa, but was reduced to 21.56 aa after incomplete sequences were removed. The incomplete sequences are 50 aa by default. The complete sequences were 4.65 aa shorter than the training sequences on average. The bottom section of Figure 4.2 shows a similar comparison for net charge distributions. The average generated sequence net charge was 9.08, but was reduced to 6.45 after incomplete sequence removal. However, the net charge of the complete sequences was still 3.15 greater than the training sequences on average. 90 Figure 4.2 Length and net charge distributions of the sequences generated by AMPd-Up. Length and net charge distributions were calculated based on 2,000 sequences generated by AMPd-Up, along with training sequences for comparison. 1,484 of the 2,000 generated sequences in “complete” status were chosen for an additional comparison. Mean (μ) and standard deviation (σ) of each distribution are as follows: training sequences (length: μ = 26.21 aa, σ = 10.34 aa; net charge: μ= 3.30, σ = 2.74), all generated sequences (length: μ = 28.90 aa, σ = 15.07 aa; net charge: μ = 9.08, σ = 7.33), and complete generated sequences (length: μ = 21.56 aa, σ = 9.87 aa; net charge: μ = 6.45, σ = 4.73). The sequence similarity of each generated sequence to the training set, composed of antibacterial peptides, was calculated for analysis (details in the Chapter 4, Section 4.4.3). Figure 4.3 shows the sequence similarity distribution of the generated sequences to the training set, with a peak between 50.00% and 55.00%. The generated sequences possess a similarity level of 49.97% compared with the training sequences on average, indicating that our sequence generation method generates novel AMP sequences less similar to the training sequences. This implies that 91 AMPd-Up may be capturing high-level features of AMPs, rather than only memorizing sequence-level information during training. An additional test on the sequence similarity of each generated sequence to all available known AMPs was done, with an average sequence similarity level of 51.03%, indicating the novelty of our generated sequences as compared with known AMPs (Figure C.1). To supplement the sequence similarity analysis, we also visualized the pairwise sequence similarities between different sequence sets (Figure C.2). A lower generated sequence similarity level between different model instances (33.56%) than within the same model instance (39.14%) indicates that different model instances tend to capture features of AMPs from slightly different aspects. We expect the novelty of generated sequences by our tool to add diversity to the current AMP sequence databases. Figure 4.3 Sequence similarity distribution of the de novo generated sequences to the AMPd-Up training set. The sequence similarity distribution, with a mean of 49.97% and a standard deviation of 9.83%, was calculated based on the 2,000 sequences generated by AMPd-Up. The sequence similarity of each generated sequence to the AMPd-Up training set was considered as the similarity of that sequence to its most similar sequence in the training set, based on which the distribution was plotted. 92 4.5.3 In vitro validation results We selected 58 peptide sequences generated by 1,000 AMPd-Up model instances for in vitro validation and bioactivity assessment. We organized our candidates into three lists: List A (DeNo1001 to DeNo1038) and List B (DeNo1039 to DeNo1042) were sampled through AMPd-Up scores as described in Chapter 4, Section 4.4.4, and a few more sequences that appear with high frequency (≥ 40 in sequence counts) in the generated set were selected to make List C (DeNo1043 to DeNo1058). Table C.1 summarizes the sequence specifications of our 58 selected putative AMPs. All sequences in Lists A and C were predicted as AMPs by AMPlify using its balanced model, while all sequences in List B were predicted as non-AMPs. Our 58 candidate peptides were tested against two bacterial isolates: the Gram-negative E. coli ATCC 25922 and the Gram-positive S. aureus ATCC 29213. Porcine RBCs were used to assess the hemolytic activity of the peptides. Out of the 58 peptides selected for in vitro validation, 40 peptides displayed antimicrobial activity against at least one bacterial strain tested. All 15 peptides that were active against S. aureus ATCC 29213 also showed antimicrobial activity against E. coli ATCC 25922. Figure 4.4a visualizes the antimicrobial and hemolytic activities (in MIC and HC50, respectively) of the 40 peptides, with the entire in vitro validation results of the 58 peptides shown in Table C.2. For a better interpretation of the results, we split the activity of the tested peptides into four levels according to the MIC/HC50 ranges: high (≤ 4 μg/mL), moderate (8–16 μg/mL), low (32–128 μg/mL), and without observable activity (> 128 μg/mL). 93 Among the 38 List A peptides tested, 28 peptides displayed antimicrobial activity, 12 of which were active against both strains tested (Figure 4.4a). Nine of the List A peptides were highly active against E. coli ATCC 25922, with DeNo1018 being the most active with an MIC of 1–2 μg/mL. These same nine peptides were also active against S. aureus ATCC 29213. Four of the nine peptides were highly active against S. aureus ATCC 29213 (MIC = 2–4 μg/mL for DeNo1016 and DeNo1017; MIC = 4 μg/mL for DeNo1007 and DeNo1022), with one (DeNo1018) moderately active (MIC = 8 μg/mL). Six peptides from List A were moderately active against E. coli ATCC 25922, and another two showed low to moderate activity against the strain. Three of these eight peptides displayed some antimicrobial activity against S. aureus ATCC 29213, one of which (DeNo1031) was moderately active (MIC = 16 μg/mL) with the other two (DeNo1021 and DeNo1026) showed low (MIC = 32–64 μg/mL) and minimal activity (MIC ≥ 128 μg/mL), respectively. Among all 28 List A peptides with proven antimicrobial activity, three were minimally hemolytic (HC50 ≥ 128 μg/mL) and 17 showed no hemolytic activity (HC50 > 128 μg/mL) in our tests. DeNo1007 was the only AMP with high antimicrobial activity against both bacterial strains tested (MIC = 4 μg/mL) and without observable hemolytic activity (HC50 > 128 μg/mL). Among the four peptides from List B tested, only DeNo1040 displayed some low-level activity against the two bacterial strains tested (Figure 4.4a). Specifically, this peptide inhibited the growth of E. coli ATCC 25922 and S. aureus ATCC 29213 providing MICs of 64–128 μg/mL and 64 μg/mL, respectively. DeNo1040 also did not show any hemolytic activity in our tests (HC50 > 128 μg/mL). We note again that peptides in List B were predicted as non-AMPs by AMPlify. 94 Among the 16 List C peptides tested, a total of 11 peptides showed antimicrobial activity against E. coli ATCC 25922, with two of them additionally active against S. aureus ATCC 29213 (Figure 4.4a). DeNo1049 displayed moderate to high activity against E. coli ATCC 25922 (MIC = 4–8 μg/mL), which was the strongest in List C. DeNo1057 was moderately antibacterial against E. coli ATCC 25922 (MIC = 8–16 μg/mL), followed by DeNo1051 (MIC = 16–32 μg/mL) and DeNo1046 (MIC = 16–64 μg/mL). DeNo1057 and DeNo1046 were the only two List C peptides with antibacterial activity against S. aureus ATCC 29213, though with low activity (MIC = 32 μg/mL and 128 μg/mL, respectively). None of the List C peptides displayed hemolytic activity (HC50 > 128 μg/mL). Among the peptides that did not show any antimicrobial activity against the bacterial strains tested, most of them were also not hemolytic to the porcine RBCs except DeNo1008 (HC50 = 16–32 μg/mL) and DeNo1039 (HC50 = 32–64 μg/mL) as shown in Table C.2. Figure 4.4b presents the proportions of peptides that were active against at least one of the bacterial strains tested under different sequence similarity levels to the known AMPs from APD3 [17] and DADP [110]. All six peptides between sequence similarities of 70.00% and 90.00% to known AMPs showed antimicrobial activity in our tests. The largest proportion of the tested peptides fall between sequence similarities of 60.00% and 70.00% to known AMPs, with nine out of 20 sequences displaying antimicrobial activity. Interestingly, lower similarity intervals of 50.00%–60.00% and 40.00%–50.00% possess relatively high proportions of antimicrobially active peptides with rates of 75.00% (12/16) and 81.25% (13/16), respectively. More than half (62.50%) of the peptides with antimicrobial activity from our experiments fall into these 95 intervals, implying there is much to be explored in the sequence space for novel AMPs. Figure 4.4c visualizes the distribution of the 58 tested putative AMPs with regard to AMPlify scores (from the balanced model) and AMPd-Up scores. AMPlify score, ranging from 0 to 80, is a prediction score reported by AMPlify, as described in Chapter 2, Section 2.4.2. AMPd-Up score, reported by AMPd-Up, ranges from 0 to 1 and is a measure of the confidence level of the model when generating the sequence. Considering the fact that multiple model instances may generate the same sequence but with different AMPd-Up scores, the average was taken in the visualization for a more comprehensive analysis. As can been seen from Figure 4.4c, most of the peptides that did not show any antimicrobial activity in our tests are located at the bottom left of the figure, suggesting that it is ideal to prioritize generative sequences with both high AMPlify and AMPd-Up scores for in vitro validation assays. 96 Figure 4.4 In vitro validation results of the 58 selected de novo designed putative AMPs. (a) Antimicrobial and hemolytic activities of the 40 peptides that were active against at least one bacterial strain of Escherichia coli ATCC 25922 and Staphylococcus aureus ATCC 29213. Antimicrobial and hemolytic activities were measured by minimum inhibitory concentration (MIC) and concentration that lyses 50% (HC50) of the red blood cells (RBCs), respectively. HC50 was determined using porcine RBCs. Data is presented as the lowest effective peptide concentration range (μg/mL) observed in three independent experiments performed in duplicate, with one maximum data point and one minimum data point dropped for each measurement. The three sections from left to right correspond to peptides with observable antimicrobial activity from List A (n = 28), List B (n = 1), and List C (n = 11), respectively. Activity of the peptides was split into four levels: high (≤ 4 μg/mL), moderate (8–16 μg/mL), low (32–128 μg/mL), and without observable activity (> 128 μg/mL), as separated by different background colors in the plot. (b) Stacked bar chart showing proportions of peptides that displayed antimicrobial activity with different sequence similarity levels to known AMPs from Antimicrobial Peptide Database (APD3) [17] and Database of Anuran Defense Peptides (DADP) [110]. All similarity ranges are of left-open and right-closed, and the sequence similarity of each candidate peptide to known AMPs was considered as the sequence similarity of that sequence to its most similar known AMP sequence. (c) Visualization of antimicrobial activity of the 58 tested peptides with b ca 97 respect to AMPlify (x-axis) and AMPd-Up (y-axis) scores. Here, the AMPlify balanced model was utilized for AMP prediction. AMPd-Up scores of the same peptide sequences generated by multiple model instances were averaged. Peptides without any observable antimicrobial activity are presented as grey crosses, and the active peptides are presented as blue dots. Dots with darker colors indicate stronger antimicrobial activity against Escherichia coli ATCC 25922, determined by the lowest MIC value of each peptide against the strain. 4.6 Discussion and conclusions In the presented work, we introduce AMPd-Up, a tool for de novo AMP sequence generation. AMPd-Up adopts an RNN language model, sampling from multiple model instances trained with different random initializations. AMPd-Up is released as an open source tool (https://github.com/bcgsc/AMPd-Up) under the GPL-3.0 license. The trained models applied to generate the sequences for validation can be accessed at https://doi.org/10.5281/zenodo.7905591 [167]. Although the architecture of our model is relatively simple compared with existing methods, we showed that simple models like AMPd-Up can work well when properly trained, as illustrated. Moreover, the sequences generated by AMPd-Up are of high novelty compared with existing AMP sequences in public databases, demonstrating the ability of our model to learn high-level AMP features. While AMPd-Up shows great promise and favorable performance, the size of its training set is still relatively small (2,253 sequences) compared with that of many traditional machine learning tasks for broader sequence data analysis, such as sentiment analysis or machine translation, which both typically enjoy hundreds of thousands to millions of data points for training available through public databases [168]. This is a critical problem that all current in silico AMP prediction and generation tools are facing. We expect this limitation to be gradually 98 resolved as more AMPs are being discovered and validated, leading to further improvement in de novo AMP sequence generation tools like AMPd-Up. Although the AMPd-Up-generated putative AMPs have a considerable level of sequence diversity (Figure C.2), we still noticed some patterns at the sequence level. Analyzing a set of 20,000 generated sequences, we observed that “LLKK” and “LKKL” were the two most frequently occurring 4-mer motifs, appearing in 44.09% and 40.73% of the generated sequences, respectively. Previous studies have shown that synthetic amphipathic α-helical peptides made up of repeat units [LLKK]n or [LKKL]n have antimicrobial properties [169,170], which explain these findings to some extent. In fact, it is suggested that repeats of 4-mer units such as these are responsible for the formation of cationic amphipathic α-helical structures, a key initiating step to the bioactivity and membrane-disrupting properties of many AMPs [169,170]. Among the 58 novel putative AMP sequences generated by AMPd-Up, 40 showed antimicrobial activity, 15 of which were broadly antibacterial against both the Gram-positive and Gram-negative isolates. Promisingly, one of the most active peptides, DeNo1007, not only possessed high antimicrobial activity against the two bacterial strains tested, but was also without observable hemolytic activity. We expect the AMP candidates generated by AMPd-Up to increase the diversity of known peptide-derived antibiotics, currently populated by mostly naturally-occurring sequences, and to augment the candidate set of potential alternatives to conventional antibiotics. Although some of our putative AMPs did not show any antimicrobial activity against the two bacterial strains tested in vitro, they may still be active against other bacterial species and/or possess some immunomodulatory functions. Also, the structures of some 99 AMPs may vary based on their microenvironment [133]. Further experimentation could be done to test candidate sequences on a wider panel of bacterial species, or to interrogate in vivo biological interactions. Results from work like ours also have broader potential impact. Resistance to last-line peptide-based therapeutics, such as colistin and other polymyxins, is increasingly being reported [44]. Concerningly, this is sometimes presented with cross-resistance to multiple AMPs [157], highlighting the need for multiple and diverse classes of peptide-based antimicrobials. De novo AMP sequence generation allows for a rational solution to this problem, as one would theoretically expect that pathogens would be naïve to many of the diverse de novo generated AMPs. Even though there may be natural AMPs similar to some of the de novo generated ones, the vast sequence space of amino acids (e.g., 1020 or one hundred quintillion for a 10-residue peptide sequence) virtually ensures that there would be a practically infinite number of them out there that are “new” to most common pathogens. Thus, we expect high-throughput in silico AMP sequence design tools like AMPd-Up to play a vital role in the fight against antibiotic resistance and the imminent rise of antibiotic-resistant bacteria. 100 Chapter 5: Conclusions and future works 5.1 Conclusions In this thesis, I present two machine learning based computational tools, AMPlify and AMPd-Up, for high-throughput AMP discovery and design, respectively. Chapter 2 and Chapter 3 address the first research objective of this thesis by describing AMPlify and its applications in AMP discovery, while Chapter 4 addresses the second research objective by describing AMPd-Up and its application in de novo AMP design. With these tools, a total number of 57 novel AMPs were unearthed with proven antimicrobial activity through in vitro validations. AMPlify enables fast and accurate in silico scanning through peptide sequence sets for novel AMPs by adopting a deep neural network with Bi-LSTM and attention mechanisms. AMPlify outperforms other state-of-the-art AMP prediction tools, and provides two model options to fit different application scenarios: 1) the balanced model for filtering through relatively curated candidate sequence sets (e.g., mature peptide sequences derived from candidate AMP precursors identified through homology-based methods); 2) the imbalanced model for larger-scale filtering where the candidate sequence sets are typically less curated with AMPs constituting only a minor portion (e.g., a huge comprehensive protein sequence dataset). AMPlify’s success in real-world applications is demonstrated by its use in two AMP discovery tasks. When analyzing the bullfrog genome, the balanced model of AMPlify successfully aided a homology-based approach and identified four novel AMPs. When investigating the UniProtKB/Swiss-Prot database, the balanced and imbalanced models of AMPlify were utilized as an integration, and 13 novel AMPs that have never been reported or annotated with antimicrobial properties in the database were 101 revealed. These results highlight the power of tools like AMPlify in high-throughput AMP discovery. On the other hand, AMPd-Up allows for efficient de novo AMP sequence generation with an RNN language model. AMPd-Up shows advantages in generating AMP sequences over the existing methods through in silico evaluations, and have successfully designed 40 novel synthetic AMPs with proven antimicrobial activity in vitro. Interestingly, the majority of these novel synthetic AMPs possess low sequence similarities to known AMPs, suggesting the feasibility of AMPd-Up in increasing both the size and the diversity of our current candidate set of peptide-based therapeutics. Compared with natural AMPs, these diverse synthetic AMPs may have their own advantages. As many of these synthetic AMPs may not exist in nature, one can assume that the majority of microbes may not readily have a means to develop resistance against them, providing additional alternatives to conventional antibiotics as well as to natural AMPs. These results demonstrate the potential utility of tools like AMPd-Up in de novo AMP design. In summary, the success of AMPlify and AMPd-Up in AMP discovery and design illustrates the powerful capability of machine learning in biological sequence analysis and drug discovery. In addition to the potential future clinical applications of the 57 novel AMPs presented in this thesis, I also expect the continued use of such tools to reveal more and more AMP candidates in our fight against the persisting problem of antibiotic resistance. 102 5.2 Future works While both AMPlify and AMPd-Up have achieved remarkable success in their respective use cases, I still noticed several follow-up works that can be done to extend or improve upon the current study. First of all, the performance of these computational tools may still have further room for improvement regarding the quality of their training sets. Compared with most other popular tasks using neural networks such as those in computer vision and natural language processing, the sizes of our training sets are still relatively small due to the limited number of lab-validated AMPs available. AMPlify and AMPd-Up models can be further improved by re-training with larger datasets. Additionally, for AMP prediction tools like AMPlify, introducing more “difficult samples” (i.e., samples that are difficult for the machine learning model to make correct predictions) to the training sets appears to be another valuable way to improve the model performance. As discussed in Chapter 2, Section 2.6, the field currently lacks a considerable number of similar peptide sequences with different bioactivities (i.e., non-AMPs that are similar to known AMPs in sequences) for training, which makes the majority of state-of-the-art AMP prediction tools less sensitive to the impact of small changes in a peptide sequence. However, I foresee the feasibility of the tool improvement in the near future, as more and more AMPs are being discovered and designed with high-throughput methods like AMPlify and AMPd-Up. Additionally, investigating the potential biological inference from the machine learning models employed in these computational tools would be a promising avenue for future research. For instance, the weight vectors produced by the attention mechanisms present machine-learned 103 patterns to people in an intuitive way. However, the extent to which these weight vectors have biological meanings requires further investigation. On the other hand, these machine-learned patterns may also assist researchers in studying the mechanisms of AMPs, as the machine learning models may uncover some crucial patterns that have never been recognized by humans. Moreover, future research could explore the joint in silico prediction of peptide toxicity and antimicrobial activity strengths. While efficacy and safety are always two important factors to consider in clinical applications, one would expect an ideal AMP candidate to be of high antimicrobial activity but low toxicity to host cells. High-throughput in silico prediction of the peptide toxicity and antimicrobial activity strengths would provide a more efficient way to filter for more reliable AMP candidates, and could also serve as a guide for AMP enhancement by modifying known AMPs into better therapeutic candidates. Lastly, the validation experiments of the AMP candidates can be further extended to get deeper understandings of their activities. The in vitro validation works discussed in this thesis only focused on selected bacterial strains, while there can be many other pathogenic microbes out there to be investigated. Testing our AMP candidates on a broader microbial panel would make the study more comprehensive. Other than that, the activities and mechanisms observed through our current set of experiments are limited. For example, the structures of some AMPs are susceptible to their the surrounding microenvironment [133] and some AMPs also modulate the host immune system [12], which both require further experiments. Certainly, there is much more to be done before an AMP candidate can be used clinically. I expect some of our discovered or 104 designed AMPs to shine their light by saving more lives from MDR bacterial infections in the not-so-distant future. 105 Bibliography 1. Hutchings MI, Truman AW, Wilkinson B. Antibiotics: past, present and future. Curr. Opin. Microbiol. 2019;51:72–80. 2. Reardon S. Antibiotic resistance sweeping developing world. Nature 2014;509:141–2. 3. Laxminarayan R, Duse A, Wattal C, Zaidi AKM, Wertheim HFL, Sumpradit N, et al. Antibiotic resistance—the need for global solutions. Lancet Infect. Dis. 2013;13:1057–98. 4. Antimicrobial Resistance Collaborators. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet 2022;399:629–55. 5. O’Neill J. Antimicrobial Resistance: Tackling a crisis for the health and wealth of nations. The Review on Antimicrobial Resistance. [Internet]. 2014 [cited 2022 Oct 31];Available from: https://amr-review.org/sites/default/files/AMR%20Review%20Paper%20-%20Tackling%20a%20crisis%20for%20the%20health%20and%20wealth%20of%20nations_1.pdf 6. Gillings MR, Paulsen IT, Tetu SG. Genomics and the evolution of antibiotic resistance. Ann. N. Y. Acad. Sci. 2017;1388:92–107. 7. Levy SB. Balancing the drug-resistance equation. Trends Microbiol. 1994;2:341–2. 8. Magiorakos AP, Srinivasan A, Carey RB, Carmeli Y, Falagas ME, Giske CG, et al. Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Clin. Microbiol. Infect. 2012;18:268–81. 9. World Health Organization. WHO publishes list of bacteria for which new antibiotics are urgently needed [Internet]. 2017 [cited 2017 Sep 22];Available from: 106 https://www.who.int/en/news-room/detail/27-02-2017-who-publishes-list-of-bacteria-for-which-new-antibiotics-are-urgently-needed 10. Koo HB, Seo J. Antimicrobial peptides under clinical investigation. Pept. Sci. 2019;111:e24122. 11. Terreni M, Taccani M, Pregnolato M. New Antibiotics for Multidrug-Resistant Bacterial Strains: Latest Research Developments and Future Perspectives. Molecules 2021;26:2671. 12. Zhang LJ, Gallo RL. Antimicrobial peptides. Curr. Biol. 2016;26:R14–9. 13. Kundu R. Cationic Amphiphilic Peptides: Synthetic Antimicrobial Agents Inspired by Nature. ChemMedChem 2020;15:1887–96. 14. Brandenburg K, Heinbockel L, Correa W, Lohner K. Peptides with dual mode of action: Killing bacteria and preventing endotoxin-induced sepsis. Biochim. Biophys. Acta - Biomembr. 2016;1858:971–9. 15. De Lucca AJ, Walsh TJ. Antifungal Peptides: Novel Therapeutic Compounds against Emerging Pathogens. Antimicrob. Agents Chemother. 1999;43:1–11. 16. Klotman ME, Chang TL. Defensins in innate antiviral immunity. Nat. Rev. Immunol. 2006;6:447–56. 17. Wang G, Li X, Wang Z. APD3: the antimicrobial peptide database as a tool for research and education. Nucleic Acids Res. 2016;44:D1087–93. 18. Koehbach J, Craik DJ. The Vast Structural Diversity of Antimicrobial Peptides. Trends Pharmacol. Sci. 2019;40:517–28. 19. Dürr UHN, Sudheendra US, Ramamoorthy A. LL-37, the only human member of the cathelicidin family of antimicrobial peptides. Biochim. Biophys. Acta - Biomembr. 2006;1758:1408–25. 107 20. Conlon JM, Mechkarska M, King JD. Host-defense peptides in skin secretions of African clawed frogs (Xenopodinae, Pipidae). Gen. Comp. Endocrinol. 2012;176:513–8. 21. McManus AM, Dawson NF, Wade JD, Carrington LE, Winzor DJ, Craik DJ. Three-Dimensional Structure of RK-1: A Novel α-Defensin Peptide. Biochemistry 2000;39:15757–64. 22. Lamberty M, Caille A, Landon C, Tassin-Moindrot S, Hetru C, Bulet P, et al. Solution Structures of the Antifungal Heliomicin and a Selected Variant with both Antibacterial and Antifungal Activities. Biochemistry 2001;40:11995–2003. 23. Selsted ME, Novotny MJ, Morris WL, Tang YQ, Smith W, Cullor JS. Indolicidin, a novel bactericidal tridecapeptide amide from neutrophils. J. Biol. Chem. 1992;267:4292–5. 24. Rosengren KJ, Clark RJ, Daly NL, Göransson U, Jones A, Craik DJ. Microcin J25 Has a Threaded Sidechain-to-Backbone Ring Structure and Not a Head-to-Tail Cyclized Backbone. J. Am. Chem. Soc. 2003;125:12464–74. 25. Varga JFA, Bui-Marinos MP, Katzenback BA. Frog Skin Innate Immune Defences: Sensing and Surviving Pathogens. Front. Immunol. 2019;9. 26. Helbing CC, Hammond SA, Jackman SH, Houston S, Warren RL, Cameron CE, et al. Antimicrobial peptides from Rana [Lithobates] catesbeiana: Gene structure and bioinformatic identification of novel forms from tadpoles. Sci. Rep. 2019;9:1529. 27. Schadich E, Cole ALJ, Squire M, Mason D. Skin peptides of different life stages of Ewing’s tree frog. J. Exp. Zool. Part A Ecol. Genet. Physiol. 2010;313A:532–7. 28. Li J, Hu S, Jian W, Xie C, Yang X. Plant antimicrobial peptides: structures, functions, and applications. Bot. Stud. 2021;62:5. 108 29. Barashkova AS, Rogozhin EA. Isolation of antimicrobial peptides from different plant sources: Does a general extraction method exist? Plant Methods 2020;16:143. 30. Sheehan G, Farrell G, Kavanagh K. Immune priming: the secret weapon of the insect world. Virulence 2020;11:238–46. 31. Wu Q, Patočka J, Kuča K. Insect Antimicrobial Peptides, a Mini Review. Toxins (Basel). 2018;10:461. 32. Lin D, Sutherland D, Aninta SI, Louie N, Nip KM, Li C, et al. Mining Amphibian and Insect Transcriptomes for Antimicrobial Peptide Sequences with rAMPage. Antibiotics 2022;11:952. 33. Riera Romo M, Pérez-Martínez D, Castillo Ferrer C. Innate immunity in vertebrates: an overview. Immunology 2016;148:125–39. 34. Tajbakhsh M, Karimi A, Fallah F, Akhavan MM. Overview of ribosomal and non-ribosomal antimicrobial peptides produced by Gram positive bacteria. Cell. Mol. Biol. 2017;63:20–32. 35. Beckloff N, Diamond G. Computational Analysis Suggests Beta-Defensins Are Processed to Mature Peptides By Signal Peptidase. Protein Pept. Lett. 2008;15:536–40. 36. Blobel G, Dobberstein B. Transfer of proteins across membranes. I. Presence of proteolytically processed and unprocessed nascent immunoglobulin light chains on membrane-bound ribosomes of murine myeloma. J. Cell Biol. 1975;67:835–51. 37. Júnior NGO, Cardoso MH, Cândido ES, van den Broek D, de Lange N, Velikova N, et al. An acidic model pro-peptide affects the secondary structure, membrane interactions and antimicrobial activity of a crotalicidin fragment. Sci. Rep. 2018;8:11127. 109 38. Eder J, Fersht AR. Pro-sequence-assisted protein folding. Mol. Microbiol. 1995;16:609–14. 39. Nguyen LT, Haney EF, Vogel HJ. The expanding scope of antimicrobial peptide structures and their modes of action. Trends Biotechnol. 2011;29:464–72. 40. Brogden KA. Antimicrobial peptides: pore formers or metabolic inhibitors in bacteria? Nat. Rev. Microbiol. 2005;3:238–50. 41. Shai Y. Mechanism of the binding, insertion and destabilization of phospholipid bilayer membranes by α-helical antimicrobial and cell non-selective membrane-lytic peptides. Biochim. Biophys. Acta - Biomembr. 1999;1462:55–70. 42. Matsuzaki K. Control of cell selectivity of antimicrobial peptides. Biochim. Biophys. Acta - Biomembr. 2009;1788:1687–92. 43. Boman HG. Antibacterial peptides: basic facts and emerging concepts. J. Intern. Med. 2003;254:197–215. 44. Aghapour Z, Gholizadeh P, Ganbarov K, Bialvaei AZ, Mahmood SS, Tanomand A, et al. Molecular mechanisms related to colistin resistance in Enterobacteriaceae. Infect. Drug Resist. 2019;12:965–75. 45. Samuel AL. Some Studies in Machine Learning Using the Game of Checkers. IBM J. Res. Dev. 1959;3:210–29. 46. Alpaydin E. Introduction to Machine Learning. Fourth Edi. The MIT Press; 2020. 47. Larrañaga P, Calvo B, Santana R, Bielza C, Galdiano J, Inza I, et al. Machine learning in bioinformatics. Brief. Bioinform. 2006;7:86–112. 110 48. Wang H, Ma C, Zhou L. A Brief Review of Machine Learning and Its Application. In: 2009 International Conference on Information Engineering and Computer Science. 2009. page 1–4. 49. Bahdanau D, Cho K, Bengio Y. Neural Machine Translation by Jointly Learning to Align and Translate. In: 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings. 2015. 50. Pak M, Kim S. A review of deep learning in image recognition. In: 2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT). 2017. page 1–3. 51. Jain M, Bengio E, Hernandez-Garcia A, Rector-Brooks J, Dossou BFP, Ekbote CA, et al. Biological Sequence Design with GFlowNets. In: Proceedings of the 39th International Conference on Machine Learning. 2022. page 9786–801. 52. Berkson J. Application of the Logistic Function to Bio-Assay. J. Am. Stat. Assoc. 1944;39:357–65. 53. Cortes C, Vapnik V. Support-vector networks. Mach. Learn. 1995;20:273–97. 54. Fix E, Hodges JL. Discriminatory Analysis–Nonparametric Discrimination: Consistency Properties. Int. Stat. Rev. / Rev. Int. Stat. 1989;57:238–47. 55. Cover TM, Hart PE. Nearest Neighbor Pattern Classification. IEEE Trans. Inf. Theory 1967;13:21–7. 56. Tin Kam Ho. Random decision forests. In: Proceedings of 3rd International Conference on Document Analysis and Recognition. 1995. page 278–82. 57. Li Y, Huang C, Ding L, Li Z, Pan Y, Gao X. Deep learning in bioinformatics: Introduction, application, and perspective in the big data era. Methods 2019;166:4–21. 111 58. Deng L, Yu D. Deep Learning: Methods and Applications. Found. Trends® Signal Process. 2014;7:197–387. 59. Abiodun OI, Jantan A, Omolara AE, Dada KV, Umar AM, Linus OU, et al. Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition. IEEE Access 2019;7:158820–46. 60. McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 1943;5:115–33. 61. Cybenko G. Approximation by superpositions of a sigmoidal function. Math. Control. Signals, Syst. 1989;2:303–14. 62. Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature 1986;323:533–6. 63. Hopfield JJ. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. 1982;79:2554–8. 64. Hochreiter S, Schmidhuber J. Long Short-Term Memory. Neural Comput. 1997;9:1735–80. 65. Cho K, van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, et al. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2014. page 1724–34. 66. LeCun Y, Jackel LD, Bottou L, Cortes C, Denker JS, Drucker H, et al. Learning algorithms for classification: A comparison on handwritten digit recognition. In: Oh JH, Kwon C, Cho S, editors. Neural networks: The statistical mechanics perspective. World Scientific; 1995. page 261–76. 112 67. Kowsari K, Meimandi KJ, Heidarysafa M, Mendu S, Barnes L, Brown D. Text Classification Algorithms: A Survey. Information 2019;10:150. 68. Li S, Li W, Cook C, Zhu C, Gao Y. Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2018. page 5457–66. 69. Young T, Hazarika D, Poria S, Cambria E. Recent Trends in Deep Learning Based Natural Language Processing [Review Article]. IEEE Comput. Intell. Mag. 2018;13:55–75. 70. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention Is All You Need. In: Advances in Neural Information Processing Systems. 2017. 71. Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E. Hierarchical Attention Networks for Document Classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016. page 1480–9. 72. Xu K, Ba JL, Kiros R, Cho K, Courville A, Salakhutdinov R, et al. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. In: Proceedings of the 32nd International Conference on Machine Learning. 2015. page 2048–57. 73. Shannon CE. A Mathematical Theory of Communication. Bell Syst. Tech. J. 1948;27:379–423. 74. Song F, Croft WB. A general language model for information retrieval. In: Proceedings of the eighth international conference on Information and knowledge management. 1999. page 316–21. 113 75. Mikolov T, Karafiát M, Burget L, Černocký J, Khudanpur S. Recurrent neural network based language model. In: Proceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010. 2010. page 1045–8. 76. Kingma DP, Welling M. Auto-encoding variational bayes. In: 2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings. 2014. 77. Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial nets. In: Advances in Neural Information Processing Systems. 2014. 78. Lima PG, Oliveira JTA, Amaral JL, Freitas CDT, Souza PFN. Synthetic antimicrobial peptides: Characteristics, design, and potential as alternative molecules to overcome microbial resistance. Life Sci. 2021;278:119647. 79. Wu Q, Ke H, Li D, Wang Q, Fang J, Zhou J. Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery. Curr. Top. Med. Chem. 2019;19:4–16. 80. Pérez de la Lastra JM, Garrido-Orduña C, Borges AA, Jiménez-Arias D, García-Machado FJ, Hernández M, et al. Bioinformatics Discovery of Vertebrate Cathelicidins from the Mining of Available Genomes. In: Bobbarala V, editor. Drug Discovery – Concepts to Market. IntechOpen; 2018. 81. Waghu FH, Barai RS, Gurung P, Idicula-Thomas S. CAMPR3: A database on sequences, structures and signatures of antimicrobial peptides. Nucleic Acids Res. 2016;44:D1094–7. 82. Waghu FH, Gopi L, Barai RS, Ramteke P, Nizami B, Idicula-Thomas S. CAMP: Collection of sequences and structures of antimicrobial peptides. Nucleic Acids Res. 2014;42:D1154–8. 114 83. Xiao X, Wang P, Lin WZ, Jia JH, Chou KC. iAMP-2L: A two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Anal. Biochem. 2013;436:168–77. 84. Meher PK, Sahu TK, Saini V, Rao AR. Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC. Sci. Rep. 2017;7:42362. 85. Gers FA, Schmidhuber J, Cummins F. Learning to Forget: Continual Prediction with LSTM. Neural Comput. 2000;12:2451–71. 86. Schuster M, Paliwal KK. Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 1997;45:2673–81. 87. Youmans M, Spainhour C, Qiu P. Long short-term memory recurrent neural networks for antibacterial peptide identification. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2017. page 498–502. 88. Yan J, Bhadra P, Li A, Sethiya P, Qin L, Tai HK, et al. Deep-AmPEP30: Improve Short Antimicrobial Peptides Prediction with Deep Learning. Mol. Ther. - Nucleic Acids 2020;20:882–94. 89. Mikolov T, Chen K, Corrado G, Dean J. Efficient Estimation of Word Representations in Vector Space. In: 1st International Conference on Learning Representations, ICLR 2013 - Workshop Track Proceedings. 2013. 90. Sharma R, Shrivastava S, Kumar Singh S, Kumar A, Saxena S, Kumar Singh R. Deep-ABPpred: identifying antibacterial peptides in protein sequences using bidirectional LSTM with word2vec. Brief. Bioinform. 2021;22:bbab065. 115 91. Wang C, Garlick S, Zloh M. Deep Learning for Novel Antimicrobial Peptide Design. Biomolecules 2021;11:471. 92. Veltri D, Kamath U, Shehu A. Deep learning improves antimicrobial peptide recognition. Bioinformatics 2018;34:2740–7. 93. Richter A, Sutherland D, Ebrahimikondori H, Babcock A, Louie N, Li C, et al. Associating Biological Activity and Predicted Structure of Antimicrobial Peptides from Amphibians and Insects. Antibiotics 2022;11:1710. 94. Huan Y, Kong Q, Mou H, Yi H. Antimicrobial Peptides: Classification, Design, Application and Research Progress in Multiple Fields. Front. Microbiol. 2020;11:582779. 95. Tossi A. Design and Engineering Strategies for Synthetic Antimicrobial Peptides. In: Drider D, Rebuffat S, editors. Prokaryotic Antimicrobial Peptides. New York, NY: Springer; 2011. page 81–98. 96. Nagarajan D, Nagarajan T, Roy N, Kulkarni O, Ravichandran S, Mishra M, et al. Computational antimicrobial peptide design and evaluation against multidrug-resistant clinical isolates of bacteria. J. Biol. Chem. 2018;293:3492–509. 97. Dean SN, Alvarez JAE, Zabetakis D, Walper SA, Malanoski AP. PepVAE: Variational Autoencoder Framework for Antimicrobial Peptide Generation and Activity Prediction. Front. Microbiol. 2021;12:725727. 98. Das P, Sercu T, Wadhawan K, Padhi I, Gehrmann S, Cipcigan F, et al. Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations. Nat. Biomed. Eng. 2021;5:613–23. 99. Szymczak P, Możejko M, Grzegorzek T, Bauer M, Neubauer D, Michalski M, et al. HydrAMP: a deep generative model for antimicrobial peptide discovery. bioRxiv 116 [Internet] 2022;Available from: https://www.biorxiv.org/content/10.1101/2022.01.27.478054v1 100. Gupta A, Zou J. Feedback GAN for DNA optimizes protein functions. Nat. Mach. Intell. 2019;1:105–11. 101. Tucs A, Tran DP, Yumoto A, Ito Y, Uzawa T, Tsuda K. Generating Ampicillin-Level Antimicrobial Peptides with Activity-Aware Generative Adversarial Networks. ACS Omega 2020;5:22847–51. 102. Van Oort CM, Ferrell JB, Remington JM, Wshah S, Li J. AMPGAN v2: Machine Learning-Guided Design of Antimicrobial Peptides. J. Chem. Inf. Model. 2021;61:2198–207. 103. Sohn K, Lee H, Yan X. Learning Structured Output Representation using Deep Conditional Generative Models. In: Advances in Neural Information Processing Systems. 2015. 104. Guo J, Lu S, Cai H, Zhang W, Yu Y, Wang J. Long Text Generation via Adversarial Training with Leaked Information. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2018. 105. Dumoulin V, Belghazi I, Poole B, Mastropietro O, Lamb A, Arjovsky M, et al. Adversarially learned inference. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings. 2017. 106. Donahue J, Krähenbühl P, Darrell T. Adversarial feature learning. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings. 2017. 117 107. Li C, Sutherland D, Hammond SA, Yang C, Taho F, Bergman L, et al. AMPlify: attentive deep learning model for discovery of novel antimicrobial peptides effective against WHO priority pathogens. BMC Genomics 2022;23:77. 108. Hammond SA, Warren RL, Vandervalk BP, Kucuk E, Khan H, Gibb EA, et al. The North American bullfrog draft genome provides insight into hormonal regulation of long noncoding RNA. Nat. Commun. 2017;8:1433. 109. Bingen E, Fitoussi F, Doit C, Cohen R, Tanna A, George R, et al. Resistance to Macrolides in Streptococcus pyogenes in France in Pediatric Patients. Antimicrob. Agents Chemother. 2000;44:1453–7. 110. Novković M, Simunić J, Bojović V, Tossi A, Juretić D. DADP: the database of anuran defense peptides. Bioinformatics 2012;28:1406–7. 111. Won HS, Jung SJ, Kim HE, Seo MD, Lee BJ. Systematic peptide engineering and structural characterization to search for the shortest antimicrobial peptide analogue of gaegurin 5. J. Biol. Chem. 2004;279:14784–91. 112. The UniProt Consortium. UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res. 2019;47:D506–15. 113. Bals R. Epithelial antimicrobial peptides in host defense against infection. Respir. Res. 2000;1:5. 114. Chollet F. Keras [Internet]. 2015 [cited 2019 Apr 17];Available from: https://keras.io 115. Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. arXiv [Internet] 2016;Available from: http://arxiv.org/abs/1603.04467 118 116. Kingma DP, Ba J. Adam: A Method for Stochastic Optimization. In: 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings. 2015. 117. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 2014;1929–58. 118. Zhao H. Keras Multi-Head [Internet]. 2018 [cited 2019 Apr 17];Available from: https://github.com/CyberZHG/keras-multi-head 119. Felix L. Keras Attention Layer [Internet]. 2018 [cited 2019 Apr 17];Available from: https://github.com/lzfelix/keras_attention 120. Wu TD, Nacu S. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics 2010;26:873–81. 121. Holt C, Yandell M. MAKER2: an annotation pipeline and genome-database management tool for second-generation genome projects. BMC Bioinformatics 2011;12:491. 122. Keller O, Kollmar M, Stanke M, Waack S. A novel hybrid gene prediction method employing protein multiple sequence alignments. Bioinformatics 2011;27:757–63. 123. Slater GSC, Birney E. Automated generation of heuristics for biological sequence comparison. BMC Bioinformatics 2005;6:31. 124. Lomsadze A, Ter-Hovhannisyan V, Chernoff YO, Borodovsky M. Gene identification in novel eukaryotic genomes by self-training algorithm. Nucleic Acids Res. 2005;33:6494–506. 125. Korf I. Gene finding in novel genomes. BMC Bioinformatics 2004;5:59. 126. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinformatics 2009;10:421. 119 127. Smit A, Hubley R, Grenn P. RepeatMasker Open-4.0 [Internet]. 2015 [cited 2020 Jan 30];Available from: http://www.repeatmasker.org 128. Clinical and Laboratory Standards Institute. Methods for dilution antimicrobial susceptibility tests for bacteria that grow aerobically: approved standard. 2015. 129. Wiegand I, Hilpert K, Hancock REW. Agar and broth dilution methods to determine the minimal inhibitory concentration (MIC) of antimicrobial substances. Nat. Protoc. 2008;3:163–75. 130. Cameron CE, Brouwer NL, Tisch LM, Kuroiwa JMY. Defining the Interaction of the Treponema pallidum Adhesin Tp0751 with Laminin. Infect. Immun. 2005;73:7485–94. 131. Vanhoye D, Bruston F, Nicolas P, Amiche M. Antimicrobial peptides from hylid and ranin frogs originated from a 150-million-year-old ancestral precursor with a conserved signal peptide but a hypermutable antimicrobial domain. Eur. J. Biochem. 2003;270:2068–81. 132. Zhao RL, Han JY, Han WY, He HX, Ma JF. Effects of Two Novel Peptides from Skin of Lithobates Catesbeianus on Tumor Cell Morphology and Proliferation. In: Brown GG, editor. Molecular Cloning – Selected Applications in Medicine and Biology. InTechOpen; 2011. 133. Cândido ES, Cardoso MH, Chan LY, Torres MDT, Oshiro KGN, Porto WF, et al. Short Cationic Peptide Derived from Archaea with Dual Antibacterial Properties and Anti-Infective Potential. ACS Infect. Dis. 2019;5:1081–6. 134. Lorenzon EN, Piccoli JP, Santos-Filho NA, Cilli EM. Dimerization of Antimicrobial Peptides: A Promising Strategy to Enhance Antimicrobial Peptide Activity. Protein Pept. Lett. 2019;26:98–107. 120 135. Li C, Warren RL, Birol I. Models and data of AMPlify: a deep learning tool for antimicrobial peptide prediction. BMC Res. Notes 2023;16:11. 136. Amaral AC, Silva ON, Mundim NCCR, de Carvalho MJA, Migliolo L, Leite JRSA, et al. Predicting antimicrobial peptides from eukaryotic genomes: In silico strategies to develop antibiotics. Peptides 2012;37:301–8. 137. Prichula J, Primon-Barros M, Luz RCZ, Castro ÍMS, Paim TGS, Tavares M, et al. Genome Mining for Antimicrobial Compounds in Wild Marine Animals-Associated Enterococci. Mar. Drugs 2021;19:328. 138. Tomazou M, Oulas A, Anagnostopoulos AK, Tsangaris GT, Spyrou GM. In Silico Identification of Antimicrobial Peptides in the Proteomes of Goat and Sheep Milk and Feta Cheese. Proteomes 2019;7:32. 139. Burdukiewicz M, Sidorczuk K, Rafacz D, Pietluch F, Chilimoniuk J, Rödiger S, et al. Proteomic Screening for Prediction and Design of Antimicrobial Peptides with AmpGram. Int. J. Mol. Sci. 2020;21:4310. 140. Khurshid Z, Najeeb S, Mali M, Moin SF, Raza SQ, Zohaib S, et al. Histatin peptides: Pharmacological functions and their applications in dentistry. Saudi Pharm. J. 2017;25:25–31. 141. Apata DF. Antibiotic Resistance in Poultry. Int. J. Poult. Sci. 2009;8:404–8. 142. Duckert P, Brunak S, Blom N. Prediction of proprotein convertase cleavage sites. Protein Eng. Des. Sel. 2004;17:107–12. 143. van der Does AM, Hiemstra PS, Mookherjee N. Antimicrobial Host Defence Peptides: Immunomodulatory Functions and Translational Prospects. In: Matsuzaki K, editor. 121 Antimicrobial Peptides. Advances in Experimental Medicine and Biology. Singapore: Springer; 2019. page 149–71. 144. Zhang Y, Skolnick J. Scoring function for automated assessment of protein structure template quality. Proteins 2004;57:702–10. 145. Zhang Y, Skolnick J. TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Res. 2005;33:2302–9. 146. Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M. ColabFold: making protein folding accessible to all. Nat. Methods 2022;19:679–82. 147. Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021;596:583–9. 148. Steinegger M, Söding J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat. Biotechnol. 2017;35:1026–8. 149. Alberts B, Johnson A, Lewis J, Raff M, Roberts K, Walter P. Analyzing protein structure and function. In: Molecular Biology of the Cell, 4th edition. New York: Garland Science; 2002. 150. van Dijk A, Veldhuizen EJA, van Asten AJAM, Haagsman HP. CMAP27, a novel chicken cathelicidin-like antimicrobial protein. Vet. Immunol. Immunopathol. 2005;106:321–7. 151. Xiao Y, Cai Y, Bommineni YR, Fernando SC, Prakash O, Gilliland SE, et al. Identification and Functional Characterization of Three Chicken Cathelicidins with Potent Antimicrobial Activity. J. Biol. Chem. 2006;281:2858–67. 122 152. Santos SR dos, Miranda A, Silva Junior PI da. Ovipin: a new antimicrobial peptide from chicken eggs Gallus gallus. bioRxiv [Internet] 2022;Available from: https://www.biorxiv.org/content/10.1101/2021.09.28.462162v2 153. Goraya J, Knoop FC, Conlon JM. Ranatuerins: Antimicrobial Peptides Isolated from the Skin of the American Bullfrog,Rana catesbeiana. Biochem. Biophys. Res. Commun. 1998;250:589–92. 154. Horváti K, Bacsa B, Mlinkó T, Szabó N, Hudecz F, Zsila F, et al. Comparative analysis of internalisation, haemolytic, cytotoxic and antibacterial effect of membrane-active cationic peptides: aspects of experimental setup. Amino Acids 2017;49:1053–67. 155. Li C, Warren RL, Birol I. Model files and data sets of AMPlify: a deep learning tool for antimicrobial peptide prediction [Internet]. Zenodo 2022;Available from: https://doi.org/10.5281/zenodo.7320306 156. Li C, Birol I. Candidate antimicrobial peptide sequences mined from the UniProtKB/Swiss-Prot database using AMPlify [Internet]. Zenodo 2023;Available from: https://doi.org/10.5281/zenodo.8133088 157. Fleitas O, Franco OL. Induced Bacterial Cross-Resistance toward Host Antimicrobial Peptides: A Worrying Phenomenon. Front. Microbiol. 2016;7:381. 158. Kintses B, Jangir PK, Fekete G, Számel M, Méhi O, Spohn R, et al. Chemical-genetic profiling reveals limited cross-resistance between antimicrobial peptides with different modes of action. Nat. Commun. 2019;10:5731. 159. Papareddy P, Rydengård V, Pasupuleti M, Walse B, Mörgelin M, Chalupka A, et al. Proteolysis of Human Thrombin Generates Novel Host Defense Peptides. PLoS Pathog. 2010;6:e1000857. 123 160. Robertson S. NLP From Scratch: Generating Names with a Character-Level RNN [Internet]. 2017 [cited 2021 Mar 15];Available from: https://pytorch.org/tutorials/intermediate/char_rnn_generation_tutorial.html#nlp-from-scratch-generating-names-with-a-character-level-rnn 161. Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In: Advances in Neural Information Processing Systems. 2019. 162. Robbins H, Monro S. A Stochastic Approximation Method. Ann. Math. Stat. 1951;22:400–7. 163. Fort S, Hu H, Lakshminarayanan B. Deep Ensembles: A Loss Landscape Perspective. arXiv [Internet] 2019;Available from: http://arxiv.org/abs/1912.02757 164. Teimouri H, Nguyen TN, Kolomeisky AB. Single-cell stochastic modelling of the action of antimicrobial peptides on bacteria. J. R. Soc. Interface 2021;18:20210392. 165. Gagnon MC, Strandberg E, Grau-Campistany A, Wadhwani P, Reichert J, Bürck J, et al. Influence of the Length and Charge on the Activity of α-Helical Amphipathic Antimicrobial Peptides. Biochemistry 2017;56:1680–95. 166. Zelezetsky I, Tossi A. Alpha-helical antimicrobial peptides—Using a sequence template to guide structure–activity relationship studies. Biochim. Biophys. Acta - Biomembr. 2006;1758:1436–49. 167. Li C, Birol I. Model files of AMPd-Up: a tool for antimicrobial peptide sequence generation [Internet]. Zenodo 2023;Available from: https://doi.org/10.5281/zenodo.7905591 124 168. Khurana D, Koli A, Khatter K, Singh S. Natural language processing: state of the art, current trends and challenges. Multimed. Tools Appl. 2023;82:3713–44. 169. Wiradharma N, Khoe U, Hauser CAE, Seow SV, Zhang S, Yang YY. Synthetic cationic amphiphilic α-helical peptides as antimicrobial agents. Biomaterials 2011;32:2204–12. 170. Khara JS, Obuobi S, Wang Y, Hamilton MS, Robertson BD, Newton SM, et al. Disruption of drug-resistant biofilms using de novo designed short α-helical antimicrobial peptides with idealized facial amphiphilicity. Acta Biomater. 2017;57:103–14. 171. Yu LT, Xiao YP, Li JJ, Ran JS, Yin LQ, Liu YP, et al. Molecular characterization of a novel ovodefensin gene in chickens. Gene 2018;678:233–40. 172. Vasilchenko AS, Rogozhin EA, Vasilchenko AV, Kartashova OL, Sycheva MV. Novel haemoglobin-derived antimicrobial peptides from chicken (Gallus gallus) blood: purification, structural aspects and biological activity. J. Appl. Microbiol. 2016;121:1546–57. 173. Evans EW, Beach GG, Wunderlich J, Harmon BG. Isolation of antimicrobial peptides from avian heterophils. J. Leukoc. Biol. 1994;56:661–5. 174. Šidák Z. Rectangular Confidence Regions for the Means of Multivariate Normal Distributions. J. Am. Stat. Assoc. 1967;62:626–33. 125 Appendices Appendix A Chapter 2 supplementary materials This appendix contains supplementary information for Chapter 2. A.1 Supplementary notes A.1.1 Performance comparison of AMP Scanner Vr.2 and AMPlify re-trained on the AMP Scanner Vr.2 dataset In order to test whether the AMPlify architecture can still perform well on other datasets, we conducted an analysis on the AMP Scanner Vr.2 datasets [92] (named “Train”, “Tune” and “Test” partitions) in two different ways. First, AMPlify was cross-validated on all data provided by AMP Scanner Vr.2 in the same way they did in their study [92]. Second, AMPlify was re-trained on the dataset (“Train+Tune” partitions) used in the original AMP Scanner Vr.2 publication for performance comparison on the “Test” partition [92]. The results for AMP Scanner Vr.2 were taken directly from Table 1 of Veltri and co-workers’ work [92], with the best tuned hyperparameters. They did not report the F1 score, so this metric is not compared here. Table A.2 shows the 10-fold cross-validation results of AMP Scanner Vr.2 and AMPlify on all data provided by AMP Scanner Vr.2. A single sub-model of AMPlify without ensemble learning still outperforms AMP Scanner Vr.2 in accuracy (92.15% vs. 91.51%), sensitivity (91.06% vs. 88.81%) and AUROC (97.15% vs. 96.58%), although its specificity is slightly lower, with a difference < 1%. After ensemble learning is adopted, AMPlify outperforms AMP Scanner Vr.2 in all four metrics by at least 1% and as much as 1.74%. 126 Table A.3 shows the performance comparison on the AMP Scanner Vr.2 test set between AMP Scanner Vr.2 and the re-trained version (on “Train+Tune” partitions) of AMPlify. AMPlify re-trained on their dataset still outperforms AMP Scanner Vr.2 in all four metrics of accuracy (92.35% vs. 91.01%), sensitivity (90.59% vs. 89.89%), specificity (94.10% vs. 92.13%) and AUROC (97.00% vs. 96.48%). However, single sub-models of AMPlify re-trained on their dataset does not show substantial advantage in overall accuracy over AMP Scanner Vr.2. First, the re-trained version of AMPlify here was trained on fewer data than AMP Scanner Vr.2 due to the early stopping technique used. Second, the validation sets we set aside for early stopping here were too small compared with those used in our own training process, as the entire set for training here is only 1/3 of ours in size. Note that the 10-fold cross-validation did not suffer from such problems because it was done on all data, with both the training and the early stopping monitoring validation sets of single sub-model within each fold larger. After a train-validation split for early stopping, the new training and early stopping monitoring validation sets for test comparison comprised only 1704–1706 and 426–428 sequences respectively, while those for 10-fold cross-validation used 2560–2562 and 640–642 sequences respectively. Figure A.3 shows a comparison between the learning curves of five single sub-models trained on our own balanced training set and on their original dataset (“Train+Tune” partitions), respectively. Obviously, the validation accuracy fluctuated greatly when the models were being trained on their dataset, which suggests that those validation sets were too small to represent the distribution of the entire dataset. Hence, single sub-models of AMPlify were not well trained on their dataset since it was difficult for early stopping to decide the best epochs in such cases. Thus, we conclude that their dataset is not sufficiently large to train and validate AMPlify. 127 In summary, AMPlify and its single sub-models can outperform the state-of-the-art method, AMP Scanner Vr.2, with respect to the 10-fold cross-validation analysis on their dataset. Further, AMPlify re-trained on their dataset is still able to outperform AMP Scanner Vr.2, and arguably the single sub-models of AMPlify were likely to gain better performance if the training set were large enough. A.1.2 Comparison of different AMP prediction tools tested on similar sequences with different labels To evaluate the ability of different AMP prediction tools in differentiating similar sequences with different labels (AMP or non-AMP), we tested AMPlify (balanced model) and its comparators on a set of 32 sequences containing a known AMP GGN5 and its 31 analogues from the work by Won and co-workers [111]. The 31 analogues of GGN5 were generated by truncating the parent peptide sequences into shorter fragments and/or by amino acid substitutions. Among the 32 sequences, 21 of them showed antimicrobial activity against at least one of the ten bacterial species tested, while the rest 11 did not show any activity [111]. The AMPlify balanced model and other tools predict all those 32 peptides as AMPs (Table A.4). We note that the online server of iAMP-2L [83] was down at the time this analysis was done (November 7, 2021), so its results were not compared here. These results indicate that all the current AMP prediction tools have limitation in identifying AMPs and non-AMPs that are highly similar in their sequences. This is likely due to the lack of experimentally validated data for highly similar AMP and non-AMP sequences in the public database for model training. 128 A.2 Supplementary tables Table A.1 Ablation study results of AMPlify model architecture using stratified 5-fold cross-validation on the balanced training set. The top section compares the architecture of AMPlify, with and without ensemble learning, with its simpler variations. The second section shows the architecture of AMP Scanner Vr.2 [92] cross-validated on our balanced training set. Values of accuracy (acc), sensitivity (sens), specificity (spec), F1 score (F1) and area under the receiver operating characteristic curve (AUROC) are presented along with their standard deviations in percentage. Model Architecture Acc Sens Spec F1 AUROC Bi-LSTM 89.04±1.02 89.10±1.14 88.98±1.80 89.05±0.98 95.82±0.56 Bi-LSTM + CA 90.13±1.02 90.05±0.94 90.20±1.91 90.13±0.95 96.14±0.37 Bi-LSTM + MHSDPA + CAa 91.70±0.66 91.40±0.71 92.00±1.51 91.68±0.61 96.92±0.30 AMPlify: Bi-LSTM + MHSDPA + CA (with ensemble) 92.79±0.77 92.12±1.28 93.47±0.53 92.74±0.80 97.44±0.53 AMP Scanner Vr.2: Embedding + Conv + Max Pooling + LSTM - [10 epochsb] - [early stoppedc] 90.55±1.43 90.52±0.76 90.14±1.62 89.45±1.10 90.95±4.08 91.58±1.48 90.54±1.17 90.42±0.74 96.40±0.58 96.35±0.43 a Trained without ensemble learning (i.e., single sub-model). b The best hyperparameter as stated in the paper [92]. c Optimal numbers of training epochs determined by early stopping range from 17 to 71. Table A.2 Comparison between AMP Scanner Vr.2 and AMPlify cross-validated on all data provided by AMP Scanner Vr.2. This table shows the 10-fold cross-validation results of AMP Scanner Vr.2 and AMPlify on all data provided by AMP Scanner Vr.2 (“Train+Tune+Test” partitions) [92]. Values of accuracy (acc), sensitivity (sens), specificity (spec) and area under the receiver operating characteristic curve (AUROC) are presented along with their standard deviations in percentage. Model Acc Sens Spec AUROC AMP Scanner Vr.2 91.51±0.89 88.81±3.53 94.21±2.68 96.58±0.66 AMPlify (re-trained, single sub-model) 92.15±0.86 91.06±1.76 93.25±2.51 97.15±0.58 AMPlify (re-trained, ensemble) 93.22±0.71 90.55±1.62 95.89±1.56 97.61±0.46 129 Table A.3 Performance comparison between AMP Scanner Vr.2 and AMPlify re-trained on the AMP Scanner Vr.2 “Train+Tune” partitions and tested on their “Test” partition. Since AMPlify applies early stopping and the exact size of training set for each single sub-model is smaller, the exact training set size for each model is listed here in the second column. Values of accuracy (acc), sensitivity (sens), specificity (spec) and area under the receiver operating characteristic curve (AUROC) are presented in percentage. Model Training set size Acc Sens Spec AUROC AMP Scanner Vr.2 2132 91.01 89.89 92.13 96.48 AMPlify (re-trained) - [single sub-model 1] - [single sub-model 2] - [single sub-model 3] - [single sub-model 4] - [single sub-model 5] 2132 1704 1706 1706 1706 1706 92.35 91.01 91.36 91.71 91.22 91.36 90.59 91.85 88.76 91.43 88.06 88.20 94.10 90.17 93.96 91.99 94.38 94.52 97.00 96.29 96.21 96.74 96.14 96.20 130 Table A.4 Prediction results of GGN5 and its analogues by different AMP prediction tools. Antimicrobial activity data of GGN5 and its analogues were taken from the work by Won and co-workers [111]. The analogues were generated by truncating the parent peptide sequences into shorter fragments and/or by amino acid substitutions. Prediction results of AMPlify, AMP Scanner Vr.2 [92], and iAMPpred [84] were listed for comparison. Serial Number Peptide Namea Active?b AMPlifyc AMP Scanner Vr.2d iAMPprede Score Prediction Score Prediction Score Prediction ABP AVP AFP 0 GGN5 ✓ 1.0000 AMP 1.0000 AMP 1.00 0.78 1.00 ABP, AVP, AFP 1 GGN5N9 0.9940 AMP 0.9979 AMP 0.69 0.75 0.65 ABP, AVP, AFP 2 GGN5N11 0.9819 AMP 0.9999 AMP 0.96 0.82 0.95 ABP, AVP, AFP 3 GGN5N12 0.9934 AMP 1.0000 AMP 0.94 0.76 0.91 ABP, AVP, AFP 4 GGN5N13 ✓ 0.9962 AMP 1.0000 AMP 0.95 0.77 0.92 ABP, AVP, AFP 5 GGN5N11(3-13) ✓ 0.9580 AMP 0.9793 AMP 0.99 0.74 0.95 ABP, AVP, AFP 6 GGN5N13(2-14) 0.7552 AMP 0.9994 AMP 0.97 0.62 0.88 ABP, AVP, AFP 7 GGN5N13(3-15) 0.9879 AMP 0.9968 AMP 0.91 0.38 0.73 ABP, AFP 8 GGN5N13(4-16) 0.9944 AMP 0.9982 AMP 0.85 0.54 0.57 ABP, AVP, AFP 9 GGN5N13(5-17) 0.8087 AMP 0.9995 AMP 0.94 0.63 0.71 ABP, AVP, AFP 10 G3K-GGN5N13 ✓ 0.9944 AMP 1.0000 AMP 0.98 0.87 0.95 ABP, AVP, AFP 11 G3K/S10L-GGN5N13 ✓ 0.9984 AMP 0.9999 AMP 0.98 0.90 0.94 ABP, AVP, AFP 12 F1A-GGN5N13 ✓ 0.9948 AMP 0.9998 AMP 0.98 0.79 0.91 ABP, AVP, AFP 13 F1W-GGN5N13 ✓ 0.8293 AMP 1.0000 AMP 0.93 0.81 0.77 ABP, AVP, AFP 14 F1W-GGN5N11 ✓ 0.9356 AMP 1.0000 AMP 0.96 0.83 0.86 ABP, AVP, AFP 15 L2W-GGN5N11 ✓ 0.9957 AMP 0.9999 AMP 0.85 0.86 0.69 ABP, AVP, AFP 16 G3W-GGN5N11 ✓ 0.9971 AMP 1.0000 AMP 0.76 0.89 0.60 ABP, AVP, AFP 17 A4W-GGN5N11 ✓ 0.9917 AMP 1.0000 AMP 0.85 0.88 0.70 ABP, AVP, AFP 18 L5W-GGN5N11 0.9819 AMP 1.0000 AMP 0.85 0.86 0.69 ABP, AVP, AFP 19 F6W-GGN5N11 ✓ 0.9888 AMP 1.0000 AMP 0.96 0.83 0.86 ABP, AVP, AFP 20 K7W-GGN5N11 0.9909 AMP 0.9999 AMP 0.55 0.71 0.28 ABP, AVP 21 V8W-GGN5N11 ✓ 0.9967 AMP 1.0000 AMP 0.86 0.88 0.78 ABP, AVP, AFP 131 Serial Number Peptide Namea Active?b AMPlifyc AMP Scanner Vr.2d iAMPprede Score Prediction Score Prediction Score Prediction ABP AVP AFP 22 A9W-GGN5N11 0.9974 AMP 0.9999 AMP 0.85 0.88 0.70 ABP, AVP, AFP 23 S10W-GGN5N11 ✓ 0.9810 AMP 0.9999 AMP 0.85 0.94 0.74 ABP, AVP, AFP 24 K11W-GGN5N11 0.9824 AMP 0.9999 AMP 0.54 0.70 0.28 ABP, AVP 25 A4L-GGN5N11 ✓ 0.9789 AMP 0.9999 AMP 0.98 0.81 0.97 ABP, AVP, AFP 26 A4K-GGN5N11 ✓ 0.9957 AMP 1.0000 AMP 0.99 0.86 0.99 ABP, AVP, AFP 27 A4F-GGN5N11 ✓ 0.8982 AMP 1.0000 AMP 0.84 0.85 0.88 ABP, AVP, AFP 28 V8L-GGN5N11 ✓ 0.9936 AMP 0.9999 AMP 0.97 0.80 0.96 ABP, AVP, AFP 29 V8K-GGN5N11 ✓ 0.9998 AMP 0.9999 AMP 0.99 0.86 0.98 ABP, AVP, AFP 30 V8F-GGN5N11 ✓ 0.9943 AMP 0.9999 AMP 0.84 0.84 0.88 ABP, AVP, AFP 31 A4W/V8W-GGN5N11 ✓ 0.9970 AMP 1.0000 AMP 0.86 0.91 0.30 ABP, AVP a Peptides were taken from the work by Won and co-workers [111]; The right superscripts in the peptide names indicate the lengths and positions of truncation from N-termini, while the left side characters indicate the positions and amino acids of substitution (e.g., GGN5N11(3-13) stands for a fragment of GGN5 from position 3 to 13 with length 11; G3K-GGN5N13 stands for a fragment of the first 13 residues from the N-terminus with a substitution of K for G at position 3); Please refer to the original paper for details [111]. b Whether the peptide shows antimicrobial activity against at least one of the ten bacterial species tested in the work: Bacillus subtilis, Micrococcus luteus, Staphyloccus aureus, Staphylococcus epidermis, Escherichia coli, Shigella dysentariae, Salmonella typhimurium, Klebsiella pneumoniae, Proteus mirabilis, and Pseudomonas aeruginosa [111]. c The balanced model of AMPlify was utilized for prediction; The probability scores of AMPlify were chosen for a clearer comparison with other tools. d The “Orig. Production” model was chosen for AMP Scanner Vr.2 [92]. e iAMPpred predicts and classifies AMPs into ABPs (antibacterial peptides), AVPs (antiviral peptides), and AFPs (antifungal peptides) [84]. 132 A.3 Supplementary figures Figure A.1 Workflow of selecting the non-AMP sequences from the UniProtKB/Swiss-Prot database. UniProtKB/Swiss-Prot(2019_02 release)169,213 sequencesSet B: 7,026 sequencesDownload sequences(length: 1-200, not annotated with any keyword) Download sequences(length: 1-200, annotated with any of the keyword ) Remove duplicates and sequences with non-standard amino acids AMP Set: 4,173 sequencesSet A: 128,691 sequences128,445 non-AMPsRemove sequences in Set B and AMP Set from Set ASample by approximating the same length distribution as the AMP SetNon-AMP Set: 4,173 sequences 133 Figure A.2 Workflow of the AMP discovery pipeline. The process describes how 75 putative AMPs were identified from the bullfrog genome, with the AMPlify balanced model utilized for AMP prediction. Invalid sequences denote those not suitable for AMPlify prediction, with lengths outside the range of 2 to 200 amino acids or with non-standard amino acids. 1,171 AMP precursor cDNA sequences Bullfrog genomeAlign AMPs to genome(GMAP)231 genomic scaffolds AMP evidenceGene prediction (MAKER2)449 candidate precursorsIn silico enzymatic cleavage 101 mature sequencesAMP prediction (AMPlify)75 putative AMPsRemove invalid sequences 134 Figure A.3 Learning curve comparison of single sub-models of AMPlify trained on two different datasets. (a) Single sub-models of AMPlify trained on our own balanced training set; (b) Single sub-models of AMPlify re-trained on the AMP Scanner Vr.2 “Train+Tune” partitions. All learning curves were plotted based on the accuracy values on the validation sets for early stopping. Square markers denote the best epochs chosen by early stopping, and the x-axes have been set in the same range in order for a clearer comparison. a b 135 Figure A.4 UpSet plot of the 101 candidate mature peptide sequences mined from the bullfrog genome and fed into AMPlify for prediction with regard to the three filters. This plot visualizes the results obtained by applying different combinations of filters to the 101 candidate mature peptide sequences mined from the bullfrog genome and fed into AMPlify for prediction. 136 Appendix B Chapter 3 supplementary materials This appendix contains supplementary information for Chapter 3. B.1 Supplementary tables Table B.1 Characteristics of the 40 putative AMP sequences mined from the UniProtKB/Swiss-Prot database that have been prioritized for synthesis. A total number of 397 short cationic novel putative AMPs with AMPlify scores ≥ 10 (i.e., AMPlify probability scores ≥ 0.9) were compared with known chicken AMPs for their similarities in predicted three-dimensional (3D) structures. The top section shows the 30 most structurally similar putative AMPs to the known chicken AMPs as measured by TM-scores, while the bottom section shows the 10 least structurally similar ones to the known chicken AMPs. Peptide name Sequence # aa Net chargea MW (Da) AMPlify scoreb Sequence similarity to known AMPsc (%) Structural similarity to known chicken AMPsd (TM-score) Reference chicken AMPe DaRe1 WLLVLQRGHRLASIKHVCQLSERKR 25 5 3027.63 17.23 40.00 0.8352 Chicken CATH-2 ScPo1 AWDWAKNWVFWTCSYLLNFLYHHHCSRDLIRR 32 2 4138.74 17.90 37.50 0.8252 Chicken CATH-2 ArTh10 DLWFGLKTEGELAFVKRTIFDVIYKSAKRKR 31 4 3716.39 10.84 35.48 0.8233 Chicken CATH-2 HoSa1 GLQKLINKIKSQMSRFSTKTNKICGP 26 6 2921.51 18.52 38.46 0.7951 Chicken CATH-2 HoSa2 TRWEAMKAKATELRVCCARRKR 22 6 2664.21 24.15 33.33 0.7829 Chicken CATH-3 DiVi1 VWEFFEAGAGLRASNASKKIYGVAKRFRR 29 5 3315.83 12.91 36.36 0.7761 Chicken CATH-3 DiDi2 FWNLNKKKKFFYKTVKNSIGQVILRDMSNN 30 6 3662.31 16.02 33.33 0.7725 Chicken CATH-2 DiDi3* GVLKIPLAFVQMIAISIALICLLIP 25 1 2650.45 30.78 44.00 0.7724 Chicken CATH-3 137 Peptide name Sequence # aa Net chargea MW (Da) AMPlify scoreb Sequence similarity to known AMPsc (%) Structural similarity to known chicken AMPsd (TM-score) Reference chicken AMPe LySt2 FMRFGKRFMRFGRFGKSAEVENNIQIAAKQS 31 5 3652.26 29.08 35.48 0.7681 Chicken CATH-3 MuMu1 RWSQWAYAGRAQFCAVRRSVFGFSVRSGMVCRPRR 35 8 4135.81 24.44 34.29 0.7676 Chicken CATH-2 CaGl1 SYWWWGFHKNVIDRREAFYADLAEKKKAEN 30 1 3759.20 10.26 33.33 0.7608 Chicken CATH-3 HoSa3 AKETLEKKKLLKELWESSKKVH 22 3 2653.16 11.53 40.91 0.7582 Chicken CATH-3 BoMo2 AWSSLHSGWAKRAWQDMSSAWGKR 24 3 2790.11 16.99 33.33 0.7533 Chicken CATH-2 ScPo2 FMIHNTLGLFYRSVVRNEIKKR 22 4 2722.25 12.28 36.36 0.7531 Chicken CATH-3 DiDi1 FTLFFPIFMIVVCVISFFNLHKR 23 2 2819.51 19.34 34.78 0.7383 Chicken CATH-3 RaNo1 YFAFYNLFHCLKKDSNNVEMYLKLLKCRLIRSKC 34 5 4203.07 13.66 35.29 0.7383 Chicken CATH-3 HoSa4 WIIQCWWRQVLEKLLAKRRR 20 5 2682.28 33.26 35.00 0.7347 Chicken CATH-3 GaGa2 GLIKRIIRQKR 11 5 1380.75 23.12 45.45 0.7305 Ovipin MuMu2* ILTPVSTVLALLLIAALILLKR 22 2 2345.04 10.44 40.91 0.7250 Chicken CATH-3 RaNo2 ASWEYLVHHVMAMGAFFSGIFWKR 24 1 2871.37 21.88 29.17 0.7242 Chicken CATH-2 HoSa5 HTSRLKARKHSKRRVRYICEFTIPQ 25 7 3111.67 19.37 32.00 0.7210 Chicken CATH-3 138 Peptide name Sequence # aa Net chargea MW (Da) AMPlify scoreb Sequence similarity to known AMPsc (%) Structural similarity to known chicken AMPsd (TM-score) Reference chicken AMPe EiHe1 ALWKLGEPSDHLLQWLVLHLASLHLRLLFKR 31 2 3704.47 10.38 38.71 0.7195 Chicken CATH-3 PlVi1 ILDKVRLWLARIRLNLLKR 19 5 2390.01 30.48 40.00 0.7168 Chicken CATH-3 MuMu3 YFAFHNLFHCLKKDSSHVEMYLKLLKCRLIQSNC 34 3 4130.92 11.32 32.35 0.7139 Chicken CATH-2 SuSc1 ARLDVAAEFRKKWNKWALSRGKR 23 6 2787.27 10.01 34.78 0.7067 Chicken CATH-3 ArTh11 RSIIRNGIRTLTWRKETKRKKK 22 9 2769.34 14.28 36.36 0.7023 Chicken CATH-3 MoIn1 VLKKSEIRIDFISRTILHI 19 2 2281.77 15.95 31.58 0.6993 Chicken CATH-2 HoSa6 GVLKKVIRHKR 11 5 1333.69 31.40 42.11 0.6985 Ovipin CaGl2 QFFLPMILRLYVSRLFISKL 20 3 2485.11 10.40 50.00 0.6976 Chicken CATH-3 HoSa7 LYVFCCRTRAKTPSVIYTINLVVTDLLVGLSLPTR 35 3 3926.73 18.06 28.57 0.6934 Chicken CATH-3 GaGa4 ARSKKSPWPGWAPLAAPHSH 20 3 2181.49 10.93 35.00 0.2230 OvoDBβ OrSa6 VMRSAGSRSSKR 12 4 1321.52 15.92 37.50 0.2209 OvoDBβ GoGo1 GRGRPKGRGRGRPRGRPRGSKR 22 11 2457.84 26.78 44.12 0.2179 CHB1 HoSa8 QVWKRR 6 3 872.04 14.30 42.86 0.2148 Ovipin HoSa9 FQWHGRKPGPETGVPQSRPPIPR 23 3 2624.99 18.42 34.78 0.2119 CHB2 139 Peptide name Sequence # aa Net chargea MW (Da) AMPlify scoreb Sequence similarity to known AMPsc (%) Structural similarity to known chicken AMPsd (TM-score) Reference chicken AMPe DrMe6 RIRGHTIKG 9 3 1037.23 11.07 37.50 0.2113 Chicken Heterophil Peptide 2 MuMu4 FLEKPPGPLGARPLGGK 17 2 1734.07 13.78 47.06 0.2094 OvoDBβ HoSa10 TPKFVGK 7 2 775.95 11.04 41.67 0.2047 OvoDBβ UnBi1 VRCRFACC 8 2 957.19 16.54 41.67 0.1997 Ovipin ScPo3 SPLHKR 6 2 736.87 17.91 42.86 0.1942 Ovipin a Net charge at pH = 7. b AMPlify scores from the balanced model. AMPlify scores range from 0 to 80; sequences with AMPlify scores > 3.01 (i.e., AMPlify probability scores > 0.5) are predicted as AMPs. c Sequence similarity to the most similar known AMP sequence from Antimicrobial Peptide Database (APD3, downloaded on July 11, 2022) [17] and Database of Anuran Defense Peptides (DADP, downloaded on December 6, 2018) [110]. d Structural similarity to the most structurally similar known chicken AMP (i.e., reference chicken AMP) from APD3 [17] (downloaded on October 14, 2022). Three-dimensional (3D) structures of the peptides were predicted by ColabFold [146,147], and the structural similarity between two peptides were evaluated by TM-score [144] normalized by the average peptide sequence length using TM-align [145]. e The most similar known chicken AMP in predicted 3D structures. * DiDi3 and MuMu2 were not successfully synthesized and were excluded for further tests. 140 Table B.2 Overview of the 40 putative AMP sequences mined from the UniProtKB/Swiss-Prot database that have been prioritized for synthesis regarding their corresponding parent sequence information in the database. This table supplements Table B.1 with information about the 40 putative AMP sequences regarding their corresponding parent sequences. The top section shows the 30 most structurally similar putative AMPs to the known chicken AMPs as measured by TM-scores, while the bottom section shows the 10 least structurally similar ones to the known chicken AMPs. Putative AMP name Position in parent sequencea UniProt entry ID Source organism Source organism categoryb DaRe1 17 – 41 B0S8I0 Danio rerio Others ScPo1 20 – 51 Q9Y7K8 Schizosaccharomyces pombe Others ArTh10 [29 – 45] + [356 – 369] Q7Y223 Arabidopsis thaliana Plant HoSa1 299 – 324 Q8NH93 Homo sapiens Mammal HoSa2 34 – 55 Q8IYJ2 Homo sapiens Mammal DiVi1 983 – 1011 Q24702 Dictyocaulus viviparus Others DiDi2 38 – 67 Q54GV3 Dictyostelium discoideum Others DiDi3* 145 – 169 Q54UP0 Dictyostelium discoideum Others LySt2 [187 – 199] + [223 – 225] + [292 – 306] P19802 Lymnaea stagnalis Others MuMu1 16 – 50 P09925 Mus musculus Mammal CaGl1 31 – 60 Q6FWE8 Candida glabrata Others HoSa3 145 – 166 Q9H3J6 Homo sapiens Mammal BoMo2 44 – 67 P82003 Bombyx mori Insect ScPo2 36 – 57 O74430 Schizosaccharomyces pombe Others DiDi1 539 – 561 Q54TM2 Dictyostelium discoideum Others RaNo1 194 – 227 P09320 Rattus norvegicus Mammal HoSa4 39 – 58 A8MTL0 Homo sapiens Mammal GaGa2 23 – 33 P10039 Gallus gallus Others MuMu2* 456 – 477 Q2TB54 Mus musculus Mammal RaNo2 110 – 133 Q5U2T1 Rattus norvegicus Mammal HoSa5 [23 – 38] + [189 – 197] O75596 Homo sapiens Mammal 141 Putative AMP name Position in parent sequencea UniProt entry ID Source organism Source organism categoryb EiHe1 32 – 62 A0A291NUI5 Eidolon helvum Mammal PlVi1 99 – 117 P0CV63 Plasmopara viticola Others MuMu3 194 – 227 O35256 Mus musculus Mammal SuSc1 22 – 44 P53366, O62827 Sus scrofa, Bos taurus Mammal ArTh11 28 – 49 Q6NKN8 Arabidopsis thaliana Plant MoIn1 1861 – 1879 Q09WW0 Morus indica Plant HoSa6 23 – 33 P24821, Q29116 Homo sapiens, Sus scrofa Mammal CaGl2 29 – 48 P05040 Candida glabrata Others HoSa7 75 – 109 Q99678 Homo sapiens Mammal GaGa4 [25 – 29] + [286 – 300] Q92080 Gallus gallus Others OrSa6 25 – 36 Q5VRI5 Oryza sativa subsp. japonica Plant GoGo1 744 – 765 A1YF22, A1YG99, A2T7S4, A2T771, Q9UKY1 Gorilla gorilla gorilla, Pan paniscus, Pongo pygmaeus, Pan troglodytes, Homo sapiens Mammal HoSa8 200 – 205 Q9BXP8 Homo sapiens Mammal HoSa9 562 – 584 Q68DV7 Homo sapiens Mammal DrMe6 17 – 25 O46201 Drosophila melanogaster Insect MuMu4 469 – 485 Q9Z0L3 Mus musculus Mammal HoSa10 286 – 292 Q86YB7 Homo sapiens Mammal UnBi1 10 – 17 C0HKK6 Unedogemmula bisaya Others ScPo3 24 – 29 O42663 Schizosaccharomyces pombe Others a Position of the putative mature AMP sequence in the corresponding parent sequence. For a putative AMP sequence that is the recombination of multiple cleaved peptides, the positions of those cleaved peptides are presented in brackets with plus signs connecting them to each other. 142 b Source organisms are classified into five categories: amphibian, plant, insect, mammal, and others. * DiDi3 and MuMu2 were not successfully synthesized and were excluded for further tests. Table B.3 Seven reference chicken AMP sequences for the 40 putative AMPs mined from the UniProtKB/Swiss-Prot database and prioritized for synthesis. The AMP sequences listed in this table are the seven reference chicken AMPs for the 40 putative AMPs listed in Table B.1. Only the three reference chicken AMPs for the top 30 putative AMPs which share highest structural similarities to known chicken AMPs were prioritized for synthesis and further tests (i.e., Chicken CATH-2, Chicken CATH-3, and Ovipin). APD3 ID Peptide name Sequence # aa Net chargea MW (Da) AP00548 Chicken CATH-2 [150] RFGRFLRKIRRFRPKVTITIQGSARFG 27 9 3264.92 AP00613 Chicken CATH-3 [151] RVKRFWPLVPVAINTVAAGINLYKAIRRK 29 7 3351.09 AP03457 Ovipin [152] YVSPVAIVKGLNIPL 15 1 1582.95 AP03014 OvoDBβ [171] QSKKCCGRCSSRMCTKREKEEHTEDCRGSFCCLTHRKKK 39 7 4609.37 AP02878 CHB1 [172] VLSAADKNNVKGIFTKIAGHAEEYGAETLERMFTTYPPTKTY 42 0 4664.27 AP02879 CHB2 [172] LTAEDKKLIQQAWEKAASHQEEFGAEALTRMFTTYPQTKTY 41 -1 4762.33 AP00265 Chicken Heterophil Peptide 2 [173] GRKSDCFRKNGFCAFLKCPYLTLISGLCSFHLC 33 4 3729.47 a Net charge at pH = 7. 143 Table B.4 Antimicrobial susceptibility testing and hemolysis experiment results of the 38 successfully synthesized putative AMPs mined from the UniProtKB/Swiss-Prot database and tested in vitro. Peptides were tested for their antimicrobial activity against Escherichia coli ATCC 25922 and Staphylococcus aureus ATCC 29213 for their minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values. Porcine red blood cells (RBCs) were used to test the hemolytic activity of the selected peptides for their hemolytic concentration (HC50) values. Data is presented as the lowest effective peptide concentration range (μg/mL) observed in three independent experiments performed in duplicate, with one maximum data point and one minimum data point dropped for each measurement. The top section shows results of the 28 successfully synthesized peptides among the top 30 peptides in structural similarities to known chicken AMPs as measured by TM-scores, while the second section shows results of the 10 least structurally similar ones to known chicken AMPs. Results of the three reference chicken AMPs (Chicken CATH-2, Chicken CATH-3, and Ovipin) for the top 30 peptides are listed in the third section for comparison. The control peptides in the bottom section includes: a positive control peptide Ranatuerin-4 [153] and a negative control peptide OT15. Peptide name Antimicrobial susceptibility testing Hemolysis testinga E. coli ATCC 25922 S. aureus ATCC 29213 Porcine RBCs MIC (μg/mL) MBC (μg/mL) MIC (μg/mL) MBC (μg/mL) HC50 (μg/mL) DaRe1 32 – 64 32 – 64 >128 >128 >128 ScPo1 >128 >128 >128 >128 — ArTh10 >128 >128 >128 >128 — HoSa1 16 16 – 32 128 —* >128 HoSa2 >128 >128 >128 >128 — DiVi1 >128 >128 >128 >128 — DiDi2 16 16 >128 >128 >128 LySt2 >128 >128 >128 >128 — MuMu1 ≥128 —* >128 >128 — CaGl1 >128 >128 >128 >128 — HoSa3 >128 >128 >128 >128 — BoMo2 >128 >128 >128 >128 — ScPo2 >128 >128 >128 >128 — DiDi1 >128 >128 >128 >128 — RaNo1 >128 >128 >128 >128 — HoSa4 32 32 64 – 128 —* >128 GaGa2 128 —* >128 >128 >128 RaNo2 >128 >128 >128 >128 — HoSa5 >128 >128 >128 >128 — EiHe1 >128 >128 >128 >128 — PlVi1 4 – 8 8 32 32 >128 MuMu3 >128 >128 >128 >128 — SuSc1 128 —* >128 >128 >128 ArTh11 64 – 128 —* >128 >128 >128 MoIn1 64 – >128 —* >128 >128 — 144 Peptide name Antimicrobial susceptibility testing Hemolysis testinga E. coli ATCC 25922 S. aureus ATCC 29213 Porcine RBCs MIC (μg/mL) MBC (μg/mL) MIC (μg/mL) MBC (μg/mL) HC50 (μg/mL) HoSa6 >128 >128 >128 >128 — CaGl2 >128 >128 >128 >128 — HoSa7 64 – >128 —* >128 >128 — GaGa4 >128 >128 >128 >128 — OrSa6 >128 >128 >128 >128 — GoGo1 64 —* >128 >128 >128 HoSa8 >128 >128 >128 >128 — HoSa9 >128 >128 >128 >128 — DrMe6 >128 >128 >128 >128 — MuMu4 >128 >128 >128 >128 — HoSa10 >128 >128 >128 >128 — UnBi1 128 —* >128 >128 >128 ScPo3 >128 >128 >128 >128 >128 Chicken CATH-2 8 – 16 8 – 16 32 32 >128 Chicken CATH-3 2 – 4 2 – 4 2 2 – 4 >128 Ovipin >128 >128 >128 >128 >128 Ranatuerin-4 4 4 1 – 2 2 16 OT15b >128 >128 >128 >128 >128 a Hemolysis experiments were not performed for putative AMPs that showed no antimicrobial activity (MIC > 128 μg/mL) in at least two repeats for each bacterial strain tested. b OT15 (TKPKGTKPKGTKPKG) is a truncated form of a negative control peptide OT20 [154] used in previous studies. * MBC values were not tested for experiments revealing antimicrobial activity but with high MIC values of ≥ 64 μg/mL. ‘—’ = not tested. 145 B.2 Supplementary figures Figure B.1 AMP mining workflow. The AMP mining workflow utilizes the rAMPage [32] cleavage module with ProP [142] to cleave putative precursor sequences, and AMPlify to predict AMP sequences. The numbers of candidate mature sequences, candidate mature sequence records, parent sequences, and UniProt entries, that remained at each step are reported. We note that two candidate mature sequence records are considered distinct from each other if any of these three attributes are different: UniProt entry ID, position in the parent sequence, and candidate mature sequence. All numbers presented in the workflow are non-redundant. Step 1Step 4Step 5Step 3Step 2Eukaryotic sequencesfrom UniProtKB/Swiss-Prot:186,302 sequences from 195,188 entriesIn silico cleavage(rAMPage cleavage modulewith ProP)Remove sequences without signal peptides predicted390,304 mature sequenceswith 439,362 recordsfrom 131,148 parent sequencesfrom 138,531 entries74,426 mature sequenceswith 86,596 recordsfrom 37,284 parent sequencesfrom 39,466 entriesAMP prediction(AMPlify imbalanced model)15,614 mature sequenceswith 19,399 recordsfrom 12,160 parent sequencesfrom 12,954 entriesAMP prediction(AMPlify balanced model)10,720 mature sequenceswith 12,791 recordsfrom 8,481 parent sequencesfrom 8,862 entriesRemove known AMPs and sequences annotated with related keywords8,008 mature sequenceswith 9,550 recordsfrom 6,349 parent sequencesfrom 6,654 entriesPredicted AMPsNovel putative AMPs 146 Figure B.2 Length and net charge distributions of the AMPs predicted by AMPlify from the UniProtKB/Swiss-Prot database. Length and net charge distributions were calculated for all 10,720 predicted AMPs as well as the 8,008 novel putative AMPs among them. The distributions of the 4,538 known AMP sequences from Antimicrobial Peptide Database (APD3) [17] and Database of Anuran Defense Peptides (DADP) [110] were plotted alongside for comparison. Mean (μ) and standard deviation (σ) values of each distribution are as follows: all predicted AMPs (length: μ = 52.83 aa, σ = 26.59 aa; net charge: μ= 3.02, σ = 3.90), novel putative AMPs (length: μ = 52.49 aa, σ = 26.49 aa; net charge: μ = 3.04, σ = 3.88), and known AMPs (length: μ = 30.21 aa, σ = 20.28 aa; net charge: μ = 3.05, σ = 3.10). 147 Figure B.3 Structural similarity distributions of the short cationic novel putative AMPs mined from the UniProtKB/Swiss-Prot database to known chicken AMPs. The structural similarity distribution of 1,596 short cationic novel putative AMPs to known chicken AMPs holds a mean of 0.4266 and a standard deviation of 0.1690. Among all short cationic novel putative AMPs, 397 of them are with AMPlify scores ≥ 10 (i.e., AMPlify probability scores ≥ 0.9). The structural similarity distribution of these 397 putative AMPs to known chicken AMPs holds a mean of 0.4434 and a standard deviation of 0.1611. The structural similarity (TM-score) of each predicted AMP to known chicken AMPs was considered as the structural similarity of the predicted AMP to the most structurally similar known chicken AMP (i.e., reference chicken AMP) from Antimicrobial Peptide Database (APD3) [17], based on which the distributions were plotted. 148 Figure B.4 Visualization of antimicrobial activity of the 38 tested putative AMPs mined from the UniProtKB/Swiss-Prot database with respect to AMPlify scores and associated structural similarities to known chicken AMPs. Peptides without any observable antimicrobial activity are presented as grey crosses, and the active peptides are presented in blue dots with their names annotated. Dots with darker colors indicate stronger antimicrobial activity against Escherichia coli ATCC 25922, determined by the lowest minimum inhibitory concentration (MIC) value of each peptide against the strain. AMPlify scores from the balanced model were used for visualization. The structural similarity (TM-score) of each tested peptide to known chicken AMPs was considered as the structural similarity of that peptide to the most structurally similar known chicken AMP (i.e., reference chicken AMP) from Antimicrobial Peptide Database (APD3) [17]. PlVi1HoSa1DiDi2DaRe1HoSa4ArTh11MoIn1HoSa7MuMu1GaGa2SuSc1GoGo1UnBi1 149 Appendix C Chapter 4 supplementary materials This appendix contains supplementary information for Chapter 4. C.1 Supplementary tables Table C.1 Putative AMP sequences generated by AMPd-Up that have been prioritized for synthesis. Lists A, B, and C include 38, 4, and 16 sequences, respectively. All sequences in Lists A and C were predicted as AMPs by AMPlify, while those in List B were predicted as non-AMPs. Sequences were sampled from all candidate peptide sequences generated by 1,000 model instances, with incomplete sequences removed. Sequences in Lists A and B were sampled through AMPd-Up scores, while List C comprises a set of sequences that appear with high frequency (≥ 40 in sequence counts) in all candidate peptides. The numbering of peptide names for Lists A and B was by AMPlify score, while List C was by sequence count, both in descending order. List Peptide name Sequence # aa Net chargea MW (Da) AMPd-Up scoreb AMPlify scorec Countd Sequence similarity to knowne (%) A DeNo1001 DLLSGLGKAAKKVAKTVLKNLLKC 24 5 2512.12 0.2362 80.00 1 45.83 DeNo1002 NLLDTLKNLAKKLAKKLLKKLLKKL 25 8 2889.71 0.2626 80.00 1 51.61 DeNo1003 NLLSTLLDAAKKAAKGAAKSAAKKLAKKLAKKL 33 9 3364.14 0.2679 80.00 1 48.48 DeNo1004 HLLSGLLSAAKKAAKKAAKKALKKLLKKLLKKL 33 12 3553.57 0.2891 80.00 1 45.45 DeNo1005 GLFSLLKKLLKKLLKKLLKKLLKKLLKKL 29 12 3431.61 0.2942 80.00 1 58.62 DeNo1006 NLLDTLKKKAKKVAKKVLKKLLKKLLKKL 29 12 3373.40 0.3150 80.00 1 48.48 DeNo1007 FLPSIIKGAAKKLPKIFCKILKKC 24 7 2688.49 0.3313 80.00 1 75.00 DeNo1008 GLLSLLKKLLKKLLKKLLKKL 21 8 2432.27 0.2949 80.00 11 66.67 DeNo1009 DLLKTLGKAAKKAAKTALKAALKGLLKKLAKKL 33 10 3446.37 0.2467 69.24 1 45.45 150 List Peptide name Sequence # aa Net chargea MW (Da) AMPd-Up scoreb AMPlify scorec Countd Sequence similarity to knowne (%) DeNo1010 VLGGLLKKLLKKLLKKL 17 6 1905.55 0.2520 69.24 1 58.82 DeNo1011 HLLSLLKKAAKKLLKKLLKKLAKKL 25 10 2868.78 0.3049 69.24 1 48.00 DeNo1012 CLLDTLKCVAKGVAGTLLDTLKCKITGKC 29 3 3009.73 0.2732 67.48 1 70.00 DeNo1013 KIFGKILKKLLKKLLKKLLKKL 22 10 2635.55 0.2995 64.47 1 54.55 DeNo1014 ALPSLLKKLAKKLAKKLLKKLLKKLLKKLLKKL 33 14 3794.08 0.3100 64.47 1 48.48 DeNo1015 NLLDTLKNVAKNVAKNVLDTLKCKITCKC 29 4 3204.89 0.3346 64.47 1 65.52 DeNo1016 FLPIIAGLAAKFLPKIFCKITKKC 24 5 2664.42 0.3579 64.47 1 87.50 DeNo1017 FLPIIAGLAAKLLPKLFCKITKKC 24 5 2630.41 0.3519 60.79 1 87.50 DeNo1018 WLPKIAGKIAGKLLKKLLKKIKKK 24 10 2744.60 0.2573 60.21 1 50.00 DeNo1019 FLPKIAGKAAKKLPKIFCKITKKC 24 8 2675.45 0.3314 58.82 2 83.33 DeNo1020 TLPDVAKNVAKNVAKTVLDTLKCKITGKC 29 4 3072.70 0.3259 58.45 1 75.86 DeNo1021 KLFGKLLKKLKKILKKIAKKIKKKL 25 13 2977.99 0.2309 57.62 1 44.83 DeNo1022 GLLSLLKKIGKKIGKLL 17 5 1822.38 0.2415 55.72 1 58.82 DeNo1023 DLLKTLKKIAKKLLKTLLKKLLKKLLKKL 29 11 3415.52 0.2838 53.67 1 48.28 151 List Peptide name Sequence # aa Net chargea MW (Da) AMPd-Up scoreb AMPlify scorec Countd Sequence similarity to knowne (%) DeNo1024 KLFGKILGKIAKKILGKILGALLSKLLSAL 30 7 3149.05 0.2150 53.22 1 46.67 DeNo1025 DLLSCLKKKGKCVLKNL 17 4 1903.41 0.1885 49.48 1 47.06 DeNo1026 RLPSLFKKLFKKIAKVVGKIAKKILKK 27 11 3124.05 0.2203 48.34 1 45.71 DeNo1027 RLPSIIPGIAGKLGGLLGGLLKGL 24 3 2313.88 0.2044 43.84 1 54.17 DeNo1028 CLPSLLPSLFKKL 13 2 1458.86 0.1713 34.88 3 53.85 DeNo1029 SLPSILSGIAGKL 13 1 1255.51 0.1991 32.75 1 61.54 DeNo1030 RLPRIFRGIRGKL 13 5 1581.96 0.1674 31.93 1 46.15 DeNo1031 PLPPIIPGIAGKLLGGLLGLLKKL 24 3 2392.07 0.2256 31.81 1 54.17 DeNo1032 YLPSVLPSVLKPL 13 1 1425.76 0.1779 31.78 1 53.85 DeNo1033 PLPPIIPGLASGLLSGLC 18 0 1718.12 0.1938 24.72 1 61.11 DeNo1034 KLPSIIKAAAKALPKLF 17 4 1809.30 0.2097 20.81 1 52.94 DeNo1035 QLPRIAGKIAKKL 13 4 1435.81 0.1833 17.41 1 61.54 DeNo1036 QLPSVLPAIAKAL 13 1 1320.63 0.1609 9.02 1 53.85 DeNo1037 CLPSILC 7 0 747.97 0.1462 7.88 1 41.67 DeNo1038 MLPSIAGAAAKGLPKLFCKITKKC 24 5 2490.16 0.2784 3.88 1 79.17 B DeNo1039 MLPKIFGKIFKKILKKILKKILKKILKKLLKKL 33 14 3976.37 0.3091 1.32 1 42.42 DeNo1040 MLPSILGALLKLL 13 1 1381.82 0.2000 0.83 3 61.54 DeNo1041 MLPKIAGKIAKKL 13 4 1410.86 0.2210 0.80 62 61.54 DeNo1042 MLPKIAGAIAKLL 13 2 1338.75 0.2080 0.77 8 61.54 Cf DeNo1043 WLPKIAGKIAGKL 13 3 1394.75 0.2202 19.83 95 61.54 152 List Peptide name Sequence # aa Net chargea MW (Da) AMPd-Up scoreb AMPlify scorec Countd Sequence similarity to knowne (%) DeNo1044 CLPSILCKITKKC 13 3 1449.90 0.2147 35.64 90 53.85 DeNo1045 FLPKIFKKIAKKL 13 5 1574.06 0.2659 25.78 77 61.54 DeNo1046 VLGSLLKGLLKKL 13 3 1381.80 0.2240 80.00 65 61.54 DeNo1047 ALPSIIKGLLKKL 13 3 1393.81 0.2104 53.55 63 53.85 DeNo1048 LLPSLLKGLLKKL 13 3 1435.89 0.2282 56.01 58 61.54 DeNo1049 ALLSLLKKLLKKL 13 4 1480.97 0.2426 69.24 56 61.54 DeNo1050 FLPKIAGKIAGKL 13 3 1355.72 0.2400 10.81 55 69.23 DeNo1051 ALPSLLKKLLKKL 13 4 1464.93 0.2259 54.39 50 53.85 DeNo1052 YLPSVLKGLLKKL 13 3 1471.88 0.2032 46.71 48 53.85 DeNo1053 LLPSLLKGLAKKL 13 3 1393.81 0.2267 52.80 47 61.54 DeNo1054 QLPKIAGKIAKKL 13 4 1407.79 0.2159 12.97 47 61.54 DeNo1055 FLPKIFKKIAKKI 13 5 1574.06 0.2514 39.72 46 55.56 DeNo1056 GLLSLLKKLLKKL 13 4 1466.94 0.2490 80.00 43 69.23 DeNo1057 ILGKLLKKLLKKL 13 5 1508.04 0.2325 51.38 40 61.54 DeNo1058 FLPKIAGKIAKKL 13 4 1426.84 0.2474 19.60 40 69.23 a Net charge at pH = 7. b AMPd-Up scores range from 0 to 1; average AMPd-Up score was reported for same sequence generated by multiple model instances. c The AMPlify balanced model was utilized; AMPlify scores range from 0 to 80; sequences with AMPlify scores > 3.01 (i.e., AMPlify probability scores > 0.5) are predicted as AMPs. d Frequency of the sequence appearing in the generated set. e Sequence similarity to the most similar known AMP sequence from Antimicrobial Peptide Database (APD3, downloaded on July 11, 2022) [17] and Database of Anuran Defense Peptides (DADP, downloaded on December 6, 2018) [110]. f Sequences with shorter lengths (≤ 20 aa) but higher net charges (≥ +3) were prioritized for List C. 153 Table C.2 Antimicrobial susceptibility testing and hemolysis experiment results of the 58 selected de novo designed putative AMPs in vitro. Peptides were tested for their antimicrobial activity against Escherichia coli ATCC 25922 and Staphylococcus aureus ATCC 29213 for their minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values. Porcine red blood cells (RBCs) were used to test the hemolytic activity of the selected peptides for their hemolytic concentration (HC50) values. Data is presented as the lowest effective peptide concentration range (μg/mL) observed in three independent experiments performed in duplicate, with one maximum data point and one minimum data point dropped for each measurement. Ranateurin-4 [153] and OT15 were used as the positive and negative control peptides, respectively. List Peptide name Antimicrobial susceptibility testing Hemolysis testing E. coli ATCC 25922 S. aureus ATCC 29213 Porcine RBCs MIC (μg/mL) MBC (μg/mL) MIC (μg/mL) MBC (μg/mL) HC50 (μg/mL) DeNo1001 2 – 4 2 – 4 32 – 128 64 – >128 >128 DeNo1002 2 – 4 2 – 4 64 – 128 64 – >128 128 DeNo1003 2 – 4 2 – 4 ≥128 ≥128 >128 DeNo1004 64 64 >128 >128 >128 DeNo1005 >128 >128 >128 >128 >128 DeNo1006 8 – 16 16 – >32 >128 >128 >128 DeNo1007 4 4 – 8 4 4 – 8 >128 DeNo1008 >128 >128 >128 >128 16 – 32 DeNo1009 8 – 16 8 – >128 >128 >128 >128 A DeNo1010 4 4 – 8 32 – 64 32 – 128 64 – 128 DeNo1011 32 32 >128 >128 32 DeNo1012 32 64 >128 >128 >128 DeNo1013 16 16 – 32 >128 >128 64 DeNo1014 64 64 >128 >128 ≥128 DeNo1015 128 128 >128 >128 >128 DeNo1016 4 4 2 – 4 2 – 8 4 – 8 DeNo1017 4 4 2 – 4 2 – 4 16 DeNo1018 1 – 2 2 – 4 8 8 128 DeNo1019 16 16 >128 >128 >128 154 List Peptide name Antimicrobial susceptibility testing Hemolysis testing E. coli ATCC 25922 S. aureus ATCC 29213 Porcine RBCs MIC (μg/mL) MBC (μg/mL) MIC (μg/mL) MBC (μg/mL) HC50 (μg/mL) DeNo1020 64 – 128 64 – 128 >128 >128 >128 DeNo1021 8 8 – 16 32 – 64 32 – 128 >128 DeNo1022 4 16 – >128 4 4 – 16 ≥128 DeNo1023 128 128 >128 >128 >128 DeNo1024 ≥128 ≥128 >128 >128 >128 DeNo1025 >128 >128 >128 >128 >128 DeNo1026 16 – 32 32 – 64 ≥128 ≥128 ≥128 DeNo1027 32 32 – 64 >128 >128 >128 DeNo1028 >128 >128 >128 >128 >128 DeNo1029 >128 >128 >128 >128 >128 DeNo1030 128 ≥128 >128 >128 >128 DeNo1031 8 – 16 16 16 32 – >64 128 DeNo1032 >128 >128 >128 >128 >128 DeNo1033 >128 >128 >128 >128 >128 DeNo1034 16 – 32 16 – 64 >128 >128 >128 DeNo1035 >128 >128 >128 >128 >128 DeNo1036 >128 >128 >128 >128 >128 DeNo1037 >128 >128 >128 >128 >128 DeNo1038 64 64 – 128 >128 >128 >128 DeNo1039 >128 >128 >128 >128 32 – 64 B DeNo1040 64 – 128 ≥128 64 64 – 128 >128 DeNo1041 >128 >128 >128 >128 >128 DeNo1042 >128 >128 >128 >128 >128 155 List Peptide name Antimicrobial susceptibility testing Hemolysis testing E. coli ATCC 25922 S. aureus ATCC 29213 Porcine RBCs MIC (μg/mL) MBC (μg/mL) MIC (μg/mL) MBC (μg/mL) HC50 (μg/mL) DeNo1043 >128 >128 >128 >128 >128 DeNo1044 ≥128 ≥128 >128 >128 >128 DeNo1045 64 64 – 128 >128 >128 >128 DeNo1046 16 – 64 16 – 64 128 128 >128 DeNo1047 ≥128 ≥128 >128 >128 >128 DeNo1048 128 128 >128 >128 >128 DeNo1049 4 – 8 4 – 16 >128 >128 >128 C DeNo1050 >128 >128 >128 >128 >128 DeNo1051 16 – 32 16 – 64 >128 >128 >128 DeNo1052 ≥128 ≥128 >128 >128 >128 DeNo1053 >128 >128 >128 >128 >128 DeNo1054 >128 >128 >128 >128 >128 DeNo1055 ≥128 ≥128 >128 >128 >128 DeNo1056 32 – >128 32 – >128 >128 >128 >128 DeNo1057 8 – 16 8 – 32 32 32 – 128 >128 DeNo1058 >128 >128 >128 >128 >128 Controls Ranateurin-4a 8 – 16 8 – 16 2 – 4 4 64 – 128 OT15b >128 >128 >128 >128 >128 a Ranateurin-4 in this set of validation experiments is from a different synthesis batch than that in Table B.4. b OT15 (TKPKGTKPKGTKPKG) is a truncated form of a negative control peptide OT20 [154] used in previous studies. 156 C.2 Supplementary figures Figure C.1 Sequence similarity distribution of the de novo generated sequences to known AMPs. The known AMP sequence set comprises 4,538 distinct sequences downloaded from Antimicrobial Peptide Database (APD3) [17] and Database of Anuran Defense Peptides (DADP) [110]. The sequence similarity distribution, with a mean of 51.03% and a standard deviation of 9.38%, was calculated based on the 2,000 sequences generated by AMPd-Up. The sequence similarity of each generated sequence to known AMPs was considered as the similarity of that sequence to its most similar known AMP sequence, based on which the distribution was plotted. 157 Figure C.2 Distributions of pairwise sequence similarities between different sequence sets in the de novo AMP design study. The pairwise sequence similarities between two different sets of sequences were calculated as the similarities of all sequence pairs between the two sets (e.g., Generated vs. Training), while the pairwise sequence similarities of the same set of sequences (i.e., sequence diversity measurements) were defined as the similarities of sequences to each other in the set (e.g., Generated vs. Generated). The intra-model sequence similarities were calculated as the similarities of sequences generated by the same model instance to each other, while inter-model sequence similarities were calculated as pairwise sequence similarities between sets of sequences generated by different model instances. A set of 2,000 random sequences matching the length distribution of the 2,000 generated sequences were added for comparison, in addition to the training and generated sequence sets. Mean (μ) and standard deviation (σ) values of each distribution are as follows: Random vs. Random (μ = 13.71%, σ = 5.09%), Random vs. Generated (μ = 10.34%, σ = 5.28%), Random vs. Training (μ = 13.76%, σ = 4.91%), Training vs. Training (μ = 18.06%, σ = 7.92%), Generated vs. Training (μ = 18.80%, σ = 8.94%), Generated vs. Generated (μ = 33.61%, σ = 16.18%), Intra-model (μ = 39.14%, σ = 19.79%), and Inter-model (μ = 33.56%, σ = 16.14%). Two-sided Kolmogorov-Smirnov tests reveal that the difference between any two of the distributions is significant (p < 0.0018), except that between Generated vs. Generated and Inter-model (p = 0.0519). We note that 0.0018 is an adjusted alpha level calculated with Šidák correction [174] from a family-wise alpha level of 0.05 for the multiple comparisons.
Thesis/Dissertation
eng
Bioinformatics
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Birol, Inanc
Machine learning for antimicrobial peptide discovery and design
Text