Science, Faculty of
DSpace
UBCV
Radaeva, Mariia
Doctor of Philosophy - PhD
University of British Columbia
In the realm of the development of cancer therapeutics, a significant portion of the human proteome, including over 1600 transcription factors (TFs), is classified as "undruggable". These proteins, often being critical drivers in various cancers, present unique challenges due to their intricate nature and complex interactions with nucleic acids. Despite these obstacles, they offer a very significant therapeutic potential, necessitating innovative approaches for their exploitation in cancer treatment. This thesis showcases the development and application of Computer-Aided Drug Discovery (CADD) in overcoming the challenges associated with targeting proteins that have been previously deemed âundruggableâ. Through detailed case studies, it reveals the strategic use of CADD in three distinct scenarios: - For the androgen receptor (AR), CADD leverages the existing experimental data to design novel inhibitors with unique chemotypes; - in case of the RNA-binding protein Lin28, CADD orchestrates the entire drug discovery process, from the identification of the binding site to the optimization of lead compounds; - for targeting HOXB13, a relatively uncharted factor, CADD facilitates navigating selectivity challenges, showcasing the adaptability of these tools in unlocking previously unattainable drug targets. Collectively, these three projects illustrate how CADD can assist in the development of inhibitors that disrupt of protein-nucleic acid interactions across various protein families and stages of drug discovery and addressing numerous CADD challenges. Additionally, this work demonstrates that the utility of CADD is deeply intertwined with experimental validation. By seamlessly integrating computational modeling with experimental workflow, this thesis not only introduces novel methodologies for targeting challenging proteins involved into transcription but also highlights the synergistic potential of advancements in both computational and experimental domains. The outcomes of this research extend the horizons of drug discovery, opening up new avenues for cancer treatment and accelerating the development of innovative therapeutic strategies.
DEVELOPMENT OF SMALL MOLECULE INHIBITORS OF PROTEIN NUCLEIC ACID INTERACTIONS WITH THE USE OF COMPUTER-AIDED DRUG DISCOVERY TOOLS by Mariia Radaeva B.Sc., University of Bath, 2018 M.Sc., University of British Columbia, 2020 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) August 2024 © Mariia Radaeva, 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: Development of Small molecule Inhibitors of Protein Nucleic Acid Interactions With The Use Of Computer-Aided Drug Discovery Tools submitted by Mariia Radaeva in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Bioinformatics Examining Committee: Artem Cherkasov, Professor, Bioinformatics, UBC Supervisor Xuesen Dong, Associate Professor, Urologic Sciences, UBC Co-Supervisor Ivan Sadowski, Professor, Biochemistry and Molecular Biology, UBC University Examiner Anotida Madzvamuse, Professor, Mathematics, UBC Anotida Madzvamuse University Examiner Marco De Vivo, Associate Director, Computational Sciences, Istituto Italiano di Tecnologia Marco De Vivo External Examiner Additional Supervisory Committee Members: Supervisory Committee Member Additional Supervisory Committee Members: Supervisory Committee Member Additional Supervisory Committee Members: Nathan Lack, Assistant Professor, Urologic Sciences, UBC Supervisory Committee Member Supervisory Committee Member Nada Lallous, Assistant Professor, Urologic Sciences, UBC Supervisory Committee Member iii Abstract In the realm of the development of cancer therapeutics, a significant portion of the human proteome, including over 1600 transcription factors (TFs), is classified as "undruggable". These proteins, often being critical drivers in various cancers, present unique challenges due to their intricate nature and complex interactions with nucleic acids. Despite these obstacles, they offer a very significant therapeutic potential, necessitating innovative approaches for their exploitation in cancer treatment. This thesis showcases the development and application of Computer-Aided Drug Discovery (CADD) tools in overcoming the challenges associated with targeting proteins that have been previously deemed âundruggableâ. Through detailed case studies, it reveals the strategic use of CADD in three distinct scenarios: - For the androgen receptor (AR), CADD leverages the existing experimental data to design novel inhibitors with unique chemotypes; - in case of the RNA-binding protein Lin28, CADD orchestrates the entire drug discovery process, from the identification of the binding site to the optimization of lead compounds; - for targeting HOXB13, a relatively uncharted factor, CADD facilitates navigating selectivity challenges, showcasing the adaptability of these tools in unlocking previously unattainable drug targets. Collectively, these three projects illustrate how CADD can assist in the development of inhibitors that disrupt of protein-nucleic acid interactions across various protein families and stages of drug discovery and addressing numerous CADD challenges. Additionally, this work demonstrates that the utility of CADD is deeply intertwined with experimental validation. By seamlessly integrating iv computational modeling with experimental workflow, this thesis not only introduces novel methodologies for targeting challenging proteins involved into transcription but also highlights the synergistic potential of advancements in both computational and experimental domains. The outcomes of this research extend the horizons of drug discovery, opening up new avenues for cancer treatment and accelerating the development of innovative therapeutic strategies. v Lay Summary This thesis explores innovative computational methods in drug discovery, focusing on three key proteins involved in cancer: HOXB13, Lin28, and the Androgen Receptor (AR). Traditionally, these types of proteins have been challenging to target effectively due to their complex interactions with nucleic acids â DNA or RNA. By employing Computer-Aided Drug Discovery (CADD) tools, this research demonstrates new approaches to designing small molecule inhibitors. For the AR, CADD uses existing data to create new drug candidates. For Lin28, an RNA-binding protein, CADD helps in identifying where drugs can attach to the protein (binding sites) and improving the best drug candidates (leads). With HOXB13, a less studied target, CADD deals with making sure the drugs only affect the intended protein and not others (selectivity). This thesis not only presents new methodologies for targeting complex proteins but also underscores the potential of combining computational modeling with experimental research in cancer therapeutics. vi Preface 1. Chapter 1: Subsections of the Chapter were published as a review paper [Kuan J, Radaeva M, Avenido A, Cherkasov A, Gentile F. Keeping pace with the explosive growth of chemical libraries with structureâbased virtual screening. Wiley Interdisciplinary Reviews: Computational Molecular Science. 2023 Nov;13(6):e1678]. In our paper, I, Mariia Radaeva, alongside Jacqueline Kuan, contributed equally in manuscript planning, writing, conducting literature searches, data analysis, and figure creation. Adeline Avenido led in visualization and offered support in drafting the manuscript. Artem Cherkasov and Francesco Gentile equally shared duties in conceptualizing the project, administration, and supervision, with Cherkasov supporting in review and editing, and Gentile also contributing to the original draft and leading the manuscript's review and editing process. 2. Chapter 2: was published as a review paper [Radaeva M, Ton AT, Hsing M, Ban F, Cherkasov A. Drugging the âundruggableâ. Therapeutic targeting of proteinâDNA interactions with the use of computer-aided drug discovery methods. Drug discovery today. 2021 Nov 1;26(11):2660-79]. In the creation of the paper, I spearheaded its development, deciding on its contents, crafting the initial draft, and subsequent versions, as well as producing the figures. Anh-Tien Ton, Michael Hsing, and Fuqiang Ban enriched the paper by contributing their expert opinions on specific sections. Together, we collaborated to distill major conclusions and derive key insights. Artem Cherkasov played a crucial role in conceptualizing the project, overseeing its administration and supervision, and provided valuable feedback during the review and editing phases. 3. Chapter 3: The main body of the Chapter was published [Radaeva M, Morin H, Pandey M, Ban F, Guo M, LeBlanc E, Lallous N, Cherkasov A. Novel Inhibitors of androgen vii receptor's DNA binding domain identified using an ultraâlarge virtual screening. Molecular Informatics. 2023 Aug;42(8-9):2300026]. In this paper, I led the virtual screening pipeline development and execution, molecular modeling, data evaluation, authored the original draft, created figures, and managed manuscript revisions. Helene Morin conducted wet lab experiments, analyzed data, optimized experimental methodologies and assays, and prepared compounds. Mohit Pandey provided support in deep docking procedures. Fuqiang Ban contributed his expertise in computational modeling. Maria Guo and Eric LeBlanc supported with experimental results and participated in the manuscript review. Nada Lallous supervised the wet lab experiments, the corresponding data analysis and reviewed the manuscript. Artem Cherkasov was pivotal in conceptualizing the project, and handled its administration, supervision, and assisted in the review and editing process. Some contents of the chapter were published as a research paper [Radaeva M, Ban F, Zhang F, LeBlanc E, Lallous N, Rennie PS, Gleave ME, Cherkasov A. Development of novel inhibitors targeting the d-box of the dna binding domain of androgen receptor. International journal of molecular sciences. 2021 Mar 2;22(5):2493]. In this study, I played a pivotal role in the conceptualization, validation, and visualization of the research, in addition to preparing the original draft. Eric LeBlanc was instrumental in conceptualization, methodology, validation, drafting the original manuscript, and visualization. Fuqiang Ban contributed to conceptualization, methodology, validation, and also in writing the initial manuscript. Fan Zhang was involved in the development of the methodology. Resources were provided by Nada Lallous, while Artem Cherkasov, Paul S. Rennie, and Martin E. Gleave contributed resources, supervised the project, acquired funding, and Artem Cherkasov further assisted in the manuscript's review and editing. viii 4. Chapter 4: Section 4.1 partially was published as a review paper [Lin Z, Radaeva M, Cherkasov A, Dong X. Lin28 regulates cancer cell stemness for tumour progression. Cancers. 2022 Sep 24;14(19):4640]. My contributions also included reviewing tables and figures and assisting in the manuscript's writing and editing, along with project administration. Zhuohui Lin took the lead in drafting the original manuscript, preparing tables and figures. Xuesen Dong played a key role in supervision and contributed to conceptualization, review, and editing processes. Artem Cherkasov also supervised the project and participated in its administration. Section 4.2 and Section 4.3 were published as a research paper [Radaeva M, Ho CH, Xie N, Zhang S, Lee J, Liu L, Lallous N, Cherkasov A, Dong X. Discovery of Novel Lin28 inhibitors to suppress cancer cell stemness. Cancers. 2022 Nov 19;14(22):5687]. My role involved leading the virtual screening and computational modeling, as well as authoring the initial draft of the manuscript, where I encapsulated our findings on Lin28 inhibitors. Chia-Hao Ho spearheaded the experimental endeavors, performing wet lab experiments and formal analyses. Assistance in these experiments, particularly in handling compounds and assay development, was provided by Ning Xie. Both Sijie Zhang and Joseph Lee contributed to the formal analysis. Joseph Lee sa a collaborator on this study developed a protocol for the expression and purification of Lin28 protein and established binding assays to validate the direct binding of the molecules. The manuscript benefited from the review and editing by Liangliang Liu, Nada Lallous, Artem Cherkasov, and Xuesen Dong. 5. Chapter 5: I was responsible for the comprehensive computational aspect of the study, which involved a detailed druggability assessment to evaluate the potential of target sites for drug binding and intervention. I also executed molecular dynamics simulations and ix virtual screening and the generation and analysis of ADMET properties using. I also performed literature search and writing of the chapter. Ugur Meric Dikbas, from Dr. Nathan Lack's laboratory, led the experimental validation part of our project. His work involved conducting wet lab experiments to test the biological efficacy and safety of our computationally identified compounds. Specifically, Ugur performed cell culture experiments utilizing LNCaP, PC3, and HEK293t cell lines. He applied a variety of assays, including cell viability/toxicity assessments, to determine the therapeutic potential and cytotoxicity of our inhibitors. Furthermore, Ugur's role included the formal analysis of experimental data. Conclusions and discussions were a collaborative effort between me, Ugur Meric Dikbas, Artem Cherkasov, Nathan Lack and Jason Smith. x Table of Contents Abstract .......................................................................................................................................... iii Lay Summary ...................................................................................................................................v Preface............................................................................................................................................ vi Table of Contents .............................................................................................................................x List of Tables ............................................................................................................................... xiv List of Figures ................................................................................................................................xv List of Abbreviations ................................................................................................................. xviii Acknowledgements ...................................................................................................................... xix Dedication ......................................................................................................................................xx Introduction ............................................................................................................... 1 1.1 Protein-Nucleic Acid Targets in Cancer Therapy ....................................................... 1 1.2 Computer-Aided Drug Discovery ............................................................................... 2 1.3 Molecular Docking ..................................................................................................... 3 1.3.1 Ultra-Large Virtual Screening ................................................................................ 6 1.3.2 DeepDocking .......................................................................................................... 9 1.4 Pharmacophore Modelling ........................................................................................ 11 1.5 Molecular Dynamics ................................................................................................. 13 1.6 Quantitative Structure-Activity Relationship (QSAR) modeling ............................. 15 1.7 Similarity Search ....................................................................................................... 17 1.8 Integrative Approach and Consensus Scoring .......................................................... 18 Literature Review of Small Molecule Inhibitors of Protein-DNA Interactions Developed With CADD ............................................................................................................ 21 xi 2.1 Introduction ............................................................................................................... 21 2.2 ETS-related gene (ERG) ........................................................................................... 21 2.3 Signal transducer and activator of transcription 3 (STAT3) ..................................... 25 2.4 Thymocyte selection-associated high mobility group box protein (TOX) ............... 30 2.5 Myc-Max interaction with DNA ............................................................................... 33 2.6 Paired box protein 2 (Pax2) ...................................................................................... 38 2.7 Forkhead box protein M1 (FOXM1) ........................................................................ 39 2.8 Activator protein 1 (AP-1) Fos/Jun........................................................................... 40 2.9 Gli protein ................................................................................................................. 41 2.10 DNA Topoisomerase II (TopoII) .............................................................................. 44 2.11 Conclusions ............................................................................................................... 45 Targeting Androgen Receptor (AR) ........................................................................ 52 3.1 Introduction ............................................................................................................... 52 3.1.1 AR Structure and Function ................................................................................... 53 3.1.2 ARâs Role in Prostate Cancer ............................................................................... 54 3.1.3 Current AR Targeting Drugs and Novel Mechanisms of Inhibition..................... 55 3.2 CADD of Novel AR Inhibitors ................................................................................. 60 3.2.1 Docking Benchmarking ........................................................................................ 60 3.2.2 Virtual Screening Pipeline .................................................................................... 64 3.3 Experimental Evaluation of AR Inhibitors ............................................................... 67 3.4 Discussions ............................................................................................................... 71 3.5 Conclusions ............................................................................................................... 74 3.6 Materials and Methods .............................................................................................. 75 xii Targeting Lin28 with the use of CADD methodology............................................ 78 4.1 Introduction ............................................................................................................... 78 4.1.1 Lin28 Structure and Function ............................................................................... 79 4.1.2 Let-7 Biogenesis and Function ............................................................................. 81 4.1.3 Lin28/Let-7 Axis Cancer ...................................................................................... 83 4.1.4 Inhibition of Lin28 with Small Molecules ............................................................ 88 4.2 CADD-based discovery of Novel Lin28 Inhibitors .................................................. 91 4.2.1 Identifying Druggable Lin28 Pockets ................................................................... 91 4.2.2 Virtual Screening for Lin28B ZKD Inhibitors...................................................... 92 4.3 Experimental Evaluation of Lead Lin28 Inhibitors .................................................. 94 4.3.1 Activity in Protein-Based Assays ......................................................................... 94 4.3.2 Cancer Cell Stemness Reduction .......................................................................... 98 4.4 Optimization of Ln7, Ln15 and Ln115 lead compounds ........................................ 101 4.4.1 Nucleobase-Inspired Design ............................................................................... 102 4.4.2 Structure-Activity Relationship (SAR) Optimization ......................................... 103 4.5 Experimental Evaluation of Optimized Derivatives of Lin28 Inhibitors ................ 107 4.5.1 Activity in Protein-Based Assays ....................................................................... 107 4.5.2 Inhibitory Effects on Cancer Cells ...................................................................... 111 4.5.3 Ln268's Efficacy in a Prostate Cancer Models ................................................... 112 4.6 Discussions ............................................................................................................. 115 4.7 Conclusions ............................................................................................................. 119 4.8 Materials and Methods ............................................................................................ 120 HOXB13 target ..................................................................................................... 124 xiii 5.1 Introduction ............................................................................................................. 124 5.1.1 HOXB13 Structure and Function........................................................................ 124 5.1.2 HOXB13âs Role in Prostate Cancer ................................................................... 125 5.2 CADD of Novel HOXB13 Inhibitors ..................................................................... 128 5.2.1 Identifying HOXB13 Potential Binding Sites..................................................... 128 5.2.2 Exploring Pocket Flexibility and Selectivity ...................................................... 129 5.2.3 Virtual Screening for HOXB13 Inhibitors .......................................................... 132 5.3 Experimental Evaluation of HOXB13 Inhibitors ................................................... 135 5.3.1 Inhibitory Effects on Cancer Cell Growth .......................................................... 136 5.4 Discussions and Future Directions.......................................................................... 137 5.5 Conclusions ............................................................................................................. 140 5.6 Materials and Methods ............................................................................................ 141 Conclusions ........................................................................................................... 145 Bibliography ................................................................................................................................148 xiv List of Tables Table 2-1. Binding site druggability assessment. ......................................................................... 48 Table 3-1. List of active AR DBD P-box inhibitors used for docking benchmarking. ................ 61 Table 3-2. List of active AR DBD D-box inhibitors used for docking benchmarking ................. 62 Table 4-1. Lin28 Roles in Cancers. ............................................................................................ 83 Table 4-2. Lin28 Inhibitors. .......................................................................................................... 88 Table 4-3. Table of analogs of Ln115 designed with nucleobase-inspired design. .................... 103 Table 4-4. Analogs of Ln15 used to derive SAR ........................................................................ 104 Table 4-5. IC50 values of Lin28 inhibitors (uM) ....................................................................... 110 Table 4-6. Microsomal stability profile of Lin28 inhibitors ....................................................... 114 xv List of Figures Figure 1-1. Molecular docking example. ........................................................................................ 4 Figure 1-2. ZINC database growth over years ................................................................................ 7 Figure 1-3. Structure-based methods for ultra-large virtual screening ........................................... 8 Figure 1-4. DeepDocking Workflow. ........................................................................................... 10 Figure 1-5. An example of a pharmacophore mode. .................................................................... 12 Figure 1-6. A standard QSAR workflow. ..................................................................................... 15 Figure 1-7. Examples of molecular descriptors. ........................................................................... 18 Figure 2-1. Compound VPC-18005 targeting the ETS-related gene (ERG)âDNA interaction site........................................................................................................................................................ 24 Figure 2-2. Compound InS3-54 targeting the STAT3âDNA interaction site. .............................. 29 Figure 2-3. Compound VPC-190444 targeting thymocyte selection-associated high mobility group box protein (TOX)âDNA interaction site. .......................................................................... 32 Figure 2-4. Compound VPC-70812 targeting the N-MycâMaxâDNA interaction site. ............... 34 Figure 2-5. Compound T-52247 targeting activator protein 1 (AP-1) Fos/Jun-DNA interaction site. ................................................................................................................................................ 41 Figure 2-6. Compound Glabrescione B (GlaB) targeting Gli1âDNA interaction site. ............... 43 Figure 2-7. Compound T60 targeting topoisomerase II (TOP2)âDNA interaction site. .............. 45 Figure 3-1. AR DBD structure and druggable pockets. ................................................................ 54 Figure 3-2. Compound VPC-14449 targeting the AR (AR)âDNA interaction site. ..................... 58 Figure 3-3. Docking Software Benchmarking for P-box (left) and D-box (right) of AR DBD. .. 64 Figure 3-4. In silico screening against P-box and D-box on the surface of AR DBD. ................. 67 xvi Figure 3-5. Identified hits, VPC-14668 and VPC-17493, inhibit transcriptional activity of AR full-length and V7. ........................................................................................................................ 70 Figure 3-6. Structural information and associated Tanimoto scores of closest analogs of the selected hit compounds among all reported AR inhibitors (sourced from CHEMBL) and our published inhibitors. ...................................................................................................................... 72 Figure 3-7. Ligand-protein interactions of VPC-14668 (A) and VPC-17493 (B). ....................... 74 Figure 4-1. Overview of the CADD timeline targeting Lin28. ..................................................... 79 Figure 4-2. Domain structure and crystal structure of Lin28. ....................................................... 80 Figure 4-3. The Biogenesis of let-7 microRNA............................................................................ 82 Figure 4-4. CADD pipeline of ZKD-Lin28B protein inhibitors. .................................................. 93 Figure 4-5. FP screen of 163 compounds. .................................................................................... 95 Figure 4-6. Direct binding and dose-dependent assays of the selected compounds. .................... 96 Figure 4-7. Selected inhibitors restore let-7d levels and downregulate SOX2 and HMGA2 genes........................................................................................................................................................ 97 Figure 4-8. Selected ZKD-Lin28B inhibitors regulate stem-like pluripotency gene network and neuroendocrine markers in t-NEPC cells. ................................................................................... 100 Figure 4-9. Structure-activity relationship guided design of Ln15 and Ln250........................... 105 Figure 4-10. Derivatives screening by fluorescent polarization. ................................................ 108 Figure 4-11. Derivatives screening by Electrophoretic mobility sift assay. ............................... 109 Figure 4-12. Derivatives screening in let-7 miRNA and Lin28b expression assays and cell proliferation assay. ...................................................................................................................... 112 Figure 4-13. Ln268 inhibits the growth of a model of neuroendocrine prostate cancer spheroids growing in 3D culture. ................................................................................................................ 113 xvii Figure 4-14. Predicted docking pose of Ln267 and Ln268. ....................................................... 118 Figure 5-1. Structural homology of HOX members. .................................................................. 125 Figure 5-2. HOXB13 is selectively essential to PCa. ................................................................. 126 Figure 5-3. The identified binding site on the surface of HOXB13. .......................................... 129 Figure 5-4. Selectivity analysis of HOXB13 targeting. .............................................................. 130 Figure 5-5. 500ns MD simulations on HOXB13 and its closest analog HOXC13..................... 132 Figure 5-6.VS pipeline to identify selective inhibitors of HOXB13 .......................................... 133 Figure 5-7. Screening Pipeline for Selective HOXB13 Inhibitor Evaluation. ............................ 135 Figure 5-8. Cell viability response to HOXB13-targeted inhibitors across cell lines. ............... 137 Figure 5-9. Comparison of binding interactions of HOXB13 with DNA and molecule 152. .... 139 xviii List of Abbreviations ADMET: Absorption, Distribution, Metabolism, Excretion, Toxicity AR: Androgen Receptor CADD: Computer-Aided Drug Design HTS: High Throughput Screening IC50: Concentration at 50% Inhibition ICM: Internal Coordinate Mechanics MOE: Molecular Operating Environment PDB: Protein Data Bank PC: Prostate Cancer PLB: Propensity for Ligand Binding QSAR: Quantitative Structure-Activity Relationship RMSD: Root Mean Square Deviation SAR: Structure-Activity Relationship VS: Virtual Screening ZINC: Zinc Is Not Commercial ZKD: Zinc Knuckle Domain xix Acknowledgements I extend my heartfelt gratitude to my mentor, Dr. Artem Cherkasov, for his invaluable support, wisdom, and the remarkable opportunities he provided throughout my academic journey. His mentorship has been pivotal in shaping my research and career. My appreciation also goes to my co-supervisor, Dr. Xuesen Dong, whose expertise in biology and steadfast optimism have significantly contributed to the advancement of our projects. I am thankful to my committee members, Dr. Nathan Lack and Dr. Nada Lallous, for their insightful feedback and consistent guidance. A special shoutout to my lab colleagues - Dr. Micheal Hsing, Dr. Fuqiang Ban, Mohit Pandey, Jiaying You, Olivia Garland, Hazem MsLati, and Kate Manskaia - for creating an inspiring and supportive work environment. A particular thanks to Dr. Jason Smith for his dedication to keeping our lab operations running seamlessly. I'm deeply grateful to my friends and family for their unwavering support and encouragement, which kept me motivated and focused. A special note of thanks to my best friend, Diana Malynovska, for being by my side through every challenge and triumph. Lastly, I acknowledge the financial support from the PCFBC Grant-in-Aid scholarship and the educational guidance from the UBC Bioinformatics graduate program. Also, I would like to acknowledge Advanced machine learning for Innovative Drug Discovery (AIDD) consortium for support and valuable connections throughout my studies. xx Dedication I dedicate this doctoral thesis to my late grandmothers, Mariia Radaeva and Nadezhda Isaeva, who were both esteemed medical doctors while staying the most caring and loving parents. Your legacy continues to inspire and guide me.1 Introduction 1.1 Protein-Nucleic Acid Targets in Cancer Therapy Proteins that bind DNA or RNA molecules play an important role in cell biology acting as transcription factors (TFs), post-transcriptional regulators or enzymes. One major therapeutic class of DNA-interacting proteins is transcription factors that recognize specific DNA sequences to regulate protein expression in response to cellular stimuli. There are over 1600 human transcription factors (1), many of which have been implicated in cancer development and progression. The discovery of transcription factor inhibitors represents a major avenue in the development of cancer therapeutics, with several molecules entering clinical use and trials every year (2-7). However, the vast majority of transcription factor inhibitors target protein-protein interactions, induce degradation or bind a ligand-binding domain and only a small fraction of them target protein-DNA interfaces directly (7). Similarly, inhibitors of RNA-binding proteins typically do not interfere directly with the protein-RNA surface (8). Unlike conventionally targeted ligand-binding pockets enclosed within enzymatic- or receptor-active sites, protein-nucleic acid interfaces are solvent-exposed and enriched with positively-charged residues such as lysine and arginine enabling coordination with the nucleic acid backbone. These features make it rather challenging to directly target protein-DNA interaction areas of TFs with small molecules. As it has been explicitly stated in a recent review paper: âthere is a profound paucity of inhibitors of protein-DNA binding, in particular, likely owing to the typically convex and highly positively charged DNA binding interfaces being difficult targets for the development of small-molecule inhibitors with drug-like propertiesâ (7). Besides, nucleic acid-binding domains tend to be highly conserved within protein families making it difficult to design a specific small molecule binder (9). Despite these challenges, there is a recent surge in the development of protein-2 nucleic acid inhibitors owing to the advancements in computational and experimental technologies (8, 10). In a recently published paper, we outline multiple examples of how CADD techniques help to tackle challenges associated with targeting protein-DNA binding proteins (10). In this thesis, I further support this claim by demonstrating CADD-driven development of inhibitors of DNA-binding proteins AR and HOXB13, and an RNA-binding protein Lin28. 1.2 Computer-Aided Drug Discovery Conventional drug discovery through high throughput screening (HTS) is an expensive and time-consuming process that enables an exploration of only a limited amount of chemotypes. The hit rates of HTS are also notoriously low ranging from 0.01 to 0.14% (11, 12). On the contrary, the virtual screening (VS) greatly accelerates drug discovery and yields significantly higher hit rates of up to 40% (12). Moreover, the use of computational tools aids various stages of drug discovery starting from an early stage of hit identification through virtual screening to later stages of structure-activity optimization of candidate drugs (13). Additionally, advanced algorithms in cheminformatics enable the identification of novel chemotypes and scaffold hopping, broadening the chemical space explored beyond traditional HTS libraries. Recent advancements in CADD techniques and the explosive growth of structural and biological data enable the development of small molecules that effectively disrupt unconventional targets such as protein-DNA interfaces. This chapter provides a brief background on the CADD tools and techniques used in the development of protein-nucleic acid inhibitors introduced in the following thesis sections. 3 1.3 Molecular Docking Molecular docking is a technique widely used in structure-based drug discovery for the prediction and scoring of ligand binding poses. It involves predicting the most stable and energetically favorable three-dimensional arrangement of a ligand-target complex (14). This process requires two key inputs: a target protein with a defined binding site and a collection of compounds. Preparing these components often involves checking and adjusting factors like 3D conformation, protonation states, bond orders, tautomerism, and stereoisomerism if necessary (15, 16). The docking process itself consists of two main steps: predicting the binding pose and scoring it. For each compound, numerous conformers are explored to identify the pose that best fits the target pocket in terms of energy and geometry. Subsequently, a scoring function is used to estimate the binding affinity of this pose (Figure 1-1). There are various types of scoring functions, generally categorized into three groups: force field-based (utilizing classical force field calculations), empirical (incorporating multiple energetic factors such as hydrogen bonding and hydrophobic interactions), and knowledge-based (based on statistical analysis of existing 3D protein-ligand structures) (17). Effective docking aims to replicate the actual binding pose as determined by crystallographic methods. 4 Importantly, in contrast to ligand-based VS methods (18), molecular docking can reveal completely novel chemistry for âcoldâ drug targets (those lacking any known binders) (19). Thus, in previous decades, docking became a gold-standard tool for early-stage drug discovery in academia and industry as witnessed by many successful studies (20-25). Figure 1-1. Molecular docking example. A generated protein-ligand complex would receive a docking score. One of the main applications of docking is the VS of compound libraries against a selected protein binding site to find potential drug candidates. Importantly, while scoring functions are calibrated on experimentally derived values, the predicted values are usually far from the actual binding affinities (26). Therefore, molecular docking is mainly used to estimate relative binding, which means ranking compounds rather than providing exact affinity values. The main advantage docking offers over more precise approaches such as free energy perturbation and molecular mechanics, such as PoissonâBoltzmann/generalized Born surface area calculations, is its speed 5 that allows it to perform on a high-throughput level (27). This is especially important as the available chemical space increases and the utility of screening of ultra-large libraries has been widely acknowledged (28). However, while stand-alone docking is relatively fast, it still does not allow to cover the vast amount of chemical scaffolds available in virtual libraries such as ZINC20 (29), or Enamine Real Space (30) and ZINC22 (31) containing 1 billion, 5.5 billion and 40 billon compounds, respectively. 6 1.3.1 Ultra-Large Virtual Screening The chemical universe is believed to encompass up to 1060 unique small molecules (32). Rational attempts to enumerate and navigate parts of this enormous space led to the development of chemical libraries, which became an increasingly important drug discovery resource. Thus, one of the earlier public virtual libraries known as ZINC (the Zinc Is Not Commercial) contained nearly 730,000 compounds in its first release in 2005 (33). Unlike other public sources such as PubChem (34) and ChEMBL (35), ZINC was specifically designed for virtual screening (VS) and prioritized purchasable chemicals for which pre-calculated 3D conformations were provided. Over the years, ZINC has steadily grown in size (Figure 1-2), reaching 34 million compounds in 2012 (36), 220 million in 2015 (37), crossing the billion mark in 2020 (29), currently stands as an ultra-large chemical library with a staggering 37 billion enumerated molecules (31). This library, along with others like the Enamine REAL database (30), features a diverse range of compounds, including numerous drug-like molecules that can be synthesized rapidly, efficiently, and at a relatively low cost (38). The growth of these vendorsâ collections has not compromised chemical diversity, as novel scaffolds are continually added, and the overlap between different collections remains low (39, 40). Importantly, these libraries maintain drug-like properties and adhere to ADMET standards, crucial for pharmaceutical development (41). This expanding chemical space presents vast opportunities for drug discovery but also brings challenges in effectively navigating and screening for bioactive molecules. Consequently, the emergence of these ultra-large chemical libraries necessitates the development of new computational approaches in drug design to address the limitations of conventional virtual screening methods. Recently, docking was promptly adapted to ultra-large molecular databases, where it demonstrated remarkable advantages over smaller-scale VS campaigns including improved hit rates and access 7 to the abundance of novel chemotypes (42). However, ultra-large brute force docking comes with an unbearably high computation cost. As a solution, new VS approaches emerged that either use the leverage of fragment-based representation of docking libraries or replace docking with ML emulation protocols (Figure 1-3). Notably, these multidirectional efforts have already enabled successful ultra-large screenings, yielding several innovative and potent ligands targeting clinically relevant targets (43-46). Figure 1-2. ZINC database growth over years 8 Figure 1-3. Structure-based methods for ultra-large virtual screening Structure-based methods for ultra-large virtual screening. (a) In the brute force approach, all molecules from a chemical library are subject to docking. Large computational resources are required to perform such docking on a large scale. (b) In fragment-based methods, a library is decomposed into building blocks that are then docked into the binding site; top-scoring compounds are then identified by expanding or merging the best fragments. (c) Machine learning surrogates rely on docking of a subset of a library and training a model to predict docking scores of the remainder of the library and rapidly discard molecules with predicted low affinities, bypassing expensive docking. 9 1.3.2 DeepDocking In virtual screening (VS), it's customary to initially refine extensive chemical libraries into smaller, more manageable groups. This reduction is typically done by classifying compounds by fragments, drug-like, lead-like, and hit-like, or by using physicochemical filters based on characteristics such as molecular weight, volume, logP, logD, topological polar surface area, the number of rotatable bonds, and the quantity of hydrogen bond donors and acceptors. Although this method of using descriptor filters is effective in shrinking the size of the database to a more manageable scale, it often excludes a significant number of unique and potentially valuable chemical compounds. Additionally, the practice of selecting a limited group of molecules with favorable docking results for further analysis tends to overlook many potential hits, leading to underutilized data. This inefficiency in utilizing available data and exploring the chemical space led our group to develop Deep Docking (DD) (44, 45, 47), a new deep learning-based technique for the accelerated screening of exceptionally large chemical libraries. DD involves training a deep multilayer perceptron (MLP) model on a small subset of a virtual library using docking scores and circular chemical fingerprints (Figure 1-4). DD then uses active learning to iteratively refine the model, enabling rapid screening of billions of molecules with up to 50 times speed acceleration compared to traditional methods. Additionally, DD can achieve up to a 6,000-fold enrichment for top-ranked hits, particularly when applied to ultra-large libraries like the REAL Space (30). The training and prediction process of DD is repeated in iterations, each time increasing the docking score threshold and discarding compounds with low predicted scores, thereby maintaining minimal loss of potential hits. 10 Remarkably, the application of DD for the VS of ZINC15 (1.36 billion molecules) against the SARS-CoV-2 Mpro revealed several novel and experimentally validated inhibitors (43, 44). Notably, Rossetti et al. further developed one of the discovered chemotypes based on a dihydro-quinolinone scaffold into a potent lead compound with antiviral activity (46). Other successful applications of DD led to the discovery of inhibitors of Lin28, an RNA-binding protein implicated in PC (discussed in Chapter 2) (48), potent antagonists of A2A adenosine receptor, a promising cancer immunotherapy target (49), novel noncovalent inhibitor for the SARS-CoV-2 papain-like protease (50) and inhibitors of N-Myc, a protein critically implicated in the development of neuroendocrine PC (51). DD source-code adaptable for various docking software, its detailed protocol and GUI version are publicly available (under MIT license) to facilitate and democratize large-scale hit discovery (45, 52). Figure 1-4. DeepDocking Workflow. First, for each molecule in the database molecular descriptors are calculated. These descriptors are then used to cluster molecules. Second, molecules are sampled from clusters and docked into the target protein. Third, the derived docking scores and corresponding molecular descriptors are used to build machine-learning models that predict docking scores of undocked compounds. With each iteration, docking scores are predicted for a batch of molecules and the poorly scored ones are removed reducing the database. The number of iterations is user-defined but usually around 5. The output is a list of molecules with predicted plausible docking scores. 11 1.4 Pharmacophore Modelling A pharmacophore is a spatial arrangement of steric and electronic features within a molecule required for successful binding to a target site. It is usually derived from the binding mode of one or several active molecules (Figure 1-5) or directly from the protein binding site since pharmacophore modelling operates on the principle that molecules that share chemical functionalities and spatial configurations have similar effects on a biological target (53). Thus, a pharmacophore model encapsulates the chemical attributes of a molecule, which are conducive to interactions with a protein, through geometric representations like spheres, planes, and vectors. Key pharmacophoric features include hydrogen bond acceptors (HBAs), hydrogen bond donors (HBDs), hydrophobic regions (H), ionizable groups both positive and negative (PI/NI), aromatic rings (AR), and areas coordinating with metals. Additionally, to accurately reflect the binding pocket's dimensions and shape, constraints such as size-limiting shape or exclusion volumes (XVOL)âregions designated as inaccessibleâare incorporated (54). A pharmacophore model can be used to screen for potential binders and could be combined with other CADD tools such as docking or similarity search. Importantly, model validation is an essential step before practical application, ensuring the model's efficacy in distinguishing between active and inactive molecules (55). One factor that inevitably hinders the performance of a conventional pharmacophore model derived from a crystal structure of a target protein is their inability to account for the dynamic nature of the binding sites. To address this issue, molecular dynamics (MD) simulations have been employed to aid the development of dynamic pharmacophore models (56-58). In principle, these models integrate the information of multiple protein snapshots derived from MD simulations. To facilitate this integration, several methods have been developed, including the âcommon hits 12 approachâ (CHA) (57) that ranks compounds based on the number of matched models from a single Molecular Dynamics (MD) trajectory, without needing known ligand information, and the âconformers coverage approachâ (CCA) (59) that evaluates how many existing compound conformers fit different protein conformational states, suggesting more favorable binding when a compound's flexibility closely aligns with the protein's. Several drug candidates were developed using the dynamic pharmacophore approach including MMP-9, Anit-TB and CDK-2 inhibitors (58, 60, 61). Figure 1-5. An example of a pharmacophore mode. The model has been developed on the basis of several active ligands bound to the dimerization site of AR DBD. 13 1.5 Molecular Dynamics One of the key obstacles in accurately predicting ligand affinities through docking lies in the static representation of the target protein's structure. The crystal structure of a protein provides glimpse into its dynamic nature, capturing just one moment in a series of ongoing atomic movements and the protein's intrinsic flexibility, both critical for ligand interaction. To emulate these dynamic changes, molecular dynamics (MD) simulations have been developed that approximate the motions and rearrangements of the atoms in a solution using the laws of Newton dynamics (62). These simulations begin with the protein's atomic coordinates as established by its crystal structure. These coordinates are then iteratively adjusted based on the molecular forces calculated for each atom, with each cycle of adjustment typically advancing the simulation by 1 to 2 femtoseconds (a quadrillionth of a second), potentially accumulating to several microseconds in total duration (62). The collective shifts from these cycles form simulation trajectories, recording vital data about atomic positions and velocities. Such trajectories allow for various valuable analyses, including the characterization of ligand-protein interactions, evaluation of both ligand and protein stability, and estimation of binding energies, both free and kinetic, especially in extended simulations (63). The applications of MD in drug discovery include the characterization of protein-ligand interactions, elucidation of the relative importance of the binding features, free energy calculations, assessment of protein stability and the development of dynamic pharmacophore models (63). Additionally, integrating docking with MD-derived binding energy data has been shown to enhance the efficacy of virtual screening (64). In such applications, MD simulations serve to generate snapshots of the target protein, facilitating ensemble docking, a technique that captures an element of the target's flexibility and more accurately predicts ligand binding affinities (65). 14 Molecular dynamics (MD) simulations are also instrumental in uncovering how drug binding can alter a protein's structure, a phenomenon known as drug-induced conformational changes (66). This technique plays a pivotal role in elucidating the mechanisms of drug action, including identifying key conformational alterations (66). Importantly, molecular dynamics (MD) can enhance the optimization of drug candidates by providing detailed insights into crucial protein-ligand interactions. This information is vital for refining the efficiency and effectiveness of these compounds, ensuring they are precisely tailored for their intended therapeutic targets. Furthermore, MD is invaluable in understanding the mechanisms underlying drug resistance, especially in cases where protein mutations are involved (63, 67). It can reveal how specific mutations affect drug-protein interactions, potentially rendering a drug inactive or even causing a functional shift, such as an antagonist becoming an agonist (68, 69). By shedding light on these dynamics, MD enables the development of drugs that are less susceptible to common resistance mechanisms, ensuring their sustained efficacy in the face of potential mutations that typically confer resistance. 15 1.6 Quantitative Structure-Activity Relationship (QSAR) modeling When a set of molecules with varying activities is known, a QSAR model could be developed to predict the properties of untested compounds (70). To build a QSAR model, one starts by selecting a dataset of molecules with known biological activity, often represented by metrics like half-maximal inhibitory concentration (IC50), dissociation constant (Kd), binary classification or another property (71). Molecular descriptors, which encode chemical properties, are then calculated for each molecule in this dataset. The next step involves using a statistical method, typically linear regression or a similar technique, to correlate these descriptors with the biological activities. The model is then validated to ensure its predictive accuracy for new compounds. This process allows the QSAR model to predict biological activity based on chemical structure, aiding in the identification of potential therapeutic agents. QSAR modelling best practices include rigorous validation of the developed models as well as a transparent, detailed reporting of model development processes to enhance reliability and applicability in medicinal chemistry (Figure 1-6)(70). Figure 1-6. A standard QSAR workflow. 16 The accuracy of such models greatly depends on the amount and quality of experimental data. Additionally, QSAR models are limited in their predictive power by the so-called applicability domain, i.e. molecules that do not have any resemblance to the training set cannot be reliably predicted. However, as the wealth of biological data accumulates in databases (e.g. PubChem (34), ChEMBL (35)) the development of highly generalizable QSAR models becomes more feasible. Furthermore, with the combined advancements in computational power and the growth of chemical databases, the use of deep learning methods became more prevalent in drug discovery and specifically QSAR modelling (72, 73). Deep QSAR models have become instrumental in drug discovery, serving diverse roles from generative molecular design, where they facilitate the creation of molecules with specific desired properties, to synthesis planning, enabling the efficient organization of reaction series (74). 17 1.7 Similarity Search The fundamental principle of a chemical similarity search lies in the notion that molecules with similar chemical structures often exhibit similar physicochemical attributes and activity patterns. Thus, the objective of a similarity search is to identify molecules closely resembling a known bioactive compound, to discover more promising drug development candidates. These candidates are sought after for their potential enhancements in protein-ligand binding, increased metabolic stability, decreased toxicity, or other relevant properties. In a similarity search process, the initial step involves selecting a reference molecule, often a known bioactive compound, and querying a comprehensive database of chemical compounds. This process hinges on the choice of a specific molecular descriptor (e.g. Molecular Access Systems keys fingerprint (MACCS) (75), Morgan (76) and topological fingerprints such as Daylight (77)) to represent key characteristics of a molecule (Figure 1-7) and a similarity metric to evaluate the resemblance between molecules (e.g. Tanimoto (78) or Tversky (79))(80). Additionally, the advent of deep learning in drug discovery has led to the increasing use of autoencoder-generated latent vectors and graph representations of molecular structures (81, 82). Throughout the search, each compound in the database is analyzed and given a similarity score, indicating its proximity to the reference molecule in a multidimensional chemical space. This results in a ranked list of compounds, with those scoring highest identified as the most similar and potentially the most promising for further exploration. 18 Figure 1-7. Examples of molecular descriptors. Left: 3D shape representation. Right: hypothetical 10-bit substructure descriptor 1.8 Integrative Approach and Consensus Scoring A typical CADD campaign combines the output of multiple tools to achieve high hit rates. The outputs of multiple molecular modelling techniques could either be used as sequential filters (e.g. compounds that have high predicted scores from DeepDocking are then docked with ICM software) or as consensus filters (e.g. select compounds that receive high docking scores from both Glide and ICM software). Such an integrative approach has been proven as a best CADD practice as it yields promising drug candidates including our recently published inhibitors of AR (83, 84). Furthermore, due to the differences in the time efficiency of each tool, it is essential to integrate CADD tools into pipelines to process large and ultra-large libraries. For instance, DeepDocking allows to filtering of up to 40B compounds to a few million that are then docked with Glide to 19 further select several hundred thousand that could be managed by ICM software in a reasonable time. Another common technique to improve the results of docking-based VS is to combine the results of multiple docking software and perform consensus voting (85-87). There is a vast variety of docking programs on the market including commercial software such as Glide (88), GOLD (89), FlexX (90), ICM (91), and FRED (92) and publicly available software such as AutoDock (93) and rDock (94). The software differs both in the pose prediction algorithms and scoring functions meaning that each software has specific implicit errors and biases. Our group has previously shown that there is very little agreement in the ranking of large libraries between different software further highlighting the vast disparity between algorithms (44). To improve the results of VS, we frequently employ consensus scoring where the distances between the docking pose predicted by different software are calculated and used as a threshold to eliminate the molecules with unreliable predictions (e.g. docking poses differ by >3Ã ). Several drug candidates have been identified using the described approach including AR (95), ERG (96) and MYC (97) inhibitors. More recently, a DD screen of ZINC15 and REAL Space databases against Mpro using multiple docking software consensus approach yielded more than one hundred experimentally confirmed noncovalent inhibitors of unexplored chemotypes with several of them having low micromolar activity (44). In this work, our group combined five docking programs, namely Autodock GPU (98), GlideSP (99), FRED (92), ICM (91) and QuickVina2 (100), in a serial screening pipeline that progressively selected molecules scoring well across all the five software. Besides focusing on consensus molecules, this strategy also offers the possibility to screen the library much faster than using the five programs individually in separate DD campaigns; this is achieved by 20 considering progressively smaller subsets of potential ligands for the DL-accelerated docking every time a new docking program was utilized. 21 Literature Review of Small Molecule Inhibitors of Protein-DNA Interactions Developed With CADD 2.1 Introduction The advancements in computational methods outlined in the Introduction have significantly contributed to the development of selective and potent inhibitors targeting protein-DNA interactions. These technological innovations, both computational and experimental, have enabled the development of inhibitors of various crucial cancer targets including (but not limited to) androgen receptor (AR), ETS-related gene (ERG), MYC, thymocyte selection-associated high mobility group box protein (TOX), and topoisomerase II (TOP2). The following literature review also highlights several less advanced drug candidates targeting protein-DNA coordination of signal transducer and activator of transcription 3 (STAT3), paired box protein 2 (PAX2), forkhead box protein M1 (FOXM1), activator protein 1 (AP-1) and Gli. 2.2 ETS-related gene (ERG) ERG (ETS-related gene) is a member of the ETS (E26 transformation-specific or E-twenty-six) family of proteins, which consists of 28 transcription factors all containing a conserved DNA-binding ETS domain (101). The 90 amino-acids ETS domain has a winged helix-turn-helix fold that binds to a GGA(A/T) DNA motif (102). The α-helix of the ETS makes contacts with the major groove of the core DNA sequences, and the loops interact with flanking sequences at the minor groove (102). In addition to the DNA-binding domain, half of the ETS family members have an N-terminal SAM or pointed (PNT) domain for protein-protein interactions and dimerization (101). The importance of ETS factors in cancers was initially established by early discoveries on the role of FLI1 in Ewing sarcoma and leukaemia (101). Furthermore, Tomlins et al. (103) reported the 22 occurrence of ERG gene fusion with an androgen regulated promoter TMPRSS2 in PC (PC), which eventually has been recognized as a hallmark event in PC (104). The resulting activation of ERG-TMPRSS2 fusion product was observed in around 50% of PC patients and ERG became an attractive drug target (103, 105-107). The feasibility of ERG targeting was initially demonstrated with YK-4-279 compound that induced apoptosis in Ewing sarcoma cells and inhibited ERG and ETV1 transcriptional activity in PC cells (108). Another compound ERGiâUSU significantly reduced ERG protein expression in VCaP PC cell line (109). However, the exact mechanisms of ERG inhibition and details of the binding modes of these compounds are yet to be determined. Targeting the ETS domain of ERG could represent a robust therapeutic strategy as among the variety of TMPRSS2-ERG fusion product the DNA-binding functional ERG domain is preserved (110). Thus, Wang et al. (2017) developed DBD-directed ERG inhibitory peptides that successfully blocked ERG-mediated transcription and suppressed PC growth and metastasis in mice xenograft models (111). The availability of crystal structure of the ERG DBD opened the door for rational design of a small molecule inhibitor through computational techniques. Importantly, despite the substantial conservation of the DBD domain among the ETS members according to our analysis the exact structural conformation remains different enough to achieve selective targeting (104). In particular, residues Pro306 and Ile395 constituting the binding site are not conserved between other members of ETS family and could act as selective anchors for ERG inhibition (Figure 2-1). The key difference is hydrophobic residue Ile395 that is replaced by polar residues Gln, Glu and Asp in other ETS members (i.e. ETS1, ETV4, ETV6). Based on these considerations, we have recently developed a series of small molecule ERG inhibitors through CADD (96). As described above in details, we identified a druggable cavity on the surface of ERG DBD fold that was also structurally distinct from homologous members of 23 ETS family (104). Consequently, a total of 3 million drug-like chemical structures were prepared from the ZINC database (112), and all of them were virtually docked into the target site on DNA-binding ETS domain of ERG. Based on the generated docking scores, the top 48 candidate molecules were then selected for in vitro screening, and the lead compound VPC-18005 has been shown to inhibit ERG-driven transcriptional activity in VCaP and PNT1B-ERG cells (IC50 of 3 and 6 μM, respectively) (96). As presented on Figure 2-1, the VPC-18005 is predicted to bind at the protein-DNA interface on the ERG-ETS domain and sterically block its interaction with the DNA. Within the binding site on the ERG-ETS domain, VPC-18005 can form a salt bridge with Lys357, hydrogen bonds with Leu313, Trp351 and Tyr372, and hydrophobic interactions with a number of surrounding amino acid residues including Pro306 and Ile395 that are non-conserved between ETSmembers (Figure 2-1b). The NMR spectroscopy results have confirmed direct binding between VPC-18005 and the ERG-ETS domain, with the chemical shift perturbations consistent with the proposed in silico model. Furthermore, EMSA experiment demonstrated that VPC-18005 can disrupt the ERG-DNA complex formation and inhibits expression of ERG downstream target genes such as SOX9. In a zebrafish xenograft model, VPC-18005 reduced migration and invasion of ERG-expressing PC cells, and suppressed metastasis at low micromolar concentrations (96). In murine pharmacodynamics and toxicology studies, VPC-18005 behaved as a soluble, stable and orally bioavailable substance. Taken together, these results provided a proof-of-principle justification of selective targeting of ERG with small molecules. Such selective inhibition has the potential for developing principally novel treatments for PC patients who developed resistance to conventional androgen-deprivation therapy. We anticipate that these 24 inhibitors should be more resistance-prone as DNA-binding domains tend to be more conserved and exhibit lower mutation rate (Figure 2-1c) (113). Figure 2-1. Compound VPC-18005 targeting the ETS-related gene (ERG)âDNA interaction site. (a) VPC-18005 (orange sphere) blocks the interaction between the ERG-ETS domain (gray surface) and DNA (cyan ribbon). (b) Magnification of the interactions between compound VPC-18005 and its surrounding protein residues (hydrogen bonds as red-dashed lines and hydrophobic interactions as green-dashed lines). Structural information was extracted from 66 and Protein Data Bank (PDB) structure 4IRI. (c) Multiple sequence alignment of DNA-binding domains (DBDs) on ERG and its close homologs including FLI1 and FEV. Alignment was performed with Clustal Omega, visualized by Jalview, and colored by percentage identity. Interacting residues with VPC-18005 are indicated by red boxes; residues conferring resistance are in solid red. Residue numbering is based on ERG (UniProt ID: P11308-4). 25 2.3 Signal transducer and activator of transcription 3 (STAT3) The signal transducer and activator of transcription 3 (STAT3) protein belongs to the STAT family of transcription factors that regulate cell growth, differentiation, immunity and apoptosis (114). STAT family members are activated via phosphorylation of a C-terminus tyrosine residue by receptor-associated Janus kinases (JAKs) and Src kinases in response to growth factors and hormones (115, 116). Phosphorylated STAT monomers form functional dimers and accumulate in the nucleus where they bind transcription-factor motifs and induce gene expression of the target genes (117, 118). In normal cells, STAT activation is transient, while in many cancer cells activated STAT3 proteins constantly present and aid cancer progression(119). STAT3 hyperactivation has been linked to the promotion of angiogenesis, metastasis and invasion as well as the production of immunosuppressive factors (120-124). Since STAT3 protein is the key player in tumourigenesis, its inhibition became an attractive therapeutic strategy (119, 120, 125). The majority of developed STAT3 inhibitors target its SH2 domain, which facilitates dimerization and phosphorylation. However, some findings indicate that unphosphorylated and monomeric STAT3 can also activate its downstream genes indicating that resistance to SH2-directed therapy might readily develop. Therefore, targeting more conserved and mutation prone DNA-binding domain to directly abrogate transcriptional activity of STAT3 can represent an alternative and more sustainable therapeutic strategy (125). The successful targeting of the DNA-binding domain of STAT3 with decoy oligonucleotide was achieved in phase 0 clinical trials (only 30 subjects) on patients with head in neck tumours serving as a proof-of-principle for this approach (126). STAT family consists of seven members: STAT1, STAT2, STAT3, STAT4, STAT5A, STAT5B, and STAT6. The role of each protein in cancer has been extensively reviewed (127, 128). Importantly, while STAT3, STAT5 and STAT6 are known to be pro-tumourigenic, STAT2 and 26 STAT4 act as tumour suppressors through activation of Th1 response, and STAT1 and STAT5 A/B have dual functions. Thus, STAT3 targeting requires high specificity to avoid inhibition of its structurally related tumour suppressor family members. Structure-based rational drug design provides a solution to achieve the desirable selectivity. For example, Huang at al (2014) employed virtual screening to identify a selective inhibitor of STAT3-DNA interaction(129). In particular, they docked 200,000 compounds against both STAT3 and STAT1 DNA-binding domains and filtered out molecules that were predicted to exclusively bind STAT1. Out of 57 selected entities, 4-[(3E)-3-[(4-nitrophenyl)-methylidene]-2-oxo-5-phenylpyrrol-1-yl] benzoic acid named InS3-54 was found to be the most active with an IC50 of 13.8 ± 0.4 μM in a luciferase reporter assay. The favourable binding of InS3-54 to STAT3 and the absence of binding to STAT1 was then demonstrated with molecular dynamics (MD) simulations followed by generalized born surface area (GBSA) analyses for ÎG calculations. According to the computational modelling, InS3-54 does not bind to STAT1 primarily because it forms clashes with residues in Pro326 and Thr327 STAT1 (the latter is not conserved between STAT1 and STAT3) (Figure 2-2a). In STAT3, InS3-54 forms hydrophobic interactions with Met331, Val343, Ile467, and Met470 and hydrogen bonds with Lys 340 and Asn466 (Figure 2-2b). Out of these, residues Val343, Ile467, and Met470 are not conserved between other members of STAT, therefore, it is likely that InS3-54 is specific to STAT3. Overall, while the majority of the DNA-interacting residues are conserved between members of STAT, InS3-54-interacting residues appear to be different conferring the selective inhibition (Figure 2-2c). The proposed mechanism of action via direct DNA-STAT3 inhibition was confirmed with EMSA and further backed up with the evidence that InS3-54 does not alkylates STAT3 nor does it affect its dimerization. Antineoplastic activity of InS3-54 was tested in several lung cancer and breast cancer cell lines 27 where it induced apoptosis and significantly decreased cell migration and invasion. Further studies aimed to improve InS3-54 affinity to overcome the observed off-target effects (130). Thus, authors retrieved 79 analogs of InS3-54 using structural similarity search, 15 of which outperformed the parent compound in luciferase assays. Furthermore, 3 analogs and InS3-54 caused a prominent reduction in hematopoietic colony-formation. Of these, the 3 analogs did not show activity in STAT3 knockout mice providing solid evidence for STAT3 specificity of these analogs. Finally, analog A18 was selected as the lead compound because it appeared to be more specific and slightly more active compared to the parent compound (IC50 of 8.8 μM). Additionally, it was found to inhibits the expression of STAT3 downstream target genes as well as reduce tumour growth and metastasis. Another recent study utilized structural information on InS3-54 series to create a pharmacophore model with 3D shape- and electrostatic features used to screen the Specs chemical library (131). Such computational screening yielded 40 compounds that were investigated in vitro and compound LC28 appeared to be the most active in the H1299â¯cell line, a human non-small cell lung carcinoma cell line, (IC50 of 8.1â¯Â±â¯4.1â¯Î¼M) and was shown to specifically bind STAT3 DNA-binding domain. LC28 was then used as a query to search for similar molecules. Five analogs of LC28 showed better activity in the H1299 cell line and better inhibition of STAT3-DNA binding (0.74â¯vs 3.92â¯Î¼M, LC28-29, LC28, respectively). Additionally, LC28 and its analogs inhibited cisplatin-resistant ovarian cancer cells with IC50 of ~7.0 μM in most affected cell line, LC28-29. Another VS campaign initiated by Buettner et al. (2014) led to the identification of a selective inhibitor of STAT3, compound C48 (132). The computational screening was based on the molecular docking of 7000 compounds into an MD-derived conformation of STAT3 followed by similarity clustering. Importantly, authors report that they choose a binding site with low similarity 28 with STAT1. Out of 52 selected ligands tested in EMSA assay, the most potent compound C36 was used as a query to further search two chemical libraries. Subsequent EMSA results for the retrieved analogs revealed the lead compound C48 that inhibits STAT3-DNA binding at an IC50 in the range of 10â50 μM. As expected, C48 did not inhibit STAT1. The consequent site-directed mutagenesis demonstrated that C48 alkylates Cys468 residue located at the DNA-binding surface thus preventing the DNA-protein association and consequently resulting in significant suppression of tumour growth in MDA-MB-468 breast cancer xenograft models. Besides the de novo development of selective inhibitors of STAT3, CADD techniques also helped to link antineoplastic activities of some compounds to STAT3-DNA inhibition. For instance, in a series of studies, a natural compound (E)-2,4-bis(p-hydroxyphenyl)-2-butenal (BHPB) was initially established as an antineoplastic agent and then its action was linked to disruption of STAT3-DNA complex (133-135). Later, Son at al (2017) attempted to optimize stability and selectivity of BHPB via modifications of a chemically labile α,β-unsaturated aldehyde moiety (136). Thus, they designed 16 analogs, out of which (E)-2-methoxy-4-(3-(4-methoxyphenyl)prop-1-en-1-yl)phenol (MMPP) demonstrated superior activity in a STAT3-dependent luciferase reporter assay and in inhibiting the growth of the NCI-H460 NSCLC cell line, a non-small-cell lung cancer cell line. Importantly, MMPPâs direct binding to the DNA-binding domain was confirmed through a pull-down experiment and further backed up by docking experiment, in which it exhibited a favourable binding pose at the DNA-interacting surface. Finally, MMPP suppressed tumour growth in xenograft mouse models of A549 non-small cell lung cancer, HCT116 colon cancer, and PA-1 ovarian cancer cells. 29 Another example is a curcumin analog, HO-3867 that exhibited a prominent antineoplastic activity in various cancer cell lines (A27820, A2780R, MCF-7, HCT-116, PC-3, HepG2, A549, and SCC4) and significantly decreased the expression of STAT3 downstream genes (137-139). Docking predictions and ELISA experimental assays suggested that HO-3867 binds the DNA-binding domain of STAT3 (140, 141). Importantly, HO-3867 showed a significant inhibition of tumour growth in ovarian cancer xenograft mice proving that STAT3-DNA inhibition with a small molecule is effective in vivo. Figure 2-2. Compound InS3-54 targeting the STAT3âDNA interaction site. (a) InS3-54 (orange sphere) binds at the DNA-binding surface of STAT3 (gray surface). (b) Magnification of the interactions between compound InS3-54 and its surrounding protein residues (hydrogen bonds as red-dashed lines and hydrophobic interactions as green-dashed lines). Structural information was extracted from 82 and Protein Data Bank (PDB) entry 1BG1.215 (c) Multiple sequence alignment of DNA-binding domains (DBDs) of STAT family proteins. Alignment was performed with Clustal Omega, visualized by Jalview, and colored by percentage identity. Interacting residues with InS3-54 are indicated by red boxes; residues conferring resistance are in solid red. Residue numbering is based on STAT3 (UniProt ID: P40763). 30 2.4 Thymocyte selection-associated high mobility group box protein (TOX) Thymocyte selection-associated high mobility group box protein (TOX) is a 526 amino-acid long nuclear protein that belongs to an evolutionarily conserved high mobility group box (HMG) family (142). TOX is composed of an HMG-box domain that spans from amino acid 257 to 328, which binds DNA in a structure-dependent and sequence-independent manner (142, 143). TOX is a key regulatory nuclear protein in the development of CD4+ T cells, natural killer cells, and lymphoid tissue inducer cells, and its expression is tightly controlled and expressed during early CD4+ T cell development (144). TOX is aberrantly expressed in cutaneous T cell lymphoma (CTCL), which is a primary lymphoma of the skin derived from cutaneous resident memory T cells. Previous studies demonstrated that TOX is a specific biomarker, strong prognostic factor, key pathogenic driver, and attractive therapeutic target for CTCL (145-148). (147). It has been shown that TOX suppression induces apoptosis and caspase activation in CTCL cells, and stable knockdown of TOX inhibits growth of CTCL cells in vitro (146). It has also been demonstrated that TOX suppression impairs tumour-forming ability of CTCL cells in vivo (146). Furthermore, transcriptomic analysis showed that TOX silencing resulted in marked expression changes of numerous genes, with the most significant changes observed in cell cycle suppressor gene SMAD3(146). Yu et al. (2015) reviewed the role of TOX in cancers and suggested it as a valuable therapeutic target (149). Despite this notion, to our knowledge there was no agent developed to target TOX directly. To address the lack of TOX small molecule inhibitors, we recently utilized an established computer-aided drug design (CADD) pipeline that combines virtual screening of 7.6 million drug-like small molecules with in vitro experimental validation, to discover an initial series of TOX inhibitors (143). As there was no previously identified druggable sites on the target, we first 31 performed computational drug binding site identification and druggability assessment of TOX. To do so, we performed âblind dockingâ (docking without a specified binding site) of around 200,000 drug-like molecules and observed a cavity where most of them were predicted to retain. We validated the druggability of the selected site by testing top 22 compounds experimentally and confirming 7 of them as weak binders (143). This step enabled us to refine a plausible binding site that was further used for a large-scale docking of 7.6 million drug-like compounds. This VS run yielded 18 small molecules that inhibited viability of TOX-dependent CTCL cells with micromolar activity (IC50 of 12 â 58 µM) and 4-fold selectivity (measure by average IC50 in TOX-Low cells divided by average IC50 in TOX-High cells)(143). As illustrated in Figure 2-3, compounds such as VPC-190444 can bind at the hot spots located in close proximity to the proteinâDNA interface on the HMG-box domain of TOX. According to computational modelling, these ligands interact with TOX residues Gln262, Pro264, Arg273, Lys313, Glu320, and Gln324 through hydrogen bonds and hydrophobic contacts, corresponding to the hot spots identified by initial druggability assessment. Furthermore, we predict that VPC-190444 is selective to TOX among its family members because it forms crucial hydrophobic interaction with residue Gln324 that is not conserved in other TOX proteins (Figure 2-3). Most of the identified actives share key common cores including diazole, triazole and imidazolidinedione moieties, NH group that forms a hydrogen bond with Gln262 and Glu320. These common substructures could be utilized for pharmacophore modeling in future rounds of hit to lead optimization. We hypothesize that such small molecule binders will interfere with TOXâDNA interaction and thus can disrupt transcriptional activity of TOX. This hypothesis was further supported by experiments where VPC-190444 increased the expression of SMAD3, which is normally suppressed by TOX (143). This study also demonstrated 32 utility of CADD in the development of protein-DNA disrupting inhibitors for challenging, previously unexplored transcription factors. Figure 2-3. Compound VPC-190444 targeting thymocyte selection-associated high mobility group box protein (TOX)âDNA interaction site. (a) VPC-19044 (orange sphere) binds at the hot spot (purple patch) on the TOX HMG-box domain (gray surface), located in close proximity to the DNA (cyan ribbon). (b) Magnification of the interactions between compound VPC-190444 and its surrounding protein residues (hydrogen bonds as red-dashed lines and hydrophobic interactions as green-dashed lines). Structural information was extracted from96 and Protein Data Bank (PDB) entries 2CO9 and 3TMM. (c) Multiple sequence alignment of DNA-binding domains (DBDs) on TOX and its close homologs, including TOX2, TOX3, and TOX4. Alignment was performed with Clustal Omega, visualized by Jalview, and colored by percentage identity. Interacting residues with VPC-190444 are indicated by red boxes; residues conferring resistance are in solid red. Residue numbering is based on TOX (UniProt ID: O94900-1). 33 2.5 Myc-Max interaction with DNA The Myc gene family is composed of three members known as MYC, MYCL, and MYCN, each respectively encoding transcription factors C-Myc, L-Myc, and N-Myc (150). Although their role differs in cancer, they share high sequence similarity and structural homology, thus functioning in identical fashion by binding to conserved E-box DNA sequences in target genes to activate transcription (151, 152). Myc proteins act as central transcriptional hubs in cellular growth and proliferation, and in normal state they are regularly expressed and tightly regulated (153, 154). However, they are often amplified, overexpressed, and deregulated to drive tumourigenesis and pathogenesis in numerous human cancers (155-158). Despite deep understanding that Myc activation is one of the key events in cancer emergence and progression, attempts at designing targeted therapies have been limited as Myc has been widely described as âundruggableâ. It is challenging to target Myc protein because it is an intrinsically disordered. To exert its transcriptional activity, Myc forms a heterodimer with its obligate partner Max through the bHLHZip motif (159). This stable heterodimer helix bundle then binds to DNA E-box promoters and enhancers and activates downstream transcriptional effects on targeted-genes (Figure 2-4) (160). Myc does not have enzymatic or other ligand-binding cavities and does not contain distinct functional domains that could be suitable for conventional inhibition with small molecules (161). Combined with largely unstructured overall structure and absence of apparent accessible hydrophobic pockets, these factors make rational development of Myc-directed inhibitors very challenging (162). 34 Figure 2-4. Compound VPC-70812 targeting the N-MycâMaxâDNA interaction site. (a) VPC-70812 (orange sphere) binds to both N-Myc (red ribbon) and Max (magenta ribbon) to block DNA E-boxes (cyan ribbons) from interacting with N-Myc. (b) Magnification of the interactions between compound VPC-70812 and the surrounding N-Myc-Max residues (hydrogen bonds as red-dashed lines and hydrophobic interactions as green-dashed lines). Structural information was extracted from.133 Most of confirmed Myc inhibitors are considered being indirect as they involve another target either through downstream Myc-dependent transcriptional signaling (163), or through pathways positioned upstream of Myc (164), or module Myc stability through undefined mechanisms (165). As most common cancer drivers share similar roles, redundancy in the complex oncogenic pathways increases the likelihood of resistance emerging to potential Myc-indirect treatments. Allosteric sites and to a certain extent, PPI surfaces, are also highly prone to selection and deleterious mutational events that reduces or eliminate the effects of targeted- therapies(166, 167). Therefore, directly targeting Myc, and more specifically, the âundruggableâ protein-DNA sites, is highly desirable to avoid potential emergence of resistance (168). 35 Significant efforts have gone into discovery of Myc-specific inhibitors since its classification as a cancer driver, and have largely comprehensively covered in a number of previous reviews (169). Herein we will mention one of Myc suppressor - Omomyc mini-protein, a dominant negative mutant developed from the bHLH domain of Myc to which key mutations were introduced to improve dimerization. Omomyc established the proof of principle for inhibiting Myc-mediated transcription by competing with Myc for binding to targeted genes (170). Demma et al. suggested new mechanisms in which Omomyc binds to DNA as an inactive homodimer with itself, preventing Myc-Max interaction with the DNA, or as a heterodimer with Max to prevent formation of Myc-Max heterodimer and thus reducing Myc protein levels in cells (171). Omomyc represents a proof-of-principle for inhibition of Myc-Max-DNA interaction by competing with Myc for DNA binding, especially in the light of its recent recruitment in clinical trials (172). Other inhibitors of Myc-Max-DNA interactions include an early stage small molecule inhibitor MYRA-A which despite its detectible antineoplastic activity, was found to be cross-reactive with Mnt-Max complex. Jung et al. (2015) identified synthetic α-mimetics JKY-2-169 that abolished Myc-Max binding to DNA and showed antiproliferative properties in Myc overexpressing cells (173). Another small molecule inhibitor targeting Myc-Max-DNA binding KSI-2826 decreased Myc-mediated transcriptional and showed significant growth inhibition in bladder cancer xenograft models (174, 175). Importantly, KSI-2826 was able to suppress tumours resistant to gemcitabine, the most effective first- and second-line chemotherapeutic in bladder cancer (176). Taken together, these results demonstrate feasibility of Myc-Max-DNA binding inhibition and the utility of compounds with such novel mode of action (MOA). Importantly, the generated structural information on Myc-Max complexes (both in unbound and bound to a DNA fragment) provides invaluable opportunity for rational, structure-based drug design efforts for Myc (177, 178). 36 Recently, we developed a new series of Myc-Max disruptors targeting the DNA binding domain (DBD) of the complex. Our rational CADD approach consisted of a large-scale virtual screening of 4.7 million drug-like compounds from ZINC12 database against the Myc-Max heterodimer DNA recognition site (residues 393-401 and 419-423 in Myc; 211-222 and 239 in Max). The best hit, VPC-70063 demonstrated inhibition of Myc transcriptional activity IC50 of 8.9 μM (97) in LNCaP cells. The compound further inhibited the proliferation of Myc-positive LNCaP PC cells without significant effects on the HO15.19 Myc-negative cell lines, suggesting Myc-specific MOA. Based on these promising results for VPC-70063, we sought to optimize the Myc series of inhibitors for better potency and specificity. In particular, as N-Myc is stabilized by forming a complex with Aurora A kinase (AURKA), we sought to design a dual-inhibitor for both N-Myc and AURKA. Such dual inhibitor could have a particularly significant impact on the treatment of neuroendocrine PC (NEPC) for which no drugs yet exist, and where N-Myc/AUKA complex plays a central oncogenic role. Using an array of CADD techniques, we have further modified the core of Myc-directed VPC-70063 molecule and enhanced it with AUKA binding features. The resulting inhibitor VPC-70812 was thus designed to target both the N-Myc-Max DBD and the AURKA ATP binding region (179). The compound was computationally designed by joining our Myc-specific scaffolds with the AURKA-specific scaffold of CD532 through a commonly shared phenyl ring. CD532 is a recent AURKA inhibitor that was revealed to be a strong conformation disruptor, able to destabilize AURKA into an inactive form, as well as inhibiting ATP binding to AURKA (165). We tested the developed dual-inhibitor in LNCaP luciferase transcription assay and it reported an IC50 of 2 µM, and it was also potent in inhibiting AURKAâs kinase activity. VPC-70812 demonstrated strong anti-proliferative activity in the low micromolar range in N-Myc driven PC cell lines (IC50 of 3.71 37 µM in 22Rv1 and 3.05 µM in LNCaP). The compound demonstrated strong cytotoxic properties as it targets two essential proteins in cell proliferation. Thus, by interfering simultaneously with both N-Myc and AURKA pathways in the NEPC network, VPC-70812 could be further developed as a multi-purpose cytotoxic chemotherapy agent. In contrast to our effort to target stabilized Myc-Max heterodimer, Yu et al. (2016) directly targeted the disordered Myc protein. To do so, they performed extensive MD conformational sampling of the disordered bHLHZip Myc domain to generate an ensemble of representative binding sites that have been utilized for multi-conformational docking. They virtually screened 201,939 compounds against the ensemble and advanced two classes of compounds into experimental testing: âhigh-conformational-specificityâ (predicted to bind strongly to 3 out of 5 conformations) and âmulti-conformational-affinityâ (predicted to bind all 5 with a notable affinity). Seven of the tested compounds appeared to bind c-Myc in protein-based assays with KDs ranging from 0.28 to18 μM. Four compounds suppressed Myc-mediated transcriptional activity and inhibited growth of the HL-60 c-Myc overexpressing cells (EC50 in range 6.9-40â¯Î¼M). Authors suggested that identified hits interact with a dynamic binding site of the disordered c-Myc protein (residues 370â387) and prevent its dimerization with Max (thereby abrogating Myc-Max-DNA coordination). Similarly, they studied a known Myc-Max inhibitor 10074-A4 through MD simulations and proposed that the ligand binds at different sites along the Myc protein and acts as a âligand cloudâ (180). This "ligand cloud" refers to the ligand's ability to dynamically occupy various conformations and binding sites on the Myc protein, showcasing the flexibility that is crucial for effectively targeting intrinsically disordered proteins (IDPs). Collectively these results demonstrated the immense utility of CADD for IDPs targeting and further illustrated the ability of CADD to target some systems conventionally regarded as âundruggableâ. 38 2.6 Paired box protein 2 (Pax2) Paired box protein 2 (Pax2) is a transcription factor that regulates differentiation of numerous cell types, especially from kidneys, reproductive tract and CNS (181, 182). Thus, normally down-regulated Pax2 exhibits aberrant overexpression in kidney cancers (183-186). Some findings indicate that Pax2 depletion has significant antineoplastic effects in renal carcinoma making Pax2 an attractive target for kidney cancer chemotherapy (187, 188). Thus, the development of a small molecule inhibitor that would directly abrogate Pax2-DNA binding represent a significant interest and potential. However, there is no crystal structure available of the Pax2 DNA-binding domain making the corresponding structure-based drug design challenging. Grimley et al. (2017) tackled this problem by employing homology modelling of Pax2 from closely related Pax5 and Pax6 (189). They also performed structure optimization of Pax2 model through molecular mechanics and MD simulations to produce an accurate model suitable for VS. Using the derived structure, they performed docking of around 100,000 drug-like compounds against the binding site on the surface of the DNA-binding domain that was defined based by mutagenesis (residues Cys63, Leu32, Val59 and Ile67)(190, 191). Reporter-based and counter-screenings assays revealed five compounds with modest activities that were then used to search for analogues. Out of those, compound EG1 was found to inhibit Pax2 in the luciferase assay with an IC50 of 10 µM. Its specificity to Pax2 was confirmed in the counter screen assay and the direct inhibition of protein-DNA interaction was detected by bio-layer interferometry (Kd of 1.35 µM). The observed protein inhibition successfully translated into cell growth suppression as EG1 interrupted kidney development in embryonic models and reduced cell viability in Pax2 positive cancer cell lines (RCC111, TOV112D and ES-2). Overall, this study provides yet another important example of 39 successful usage of homology modelling and methods of CADD in general for the discovery of inhibitors for novel targets, even lacking a resolved structure. 2.7 Forkhead box protein M1 (FOXM1) Forkhead box protein M1 (FOXM1), is a proliferation-associated TF that is ubiquitously found in progenitor cells and in various malignancies including ovarian-, breast-, prostate-, colorectal-, lung-, gastric- cancers and melanomas (192, 193). The importance of FOXM1 in cancer progression along with its central role in determining cellular drug sensitivity urged the development of effective FOXM1 inhibitors (194-196). In particular, Gormally et al. (2014) reported compound FDI-6 that blocks the DNA-FOXM1 binding (IC50 of 22.5 ± 12.3 μM in EMSA) (197). The hit appeared from the high-throughput FP screen of 54,211 drug-like compounds followed by EMSA validation. Additionally, non-specific binding to DNA was ruled out with mass spectrometry and counter-screening. Another study demonstrated FDI-6-induced-suppression of FOXM1-activated genes using RNA-sequencing of MCF-7 cells(198). More recent in silico investigation of FDI-6 binding to FOXM1 using molecular docking and MD simulations uncovered a potential binding site comprised of residues Asn283, Arg286, His287 where residue His287 forms crucial Ïâsulphur interaction with positively charged ligand sulphur (199). Notably, authors claimed that structurally unrelated compounds thiostrepton and thiazolidinediones bind FOXM1 in a similar fashion. Indeed, there is compelling evidence that thiostrepton, a cyclic oligopeptide with antimicrobial and antineoplastic properties, directly interacts with FOXM1(200). Thus, this study illustrates the utility of computational modelling of existing inhibitors to uncover binding modes and novel MOAs. Importantly, the reported similarity in binding modes between FDI-6, thiostrepton and thiazolidinediones could serve as a pharmacophore model for future virtual screens in pursuit of novel FOXM1 inhibitors. 40 2.8 Activator protein 1 (AP-1) Fos/Jun Activator protein 1 (AP-1) is a heterodimer transcription factor composed of proteins from different families Fos, Jun, and ATF (201). AP-1 binds to DNA and regulates expression of genes involved in proliferation, differentiation, and apoptosis. The AP-1 proteins such as Fos and Jun have been implicated in many diseases including cancer, fibrosis, and inflammatory disorders (202-204). A number of small molecules such as nordihydroguaiaretic acid (NDGA)(205), curcumin derivative (BJC005) (206) and T-5224 (207) (IC50 of 7.9 nmol, 5.4 μM and 10 μM, respectively) have been reported to inhibit AP-1 binding to DNA. To elaborate this MOA Yin et al. used CADD methods such as induced-fit docking and MD simulations to show how these compounds prohibit conformational changes of AP-1 and block access of DNA(208). As shown in Figure 2-5 T-5224 forms hydrogen bonds with positively charged residues Arg164, Arg173, Arg277 and Lys284. Authors concluded that these inhibitors bind the DNA-binding residues and further block the conformational change essential for stable association of Fos/Jun complex with DNA. The study provided significant insights in the Fox/Jun inhibition mechanism valuable for future drug design efforts. In particular, the identified binding site could be utilized for docking-based large-scale virtual screening. 41 Figure 2-5. Compound T-52247 targeting activator protein 1 (AP-1) Fos/Jun-DNA interaction site. (a) T-52247 blocks the interaction between AP-1 Fos (purple ribbon)/Jun (red ribbon) and DNA (cyan Structural information was extracted from160 and Protein Data Bank (PDB) entries 5VPB and 5VPE.ribbon). (b) Magnification of the interactions between VPC-52247 and its surrounding protein residues from Fos (purple) and Jun (red). Hydrogen bonds are shown as red-dashed lines and hydrophobic interactions as green-dashed lines. 2.9 Gli protein The Hedgehog (Hh) signalling pathway is crucial for stemness and tissue development and is aberrant in the majority of cancer types (209, 210). The pathway involves a cascade of events starting with Patched (Ptch) receptor degradation followed by Smoothened (Smo) protein activation and finally activation of Gli transcription factors that directly regulate gene expression of many cancer-relevant genes (e.g. invasion-related genes BMI, SNAIL, metastasis-inducing genes VEGF-A, VEGF-B, VEGF-C) (211-214). The Hh pathway is a recognized target for anticancer therapeutics with three Smo inhibitors already being approved by the FDA(215). However, significant side effects associated with these treatments and the emergence of Smo-independent activation of Hh urge the development of inhibitors that target the final step of the pathway â Gli (216). 42 Infante et al. (2015) reported the first-in-class inhibitor of Gli1-DNA interaction, Glabrescione B (GlaB), and identified hot spot residues on the surface of Gli1 DNA-binding domain that mediate the successful inhibition (217). Notably, authors assumed that the residues that contribute to the most to protein-DNA binding energy (hot spots) should also be targeted with potential inhibitors. Therefore, they studied Gli1-DNA binding through MD simulations of mutants to derive ÎG energy contributions for each residue and validated their findings through functional assay with HEK293T cells including luciferase reporter, proliferation, ChIP and immunohistochemistry assays. Authors virtually screened 800 natural compounds against a docking pocket on the DNA-binding surface and selected only those compounds that formed bonds with the identified hot spot residues, Lys340 and Lys350. After the list was further filtered with MM/GBSA calculations, six compounds were advanced into experimental validation in a luciferase reporter assay revealing the main hit GlaB (Figure 2-6). This molecule was then confirmed to directly bind Gli1 via Lys340 and Lys350 residues by NMR spectroscopy and EMSA (IC50 of ~30â¯Î¼M). Thus, the study claimed that the identified hot spot residues are crucial for Gli1-DNA recognition and represent key pharmacophores for successful inhibition. Importantly, GlaB significantly reduced the expression of Gli target genes and demonstrated antineoplastic activity in vivo in GLI-dependent medulloblastoma and basal cell carcinoma. Further studies supported the therapeutic value of GlaB due to its ability to inhibits Glioma cell growth (treatment with 5â¯Î¼M for 48â¯h) (218). The structure of GlaB was later used to develop a pharmacophore model used to screen the Asinex and the AKos chemical libraries in pursuit of a novel Gli1-DNA interaction inhibitor (219). The identified hit compound, α-mangostine, suppressed Hh signalling and inhibited the growth of melanoma and medulloblastoma cell lines (at concentrations of 2.7 and 1.9 μM, respectively). Molecular docking revealed a favourable binding pose where α-mangostine binds Lys340 and Lys350 supporting the 43 earlier proposed pharmacophore. Interestingly, two independent studies also linked antineoplastic activity of α-mangostine to Gli1 inhibition further supporting computationally predicted binding (220, 221). To sum up these studies of Gli target demonstrate that hot spot residues are useful structural pharmacophores that enable researchers to identify potent protein-DNA inhibitors. A similar concept has been long acknowledged in the development of protein-protein interaction (PPI) inhibitors â residues responsible for PPI binding also happen to be ligand binding (222). Figure 2-6. Compound Glabrescione B (GlaB) targeting Gli1âDNA interaction site. (a) GlaB inhibits the interaction between Gli1 (gray) and DNA (cyan ribbon). (b) Magnification of the interactions between compound GlaB and Gli1 protein residues. Hydrogen bonds are shown as red-dashed lines and hydrophobic interactions as green-dashed lines. Structural information was extracted from171 and Protein Data Bank (PDB) entry 2GLI. 217 44 2.10 DNA Topoisomerase II (TopoII) DNA Topoisomerase II (TopoII) is a critical enzyme that regulates DNA topology during DNA replication and transcription (223, 224). TopoII performs DNA relaxation and decatenation via a series of highly coordinated steps that involve protein-DNA binding, DNA cleavage and subsequent re-ligation (225). TopoII is highly overexpressed in a variety of cancer cells and remains an utterly important drug target; several widely-used FDA-approved drugs act by forming genotoxic TopoII-DNA cleavage complexes (etoposide, doxorubicin, mitoxantrone, amsacrine) (226-232). However, the toxic nature of these chemicals causes severe side effects including myelosuppression and secondary malignancies (226, 233-235). In contrast, catalytic inhibitors that block enzyme activity without the formation of the genotoxic complexes are deemed to be safe albeit rather ineffective (236). Notably, our recent study demonstrated that catalytic inhibitors of TopoII suppress therapy-resistant forms of PCs both in cell lines and in xenograft models (237). In particular, to develop a potent and safe catalytic inhibitor of TopoII, we initiated a VS campaign where we utilized a previously unexplored pocket on the surface of the DNA-binding domain to find an inhibitor that would directly block TopoII binding to DNA (238). The binding site comprised of residues Gly721, Gln726, Lue771, Asn779, Asn851 and Trp931 was identified through computational analysis of volumes and amino acid compositions of protein cavities. We screened 6 million drug-like compounds derived from the ZINC15 database using Glide and eHiTS docking software followed by ADMET filtering (37, 239, 240). After the initial VS run, the most potent compound was used as a query to broader search for close analogs. This led us to the discovery of T60 molecule (Figure 2-7) which inhibited TopoII in both decatenation- and relaxation assays relaxation assays (IC50 of ~0.3 µM and ~4.7 µM, respectively) (238). T60 treatment decreased cell proliferation in K562 and HeLa cell lines and suppressed tumour growth 45 in K562 xenografts. The proposed mechanism of action via direct inhibition of protein-DNA interaction was supported by EMSA experiment. We analysed the binding mode of T60 through MD simulations and observed critical hydrogen bonds formed with Gly721, Lys723 and Gln726 and strong hydrophobic attraction with residues Asn851 and Trp931. These findings will guide further structural optimization efforts. Thus, this pilot study served as a proof-of-principle for the development of a catalytic inhibitor that directly blocks protein-DNA binding (a previously reported compound with the same mechanism of action, merbarone, exhibits a controversial intercalating activity (241, 242)). Importantly, it illustrates that computational analysis of a protein surface could identify a druggable site that confers inhibition. Figure 2-7. Compound T60 targeting topoisomerase II (TOP2)âDNA interaction site. (a) T60 blocks the interaction between TOP2 and DNA (cyan ribbon). (b) Magnification of the interactions between compound T60 and its surrounding protein residues from TOP2. Hydrogen bonds are shown as red-dashed lines and hydrophobic interactions as green-dashed lines. Structural information was extracted from192 and Protein Data Bank (PDB) entry 4FM9.218 2.11 Conclusions Protein-DNA interactions remain one of the least explored interaction target types in cancer therapeutics. This is largely because the protein-DNA surfaces are deemed undruggable as they 46 generally lack deep targetable pockets (7). However, as evident from the examples outlined in this review the surfaces might well be druggable with the aid from CADD. Despite the growing interest in protein-DNA inhibition, the potential ligand-binding sites on the surfaces of DNA-binding domains have yet to be thoroughly investigated. We suggest that druggable sites on the surfaces of DNA-binding domains could be identified through various computational techniques. For example, some computational tools predict cavity druggability through hydrophobicity analysis and volume calculations (243). We successfully employed such tool to identify binding sites on the surface of AR DBD and the DNA-binding interface of TopoII (83, 238). In another instance, we used âblind dockingâ technique (binding site is identified based on where the majority of molecules are docked) followed by experimental validation to locate the druggable site (143). In the absence of conventional druggable sites on a protein-DNA surface, one might consider a hot-spot-guided approach to define the binding site. These so-called hot-spots are residues that contribute to most of the protein-DNA binding energies (244). It is logical to speculate that a small molecule binding these residues will effectively inhibit the protein-DNA binding. Indeed, the major overlap between ligand hot-spots and protein-protein interaction (PPI) hot-spots was outlined in a meta-analysis of PPI inhibitors (222). Importantly, this idea was applied for the development of hot-spot-guided PPI inhibitor design strategies such as hot-spot-guided automated pharmacophores and small-molecule inhibitor starting points (245-247). Interestingly, PPIs were once thought to be undruggable too but now we witness a wealth of PPI inhibitors (248). Thus, we should learn established principles and aim to apply them to the domain of protein-DNA inhibition. The aforementioned study on the GLI1-DNA interaction inhibition successfully employed the hot-spot guided principle by selecting compounds that bind predicted hot spot residues (217). Furthermore, we retrospectively evaluated the binding of the VPC-14449 to AR 47 DBD target and observed that it forms bonds with predicted hot-spot residues Tyr576 and Arg591 (83). Similarly, the reported binding pose of the FOXM1 inhibitor FDI-6 forms crucial bonds with predicted hot spots His287, Ser290, Tyr308 (197). TOX inhibitor VPC-190444 also binds predicted hot-spot residues Gln262 and Lys263. STAT3 hot spot residues Lys320 and Val343 bind InS3-54 inhibitor. These retrospective observations prove the utility of hot spot-guided binding site identification. Luckily, the identification of protein-DNA hot spots is now feasible without expensive laboratory testing (e.g. through mutagenesis) as there are now machine learning tools with relatively high predictive power. For example, PrPDH and SAMPDI are open web-based servers that predict hot spots based on structure/sequence features and molecular mechanics, respectively (249, 250). It is also worth mentioning that not every computational binding site identification method could effectively work for each target. Some tools will fail to identify druggable sites for a particular target, while the surface might indeed be druggable with the aim of other CADD approaches. This is especially relevant for unexplored target types such as transcription factors because the majority of tools are trained on (or referenced to) conventional ligand-binding sites. To illustrate this approach, we screened the binding sites of aforementioned targets with computational tools (Table 2-1): SiteFinder (MOE) (251) that estimates pocket volume and assess amino acid composition; and P2Rank web-based tool (252) that predicts binding sites using a machine-learning model trained on points placed on a solvent-accessible protein surface areas. In Table 2, we also included whether the reported binding residues were identified as hot spots using PrPDH web tool(250). For some targets, only hot spot methods allowed to locate the binding site (e.g. FOXM1, Gli1), while for other targets, sites were identified with SiteFinder but not with P2rank (e.g. AR, STAT3, TOX) (Table 2-1). In general, both tools rate these sites considerably lower compared to 48 conventional binding sites. In particular, the authors of PBL score report that an average binding site has a score around 2 (AR, STAT3, TOX, TopoII have significantly lower scores) (253). Similarly, we found that AR LBS or TopoII ATP-site received P2Rank probability score around 0.8, while all studied DNA-binding sites were scored considerably lower (Table 2-1). Thus, we conclude that transcription factor binding sites might remain undetected if referenced to conventional ligand-binding sites. Table 2-1. Binding site druggability assessment. PBL score(253) was computed with SiteFinder package available in MOE(251). P2Rank probability score generated using P2Rank web-based tool(252). Hot spot residues predicted with PrPDH web-based tool(250). NF â not found. Another obstacle that was used to argue for the undruggability of protein-DNA interfaces is the high conservation of DNA-binding domains between members of a protein family that makes it hard to achieve specificity. One prominent example is the highly homologous DNA-binding domains of hormone nuclear receptors (AR, ER, GR, PR)(254). However, the modern structure-based drug design tools (e.g. molecular docking, molecular dynamics, pharmacophore modelling) SiteFinder PLB score P2rank probability score Hot spots AR 1.13 NF Found ERG 1.84 0.36 NF STAT3 1.06 0.014 Found TOX 0.56 NF NF Myc-Max 3.14 0.29 Found FOXM1 NF NF Found AP-1 1.7 0.53 NF Gli1 0.13 NF Found TopoII 0.84 0.005 NF 49 allow designing inhibitors that preferentially bind sites that differ only by a few residues compared to their family members. Examples of rationally designed selective inhibitors include VPC-14449 which selectively binds AR, VPC-18005 designed to bind ERG and InS3-54 specific to STAT3 (83, 96, 129). We argue that high conservation of DNA-binding domains provides an advantage for drug development because the DNA-binding domains tend to be less prone to mutations (104, 255). Thus, protein-DNA inhibitors are less likely to suffer from mutation-induced resistance. For instance, in the case with AR, all currently prescribed anti-AR drugs target the ligand-binding domain, which is highly prone to mutations with some of them being especially malignant by converting inhibitors to agonists (256, 257). Therefore, the development of an inhibitor that targets more mutation-averse DNA-binding surface is an attractive strategy for the antiandrogen therapy. Importantly, AR DBD binder address the problem of fully active ARV truncated isoforms that lack the ligand binding domains entirely (258). Thus, in our opinion, the highly conserved and functionally essential nature of the protein-DNA surfaces presents an opportunity to diminish the drug resistance issue while the desired selectivity could be achieved through rational design. The intrinsic disorder of many transcription factors possesses another challenge for small molecule targeting since these proteins lack defined ligand-binding sites. This notion likely fostered the idea of transcription factor undruggability given that around 90% of transcription factors have at least one intrinsically disordered region (259). However, the advancements in computational technologies enabled researchers to target IDPs. In particular, Yu et al. (2016) successfully targeted Myc by employing so-called âensemble or multi-conformational dockingâ (65, 260) . The extensive sampling of the IDP conformations through molecular dynamics allows capturing transient binding sites that could be used for virtual screening. Additionally, as outlined by Chen et al. (2020), the rise of Graphic Processing Unit (GPU)-computing should further facilitate the 50 feasibility and affordability of extensive MD simulations to study IDPs (261). Furthermore, a meta-analysis of IDPs revealed that they possess almost twice as many binding sites as ordered proteins and these binding sites were claimed to more druggable (262). The authors of the meta-analysis expressed great optimism about IDP targeting with small molecules and claimed that IDPs should no longer be considered undruggable, further supporting the shift from transcription factorsâ undruggability. It is important to mention that CADD also helped to link the activity of some known drugs to direct protein-DNA inhibition MOA. For example, several inhibitors of FOXM1 were identified to bind to the DNA-binding interface using MD simulations. The elucidated binding modes could serve as pharmacophore models for drug design efforts. An inhibitor of Fos/Jun was also studied with induced molecular docking and MD simulations to locate the binding site and gain understanding of the inhibition mechanism. In summary, protein-DNA interactions are crucial for the regulation of gene expression and implicated in numerous types of cancer. Significant advancement of small molecule databases, large-scale virtual screening and other computational tools have greatly improved the feasibility of targeting protein-DNA sites. This review highlights several successful examples of drug development to inhibit protein-DNA interactions in key cancer targets such as AR, ERG, MYC, TOX, TopoII through the use of large-scale in silico compound screening, followed by in vitro validations. Small molecule protein-DNA inhibitors have also been reported for other targets including STAT3, PAX2, FOXM1, AP-1 and GLI. We anticipate recent development in artificial intelligence and deep learning platform will further facilitate drug target identification (263), protein structure prediction (264), accelerated virtual screening (265), and de novo drug design 51 (266). Taken together, these approaches will enable therapeutic targeting to protein-DNA interactions from over 1600 human transcription factors in diseases even beyond cancer. 52 Targeting Androgen Receptor (AR) 3.1 Introduction The critical role of AR in prostate cancer (PC) is well-established, with AR activation being a key driver of the disease (267-272) Traditional therapeutic strategies, such as androgen deprivation and direct antagonism with small molecules, have been foundational in PC management. However, the development of castration-resistant prostate cancer (CRPC), often unresponsive to conventional antiandrogen treatments, presents a formidable clinical challenge (273-275). This resistance sometimes arises from mutations in the androgen-binding site (ABS) or splicing of the ligand-binding domain (LBD), with variants like AR V7 driving the disease progression independent of androgens (276, 277). Addressing these challenges, this chapter presents an innovative therapeutic strategy: targeting the DNA-binding domain (DBD) of AR using novel small molecule inhibitors. By shifting focus from the LBD, this approach aims to tackle drug-resistant forms of PC, employing methods like direct inhibition of the protein-DNA interaction site (P-box) and disruption of AR DBD dimerization (D-box). In the context of computational drug discovery (CADD), this chapter highlights significant technical advancements. The use of DeepDocking, an AI-facilitated tool, enabled us to explore previously uncharted chemotypes, moving beyond conventional molecular libraries. This approach exemplifies how AI and machine learning can expand the horizons of drug discovery, particularly in identifying compounds with unique mechanisms of action against protein-DNA interactions. 53 In this chapter, we emphasize the criticality of carefully selecting CADD tools for specific tasks. Due to the distinctive shallow and charged nature of protein-DNA interaction surfaces, it's essential to choose appropriate tools. Not every molecular docking software can accurately navigate these unique environments to predict the correct molecular configurations and estimate binding energies precisely. Therefore, a comprehensive evaluation using benchmark data is indispensable to validate the accuracy and efficacy of these tools. Throughout this chapter, one of the key focuses is the integration of experimental data into CADD modelling. This integration is crucial for establishing a reciprocal relationship between computational predictions and laboratory validations. By aligning CADD techniques with experimental results, we enhance the precision and efficacy of drug discovery. This harmonious blend of computational and experimental methods showcases the extensive potential of CADD in developing new therapeutics, particularly for the complex task of targeting protein-DNA interactions. In sum, this chapter not only presents a shift in AR targeting strategies but also illustrates the transformative impact of CADD in identifying novel inhibitors for complex molecular targets. 3.1.1 AR Structure and Function AR is a member of the nuclear receptor (NR) family, consisting of 48 members, many of which have been extensively studied for their role in cancer development and progression (113, 278, 279). All NRs are modular in organization and composed of the following regions: N-terminal regulatory domain (NTD), DNA binding domain (DBD), hinge region, and ligand binding domain (LBD) that may contain ligand binding site (LBS) (Figure 3-1). In AR, testosterone and dihydrotestosterone (DHT) bind to the LBD, leading to a conformational change in AR. After ligand binding in the cytoplasm, AR translocates to the nucleolus, forms a dimer, and binds to the 54 promoters and/or enhancers of NR-regulated genes target genes through a zinc-finger of the DBD. AR DBD consist of two α-helixes: P(roximal)-box ârecognition helixâ (amino acids 577â581; GSCKV) that directly binds transcription factor motifs of the downstream-regulated genes and D(istal)-box (amino acid 596â600; ASRND) that is responsible for AR dimerization (272, 280) (Figure 3-1). Figure 3-1. AR DBD structure and druggable pockets. Top Left: Domain structure of AR. Bottom: Crystal structure of AR DBD dimer (PDB: 1R4I(280)). In blue: D-box with VPC-17005 docked. In red: P-box with VPC-14449 docked. White ribbon corresponds to DNA. 3.1.2 ARâs Role in Prostate Cancer AR controls the expression of various genes located downstream of the androgen-response element including but not limited to secreted proteins (KLK3, KLK2), fusion genes (TMPRSS2-ERG), growth stimulators (IGF1R, APP), PI3K modulation (FKBP5), transcription factors (NKX3.1, FOXP1), a metabolic enzyme (CAMKK2), cell cycle regulators (UBE2C, TACC2), and glucuronidation (UGT1A1)(281). AR is the main driver of PC as it activates the transcription of a variety of oncogenes and stimulates cancer cell growth (267-272). A common example of AR-55 mediated activation is the activation of the TMPRSS2-ERG gene, which occurs in roughly half of all CRPC cases and plays a crucial role in the disease's development and progression(282). Despite initial downregulation in response to androgen deprivation therapy, TMPRSS2-ERG expression is reactivated by the AR in CRPC, potentially contributing to tumour progression. The AR not only activates but also represses transcription in PC (283). In particular, the AR deactivates several tumour suppressors, thereby contributing to the disease's progression. For example, PTEN, a critical gene for tumour suppression, often finds itself downregulated or functionally inactivated due to AR signaling, leading to enhanced cell survival and proliferation (284). Similarly, NKX3.1, a prostate-specific tumour suppressor, is suppressed in certain contexts by AR, furthering the progression of the cancer (285). The interaction of AR with p53, a widely recognized tumour suppressor, results in diminished p53 activity, promoting tumourigenesis (286). Additionally, the Retinoblastoma 1 (RB1) protein, essential for controlling the cell cycle, is inhibited by AR signalling, leading to unchecked cell proliferation (287). Even SPINK1, while not a traditional tumour suppressor, exhibits an inverse relationship with AR signalling; in cases of overactive AR, SPINK1 expression can be downregulated, which is associated with more aggressive tumour behaviours (288). This complex interaction between AR signalling and various tumour suppressors underscores the multifaceted role of AR in the development and progression of PC. 3.1.3 Current AR Targeting Drugs and Novel Mechanisms of Inhibition AR is a well-known master regulator of PC, which is the second most common malignancy in men and one of the leading causes of cancer-related deaths worldwide (289). Androgen receptor pathway inhibitors (ARPI) are the standard of care to treat locally advanced, recurrent, or metastatic forms of PC. While these treatments are initially effective, the disease almost inevitably 56 develops drug resistance and recurs as a more aggressive CRPC. The identified resistance mechanisms involve gain-of-function mutations (290, 291) in the AR androgen binding site (ABS) and expression of constitutively active AR splice variants (ARVs) lacking the entire C-terminal segment, such as ARV7 and ARv567es (292, 293). The currently used first-generation anti-androgens flutamide, bicalutamide, nilutamide and the second-generation enzalutamide, apalutamide and darolutamide all exhibit the very same mechanism of action and bind to the LBS site of the AR, competing with natural androgens (294, 295). However, certain mutations in the LBS can result in its activation by weak adrenal androgens, steroidal and non-steroidal ligands (296), and can even convert AR inhibitors into agonists (275, 297-299). This leads to more aggressive CRPC that does not respond to common antiandrogen treatments (273-275). The mechanisms underlying such resistance include gain-of-function point mutations in the ABS that prevent antagonistic action of the drugs as well as complete splicing of the ligand-binding domain (LBD) (275, 300-302). For example, a well-characterized splice variant AR V7, lacking the LBD, was shown to activate gene expression of various oncogenes and drive the progression of PC in an androgen-independent manner (276, 277, 303). Therefore, the development of new therapeutics that target AR signaling through other functionalities than blocking the androgen binding site is crucial to alleviate the issue of resistance and associated PC mortality. Since the binding of AR-DBD to DNA is essential for the functioning of both full-length AR and ARVs, targeting the AR-DBD and DNA interaction represents an entirely novel and promising therapeutic approach to CRPC that could address the problem of drug resistance, as highly conserved AR DBD is expected to be less prone to mutations (255). However, a very high degree of sequence conservation of DBD among major nuclear receptors prevented its prior consideration 57 as a potentially targetable site as it presents a challenge to develop a specific inhibitor (304, 305). Nowadays, recent advancements in computational modelling enable the development of inhibitors with selective mechanisms of action. Thus, we have previously reported (83, 306) the discovery of AR DBD inhibitor VPC-14449 from virtual screening and medicinal chemistry optimization. It was fully characterized as a DBD/DNA interaction disruptor with an IC50 at 0.31 µM in the AR transcriptional assays. To achieve this potent and yet very selective inhibition of AR DBD, we employed homology modelling to build human AR-DBD structure and analyzed the corresponding protein surface for the presence of targetable pockets that could yet distinguish AR from very close NR homologues. The identified site, involving residues Ser579, Val582, Phe583, Arg586, Gln592, Tyr594, and Lys610, was used to perform a docking-based virtual screening of a 3 million compound library (Figure 3-2). Importantly, we found that residues at positions 591-593 constituting the binding site are not conserved among NRs providing an opportunity for selectivity (Figure 3-2). Thus, our lead compound VPC-14449 was confirmed to be specific to the AR through experimental testing. Furthermore, we demonstrated that VPC-14449 suppresses CRPC cell lines exhibiting clinically relevant forms of resistance including truncated ARVs(258) (the binding pose of VPC-14449 is illustrated in Figure 3-2). Importantly, through mutagenesis analysis, we demonstrated that VPC-14449 binds AR-specific residues Lys591, Gln592 and Lys593 and thus exhibits selectivity(258). Furthermore, VPC-14449 appeared to be ineffective in an AR construct where residues 591-595 (KQKYL) were replaced with glucocorticoid receptor motif (QHNYL) further proving its specific AR binding(258). These findings indicate that selective targeting of AR DBD sites represents a very novel and original strategy that can help overcome the problem of antiandrogen resistance. In the following study, Xu et al.(307) performed optimization of our AR DBD prototype inhibitors 58 VPC-14228, and identified another DBD binder SKLB- C2807, with a similar level of AR inhibitory potential (IC50 at 0.38 µM). Similarly, Lee et al.(308) designed MTX-23 based on proteolysis targeting chimera (PROTAC) using our lead AR DBD inhibitor VPC-14449. The developed compound showed inhibitory activity against both AR-V7 and full-length AR with a half-maximal degradation concentration (DC50) of 0.37 and 2 μmol/L, respectively. Figure 3-2. Compound VPC-14449 targeting the AR (AR)âDNA interaction site. (a) VPC-14449 (orange sphere) blocks the interaction between the AR dimer (purple and red ribbons) and DNA (cyan ribbon). (b) Magnification of the interactions between compound VPC-14449 and its surrounding protein residues (hydrogen bond as red-dashed lines and hydrophobic interactions as green-dashed lines). Structural information was extracted from Protein Data Bank (PDB): 1R4I. (c) The multiple sequence alignment of DNA-binding domains (DBDs) on AR and its close homologs, including the estrogen receptor (ER), glucocorticoid receptor (GR), and progesterone receptor (PR). Alignment was performed with Clustal Omega, visualized by Jalview, and coloured by percentage identity. Interacting residues with VPC-14449 are indicated by red boxes; residues conferring resistance are in solid red. Residue numbering is based on AR (UniProt ID: P10275-1). Another strategy to target the AR DBD is the disruption of its homo-dimerization, which is essential for the activation of all forms of AR, including full-length and splice variants (309-312). Indeed, while monomers of full-length AR could still interact with androgen response elements to a minimal extent, monomers of AR-V7 seem to be completely non-functional (313). 59 In a previous study (314), we identified a druggable binding site on the surface of the D-box and developed small molecules that disrupt the AR functional dimerization. Our lead D-box inhibitor VPC-17005 suppressed androgen signalling, demonstrated AR specificity through mutagenesis studies, and inhibited the growth of AR-positive cancer cell lines. However, VPC-17005 demonstrated rather poor metabolic stability making it a non-suitable candidate for in vivo studies and future optimization. However, the inconsistent structure-activity relationship and problematic pharmacokinetic profile of VPC-14449, VPC-17005 its analogues urged us to search for other inhibitors of AR-DBD. Thus, while previous studies provided foundational knowledge and served as a proof-of-principle for DBD targeting, we decided to initiate a new round of VS to find novel chemotypes of AR DBD inhibitors. 60 3.2 CADD of Novel AR Inhibitors 3.2.1 Docking Benchmarking To identify novel chemotypes that target AR DBD, we employed an in-house computer-aided drug design (CADD) pipeline that relies on Deep Docking (315). For both of the sites on the surface of AR DBD, P- and D-box sites, we initiated separate in silico screenings. First, to select which docking software to use in the pipeline, we assessed the performance by testing how well each software separates previously identified active compounds from generated decoys (compounds with similar chemical properties but different structures; presumably inactive) (20, 95, 314, 316). For the P-box, we have chosen 11 compounds that suppress AR full-length and AR V7 transcriptional activity (Table 3-1). For D-box, we selected 17 compounds that were active in AR full-length assay and showed anti-dimerization effects in previous studies (Table 3-2) (314, 316). For each subset, we then generated 50 decoys per active molecule using the Database of Useful Decoys: Enhanced (DUD-E) (317). We then docked the active compounds and corresponding decoys using four docking software available in our laboratory: Glide, Internal Coordinate Mechanics (ICM), OpenEye FRED (92) and AutoDock (318). We then calculated the Area Under the Receiver Operating Characteristic Curve (ROC AUC) scores with the output docking scores to estimate how well each software separates actives from decoys. 61 Table 3-1. List of active AR DBD P-box inhibitors used for docking benchmarking. Structure ID FL eGFP IC50 PSA IC50 Microsomal stability (T1/2) DHT displ. MTS IC50 (LNCaP) 14103 0.52 0.51 240 14203 3.27 3.91 14204 9.16 10.60 14228 0.23 0.18 10 320 0.1 14292 0.61 0.58 215 0.8 14320 4.12 2.26 14365 0.23 0.15 9 270 0.3 14367 0.30 0.09 15 270 0.05 14368 0.01 0.02 19 215 0.1 14370 0.07 0.17 15 220 0.1 14378 7.35 8.08 14385 0.62 0.69 14402 0.66 0.57 10 0.8 62 14440 0.08 0.04 14449 0.12 0.20 14 0.4 14451 0.33 0.44 12 0.8 14500 0.05 7 FL eGFP IC50 â IC50 (uM) of inhibition of AR full length transcriptional activity in LNCaP cell line PSA IC50 - IC50 (uM) of inhibition of prostate-specific antigen (PSA) production in LNCaP cell line Microsomal stability â t1/2 (min) measured in rat liver microsomes DHT displacement â displacement of endogenous DHT from the ABS site MTS â IC50 (uM) of the inhibition of the growth of prostate cancer cell lines LNCaP Table 3-2. List of active AR DBD D-box inhibitors used for docking benchmarking SMILES ID V7 %inh at 10uM eGFP FL % (12 uM) FL eGFP IC50 17005 0.8 17116 10 111 25 17121 10 73 15 17156 100 44 25 17159 42 56 10 17160 70 76 2 17166 90 63 17177 80 43 17178 80 28 17179 80 17 17181 90 43 17184 80 73 >25 17199 50 95 10 17226 45 76 5 17247 45 102 8 17248 90 125 3 17249 17281 90 5 V7 % inh at 10 uM â inhibition of AR V7 transcriptional activity in LNCaP cell line at 10uM eGFP FL at 12uM â inhibition of AR full-length transcriptional activity in LNCaP cell line at 12uM FL eGFP IC50 â IC50 (uM) of inhibition of AR full-length transcriptional activity in LNCaP cell line The ROC AUC is an essential tool in assessing the effectiveness of binary classifiers, like docking software, in distinguishing between two classes - in this case, active compounds and decoys. By plotting the true positive rate (sensitivity) against the false positive rate (1 - specificity) at various threshold levels, the ROC curve provides a comprehensive picture of a classifier's performance 64 across different situations. The AUC, quantifying the area under this curve, serves as a singular metric summarizing this performance. A high AUC value indicates that the software is more likely to correctly differentiate between actives and decoys, demonstrating its accuracy and reliability in predicting molecular activity. In our analysis, for both sites, Glide performed the best with 0.61 and 0.81 ROC AUC scores (P-box and D-box, respectively) followed by ICM (ROC AUC of 0.59 and 0.75). The performance of FRED and Autodock on both sites was close to random with AUC scores around 0.5. Thus, FRED and AutoDock were not considered for further use. Figure 3-3. Docking Software Benchmarking for P-box (left) and D-box (right) of AR DBD. 3.2.2 Virtual Screening Pipeline Next, we screened an ultra-large library comprising 1.4 billion molecules from the ZINC20 database(29). The initial screening was performed using our in-house developed AI-based tool that accelerates docking called DeepDocking(47). Briefly, a random sample from the ZINC20 database was first docked with GlideSP and used to train a neural network that predicts docking scores for the site of interest. The docking scores of the remaining molecules were then predicted and the 65 best-predicted molecules were used to train another neural network. This process is repeated reducing the number of molecules left after each iteration and enriching an average docking score. Thus, from 1.4 billion compounds passed through five iterations of DeepDocking, we ended up with 15 million and 17 million compounds with predicted low docking scores for P-box and D-box, respectively (Figure 3-4A). These molecules were then docked with GlideSP and compounds that exhibited scores better than those of our previously identified inhibitors (-4.7 for VPC-14449 targeting P-box; -7.5 for VPC-17005 targeting D-box) were selected. We then performed consensus scoring as it was shown to be an effective strategy to improve docking results and enrich the selection set (319, 320). Informed from the decoy experiment that Glide and ICM perform the best on both sites, we docked the remaining compounds with ICM software and computed distances between Glide-predicted and ICM-predicted poses. The compounds with predicted poses differing by more than 2 Angstroms were removed from the selection set. We then removed reactive compounds and those with non-drug-like permeability (logP outside of the 1-4 range). These criteria were based on the properties of active compounds from previous screening campaigns (20, 314). Finally, we eliminated compounds with predicted poor ADMET profiles using the ADMET predictor by Simulations-Plus®. To perform the final selection, we constructed pharmacophore models using the knowledge from our previously identified hit compounds (20, 314, 316). For P-box, we learned through mutagenesis studies (with VPC-14449) that interaction with Tyr594 is necessary for binding. Thus, hydrogen bonding with Tyr594 was set as a necessary pharmacophore feature and compounds with a docking pose lacking this interaction were filtered out (Figure 3-4C). For D-box, we examined the binding poses of previously published inhibitors (i.e. VPC-17005, VPC-17160, VPC-17121, and VPC-17281) and derived a pharmacophore model containing two aromatic features and one 66 hydrogen bond acceptor (Figure 3-4B). Importantly, we ran a 50 ns molecular dynamics simulation with VPC-17005 in the D-box site to determine the relative importance of the interactions. We observed that the hydrogen bond formed between the amide group of the compounds and the cysteine-bound zinc atom was maintained throughout the whole simulation. Thus, the presence of a hydrogen acceptor at the junction between two aromatic groups was considered an essential feature and was used to filter down the D-box selection set. Finally, we applied pKi scoring implemented in Molecular Operating Environment (251) (MOE) for both sets to diversify scoring methods and reduce docking-inherent biases during final selection. The compounds (~8K for P-box and 5K for D-box) that passed all stages of filtering were then ranked by glide score, glide ligand efficiency score, ICM score and pKi score. The top 20-40 molecules from each ranking were selected. The ranking by different metrics allows diversifying the selection set as scorings have implicit biases. The final sets were purchased for experimental validation and contained 160 and 270 compounds for the P-box and the D-box, respectively. 67 Figure 3-4. In silico screening against P-box and D-box on the surface of AR DBD. A) CADD pipeline workflow. The number on both sides indicate the amount of molecules left after each step of filtering. C) Pharamcophore model built to select compounds for D-box. The model was based on common features of compounds VPC-17005, VPC-17160, VPC-17121, and VPC-17281 (overlaid in greed). D) Pharmacophore model for P-box selection developed based on the binding mode of VPC-14449. 3.3 Experimental Evaluation of AR Inhibitors The purchased compounds were first tested for the inhibition of full-length AR transcriptional activity in LNCaP cells. An enhanced green fluorescent protein (eGFP) under the control of a probasin-derived promoter was expressed when AR was transcriptionally active (321). Therefore, molecules that inhibit AR transcriptional activity lower the expression of eGFP protein. We first 68 screened for molecules that showed over 50% AR inhibition at 10µM concentration. From the P-box series, 23 compounds showed more than 50% inhibition at 10µM concentration while from the D-box, 54 compounds passed the set threshold. Hit rates from the initial screening were equal to 14% (23 out of 163) and 20% (54 out of 270) for P-box and D-box, respectively (these hit rates surpass our initial screens of 10% for P-box and 8% for D-box (20, 314)). We next treated these LNCaP cells with increasing concentrations of the short-listed molecules. By quantifying eGFP levels for each concentration, we could determine the IC50 of the studied molecules. The IC50s ranged from 0.28 to 26.8 µM. Hit compounds VPC-17493 and VPC-14668 exhibited IC50s of 0.28 µM and 0.9 µM, respectively (Figure 3-5A, B). Additionally, we tested the expression of AR-regulated prostate-specific antigen (PSA), which is a well-established biomarker of PC (322), in response to treatment with the selected hit compounds. We observed a dose-dependent reduction in PSA production with IC50s of 0.3 and 0.9 for VPC-14668 and VPC-17493, respectively (Figure 3-5C). To make sure that our lead compounds also inhibit AR-V7 and do not bind to the LBD, we evaluated their effect on V7 transcriptional activity. PC3 cells stably expressing an inducible V7 and an ARR3tk-NanoLuc luciferase reporter were induced by doxycycline and then treated with increasing concentrations of lead compounds. Thus, the inhibition of NanoLuc luciferase reflects the inhibition of AR-V7 transcriptional activity. To exclude off-target effects of the molecules, PC3 cells stably expressing the reporter by itself were used. Many molecules that showed AR inhibition in the eGFP assay did not inhibit V7 activity, suggesting an effect through binding to the LBD. Our lead molecules, VPC-17493 and VPC-14668, exhibited a dose-dependent inhibition of V7 transcriptional activity, however with much weaker IC50 than the ones calculated for full length: ~10 and 12 µM, respectively (Figure 3-5D). Both VPC-17493 and VPC-14668 were 69 inactive in this counterscreen assay excluding an off-target effect or direct interference with the NanoLuc luciferase (Figure 3-5E). 70 Figure 3-5. Identified hits, VPC-14668 and VPC-17493, inhibit transcriptional activity of AR full-length and V7. A) Left: structure of VPC-14668; Right: structure of VPC-17493 B) Inhibition of AR full-length transcriptional activity in eGFP reporter assay in LNCaP cells.C) The inhibition of AR-regulated PSA expression in LNCaP cells. D) Inhibition of AR V7 transcriptional activity in NanoLuc luciferase reporter assay in PC3 cells E) Conterscreen in PC3 cells expressing NanoLuc luciferase without the V7 to evaluate off-target effects. Data points represent mean ± SD (N=3). 71 3.4 Discussions AR plays a key role in the development and progression of PC. Conventional AR blockers (AR antagonists) bind to the androgen binding site within the LBD, which is susceptible to mutations or truncations that confer resistance to available treatments. In our previous studies, we showed that inhibiting AR at DBD is an effective strategy to block AR transcriptional activity. Herein, we demonstrate how the use of an AI-accelerated CADD campaign helps to identify novel AR inhibitors. The hit compounds, VPC-14668 and VPC-17493, were discovered through screening campaigns that targeted the DNA-binding interface site, namely the P-box, and dimerization site, namely the D-box, respectively. Both of the compounds effectively block the transcriptional activity of AR full length and the secretion of PSA, a PC biomarker, in a PC cell line. Furthermore, VPC-14668 and VPC-17493 inhibited AR V7 transcriptional activity, however less potently than full length. This discrepancy in potency is most probably due to the use of different cell models and the higher expression levels of V7 in the tested cell line (323). Importantly, PC cells that acquire AR V7 become resistant to widely used anti-androgens such as enzalutamide and drive the development of highly metastatic forms of PC (302, 324). Thus, the development of candidate drugs that target AR V7 represents an attractive alternative therapeutic strategy. This study demonstrates how the use of CADD techniques enables finding novel chemotypes with desired bioactivities. In particular, Figure 3-6 highlights that the newly developed compounds are significantly different from any reported AR inhibitors. Our study further adds to the growing body of research advocating for ultra-large screenings in drug discovery (39, 43, 325, 326). Both of the identified hits, VPC-14668 and VPC-17493, represent unexplored chemical scaffolds that are not present in the standard âin-stockâ libraries of the ZINC20 database (29). These compounds are stored in the âmake-on-demandâ libraries that grow each year and are projected to exceed 100 72 billion soon (327). Thus, the identification of those compounds would be unlikely with conventional molecular docking techniques that allow the screening of up to a few million compounds with a generous amount of computational resources. Therefore, as the available chemical space grows each year, the use of tools that accelerate virtual screening, such as DeepDocking, is essential to effectively screen for new potent drug candidates. Figure 3-6. Structural information and associated Tanimoto scores of closest analogs of the selected hit compounds among all reported AR inhibitors (sourced from CHEMBL) and our published inhibitors. 73 The analysis of docking poses of the hit compounds revealed shared binding features with our previously identified hits. In particular, the P-box targeting compound VPC-14668 forms a strong hydrogen bond with Tyr594 (-2.1 kcal/mol) through an oxygen atom in a similar manner to VPC-14449âs morpholine ring (Figure 3-7A)(20). Importantly, this bond was conserved throughout 70% of a 50ns molecular dynamics simulation illustrating the importance of the bond. Such large retention of the bond throughout the simulation indicates that the bond is stable and has a large energetic contribution to the binding affinity. This observation is in agreement with the previously identified loss of activity of compounds VPC-14449 in mutant Tyr594Asp AR DBD(20). Therefore, we conclude that hydrogen bonding with Tyr594 is an essential binding feature for AR DBD inhibition at P-box. Interestingly, residue Tyr594 is likely to be a so-called âhot-spotâ residue (i.e. residue with a strong contribution to protein-DNA binding affinity) according to predictions with PreHots web server (328). Thus, it is plausible that disruption of DNA interaction with Tyr594 is required to suppress AR transcriptional activity. These findings support our previous claim that the structure-based design of molecules that bind to hot spot residues represents a viable strategy to disrupt protein-DNA interactions (10). Similarly, VPC-17493 resembles binding modes of published inhibitors including VPC-17005, VPC-17160 and VP-17281(314, 316) (Figure 3-7B). The hit compound also contains an amide group that forms critical hydrogen bonding with the zinc atom and neighbouring cystines. Importantly, molecular dynamics analysis showed that this interaction was also stably maintained throughout the whole simulation confirming that the selected inhibitor forms a strong bond with the zinc ion residing in the D-box. Additionally, strong hydrogen bonding with Ala597 and Asn611 was observed throughout the simulation. Therefore, VPC-17493 further exemplifies our binding hypothesis for disruption of AR dimerization where an amide group bound to the zinc atom and two hydrophobic groups represent an active scaffold. 74 Figure 3-7. Ligand-protein interactions of VPC-14668 (A) and VPC-17493 (B). Residues in green â hydrophobic, in purple â polar. Blue shading â solvent-exposed areas 3.5 Conclusions This chapter has showcased the successful application of CADD in identifying novel inhibitors targeting AR. Our AI-enhanced virtual screening led to the discovery of two unique chemotypes, each addressing a specific site on the AR: the protein-DNA interface and the dimerization site. The elucidation of their binding modes through molecular docking and dynamics has been pivotal in understanding their inhibitory mechanisms. The significance of this research lies in its demonstration of CADD's potential to navigate complex molecular landscapes, particularly in targeting protein-DNA interactionsâa traditionally challenging area in drug design. The compounds identified here represent a significant advancement in AR-targeted therapies, highlighting the synergy between advanced computational techniques and targeted molecular intervention. 75 3.6 Materials and Methods Virtual Screening For structure-based virtual screening, we used the crystal structure of rat AR DBD bound to DR3 oligonucleotide (PDB:1R4I) (280). The structure was prepared using Protein Preparation Wizard from Schrödinger (329, 330). In detail, hydrogen atoms were added, the energy of the system was minimized, bond orders were assigned and missing side chains were added with the Prime module. This preparation process ensures reproducibility as the steps are well-defined and can be repeated with the same parameters. A small molecule library was downloaded from the ZINC20 (29) database and prepared with OpenEye Omega software (331). In particular, the preparation process involved protonation, partial charge assignment and energy minimization. The docking with GlideSP (Schrodinger) was performed using standard settings. The docking with ICM was performed using a thoroughness of 5. The docking process with both GlideSP and ICM is deterministic, meaning that running the docking with the same settings will yield the same results, ensuring reproducibility. DeepDocking was set to run for 5 iterations with other steps as outlined in the protocol publication (45). Pharmacophore model development and screening were also performed using MOE software (251). Molecular dynamics (MD) were performed using the Desmond package from Schrodinger. The structure was solvated using the System Builder package from Schrodinger using simple point charge (SPC) water model (332) and neutralized by adding Na+ ions. The system was built using the OPLS3e force field (333). The simulations were carried out with a 2-femtosecond time step using a constant-pressure (NPT) ensemble at 300K and a pressure of 1.03 bar. Each simulation recorded 1000 frames that were then analyzed using the Simulation Interactions Diagram module 76 (Schrodinger). The MD simulations were initialized with a specific seed, which was recorded to ensure that the simulations can be reproduced exactly under the same conditions. Compounds All compounds identified through virtual screening were purchased from Enamine (https://enamine.net/). Plasmids and constructructs Full-length human AR (hARWT) was encoded on pcDNA3.1 expression plasmid. Lentivirus plasmid pLIX402-ARV7 (ARV7 cDNA, doxycycline-inducible promoter, puromycin selection), was a gift from Dr. N. Lack. pLIX402-Na4.2. PC cell lines LNCaP (ATCC, CRL-1740) and PC-3 (ATCC, CRL-1435) cells were obtained in 2013. All the cell lines were biweekly tested for mycoplasma and authentication was confirmed by genomic sequencing or STR profiling. Reporter assays To evaluate the effect of the tested molecules on AR full-length transcriptional activity, we used LNCaP cells incorporating an AR2PB-eGFP reporter construct (2 x probasin) (321). We evaluated the inhibition of eGFP expression and the secretion of PSA assays as described (20, 321). Briefly, LNCaP cells were first cultured in charcoal-stripped serum (CSS) media for 5 days to deplete endogenous androgens, thereby reducing baseline eGFP expression to minimal levels. Following androgen depletion, the cells were treated with a synthetic androgen, such as R1881, at various concentrations to induce AR activity and subsequent eGFP expression. After a 24-hour induction period, the test compounds or inhibitors were added to the wells, and eGFP fluorescence was measured at regular intervals (e.g., every 24 hours) using a fluorescence microplate reader. Cell 77 viability and proliferation were assessed in parallel using MTS assays, ensuring that any observed effects on eGFP expression were due to AR modulation rather than cytotoxicity. To evaluate the effect of our molecules on V7 activity, we used a doxycycline-inducible PC3_iV7 + 3TKNLuc cell line that expresses V7 and V7-activated NanoLuc luciferase reporter (314). To exclude molecules with off-target effects, we also evaluated the molecules in a PC3 cell line that stably expresses the NanoLuc luciferase reporter alone(314) (Counterscreen assay). Cells were induced by 200ng/mL Doxycycline and treated with increasing concentrations of compounds for 24h. Cell lysis and Nano-Glo Luciferase reading (Promega) were performed as previously described (314). Luminescence detection was carried out on a TECAN m200pro plate reader. 78 Targeting Lin28 with the use of CADD methodology 4.1 Introduction In the exploration of novel therapeutic strategies for cancer treatment, the modulation of RNA-binding proteins, particularly Lin28, has emerged as a promising approach (334, 335). This chapter focuses on the development of small molecule inhibitors of Lin28 through CADD. The rationale for targeting Lin28 stems from its significant role in regulating let-7 miRNAs, known for their tumour-suppressive properties. The direct delivery of let-7 miRNAs in vivo has been challenging, primarily due to off-target effects and low efficacy (336, 337). Consequently, targeting Lin28, the key post-translational negative regulator of let-7, presents a viable alternative. Indeed, several identified small molecules successfully inhibited Lin28 and restored let-7 levels serving as a proof of principle for chemical modulation of Lin28 (338-340). Lin28's involvement in cancer progression, especially in PC, further underscores its therapeutic potential. Evidence suggests that downregulating Lin28 significantly impairs PC growth and stemness, and it is crucial in preventing the transition from prostate adenocarcinoma to the more aggressive treatment-emergent neuroendocrine prostate cancer (t-NEPC) (341-343). Furthermore, high levels of Lin28 are associated with enhanced expression of the AR, a primary driver of PC (342). This chapter will delve into targeting the interaction site between the Lin28 zinc knuckle domain (ZKD) and the let-7 conserved motif, GGAG, aiming to disrupt this binding and prevent let-7 degradation. The findings discussed herein not only highlight the potential of CADD in the development of small molecule inhibitors for specific targets like Lin28 but also underscore the broader implications of this approach in the realm of RNA-binding protein targeting. The successful modulation of such proteins could pave the way for new avenues in cancer therapy through the inhibition of RNA-binding proteins, 79 demonstrating the vast potential of CADD in drug discovery and development. The CADD efforts described in this chapter represent a structured and methodical approach to identifying and optimizing inhibitors for Lin28. As illustrated in Figure 5.1, the sequential and iterative nature of these computational methodsâstarting from an initial pilot study and progressively moving through DeepDocking and SAR optimization ânot only highlights the evolution of the project but also underscores the importance of each phase in refining and improving the potential inhibitors of Lin28. Figure 4-1. Overview of the CADD timeline targeting Lin28. This chapter describes the steps starting from DeepDocking. 4.1.1 Lin28 Structure and Function Lin28 is known as one of the four core stem cell genes among Oct4, SOX2, and Nanog that can transform terminally differentiated fibroblasts into inducible pluripotent stem cells (344). Lin28 is the only RNA-binding protein among these genes and has also been reported to regulate SOX2 and Oct4 expression in cancer cells (345, 346). During development, the expression of Lin28 appears as early as the two-cell stage (347) and it functions as the gatekeeper to control the transition between pluripotency and committed cell lineages (348). 80 There are two paralogs of Lin28 protein in vertebrates, Lin28A and Lin28B (349). Those paralogs are usually mutually exclusive in expression in human cancer cells with Lin28A being expressed in MES (human uterine sarcoma) and IGROVE1 (human ovarian carcinoma) while Lin28B is primarily detected in H1299 (human non-small cell lung carcinoma), HepG2 (human nontumorogenic hepatocellular carcinoma), and K562 (human erythroleukemic) cell lines (350-353). Their structures share high homology in protein sequences except that Lin28B has a longer C-terminus containing nuclear localization signal (NLS) and nucleolar localization signal (NoLS) (349, 354) (Figure 4-2). Indeed, various studies confirmed that the cellular localization of the two isoforms is different. Lin28B can be localized throughout the cells even inside the nucleolus, while Lin28A is predominantly localized at the cytosol (349, 354). Both paralogs contain highly conserved RNA binding regions: the N terminal-cold shock domain (CSD) and a C terminal cysteine-cysteine-histidine-cystine (CCHC) â zinc knuckle domain (ZKD) (349). The main function of both paralogs of Lin28 is to bind immature forms of let-7 microRNA family members and prevent them from being synthesized (Figure 4-2). Figure 4-2. Domain structure and crystal structure of Lin28. Cold shock domain (CSD) in green and zinc knuckle domain (ZKD) in blue connected in bi-partite manner by pre-let-7 (white ribbon). The GGAG motif is highlighted in red and the surface of the proposed binding pocket is colored in bright green 81 4.1.2 Let-7 Biogenesis and Function Let-7 miRNAs are key regulators of embryonic development and cancer progression as let-7 functions as a tumour suppressor through direct downregulation of several key oncogenes including RAS (355), c-Myc (356) and HMGA2 (357, 358). Thus, the blockage of let-7 biogenesis reverses the process of tumour suppression and has been linked to tumour development. Let-7 miRNAs are synthesized through several steps (Figure 1). First, the primary miRNA transcripts of let-7s (pri-let-7s) are transcribed from the MIRLET7 genes by RNA polymerase II (359, 360). These transcripts form structures of hairpin stem loops consisting of a stem region, and they have a characteristic pre-element (preE) bulge and a preE loop (359, 361). Second, pri-let-7 is cleaved at the stem-loop structure of the preE by Drosha (a nuclear-localized RNase III) and Pasha (double-stranded RNA binding protein), into a shorter (60-80 nts) pre-let-7 hairpin structure (359). The pre-miRNA is then exported out of the nucleus by exportin-5 (362). Finally, the preE element of pre-let-7 is cleaved by Dicer to produce a mature let-7 miRNA duplex with a 3â overhang in the cytoplasm (361). As shown in Figure 4-3, two paralogs of Lin28 bind to let-7 to prevent them from being further processed (363, 364). The two conserved domains, CSD and ZKD, have a high affinity to let-7 precursors at their terminal loop region of preE (361). Specifically, ZKD binds to the 3â end of the preE bulge containing the GGAG motif, while CSD preferentially binds to the preE loop with the NGNGAYNNN motif (361). Mayr et al. (365) showed that CSD binds to microRNA first and then induces a conformational change in the preE, making it available for ZKD binding. Although CSD has a higher affinity to miRNA, ZKD binding to the sequence is required and sufficient to recruit TUTase enzymes and induce oligouridylation (366). In the cytosol, the Lin28B CSD recognizes the preE loop of pre-let-7 while allowing ZKD to bind with the conserved motif preE bulge within 82 let-7 (GGAG) (367). ZKD binding to miRNA blocks the Dicer cleavage site, which is proximal to GGAG conserved preE bulge, and prevents pre-let-7 maturation (349). It is worth mentioning that some members of the let-7 family can escape Lin28-mediated inhibition (368). Figure 4-3. The Biogenesis of let-7 microRNA. In the absence of Lin28B, pri-let-7s are cleaved into pre-let-7 miRNAs, then converted to mature let-7 with the aid of Dicer in both cytoplasm and nucleus. The stages at which Lin28A and/or B inhibit let-7 maturation are marked. The lower right square shows the structure of pri-let-7. The upper right square demonstrates the site where Dicer cleaves pre-let-7. The left square shows the bipartite binding of Lin28A/B to pri/pre-let-7 members. Created with BioRender.com. 83 4.1.3 Lin28/Let-7 Axis Cancer The role of Lin28A and Lin28B in cancer development and progression has been elucidated over the last decade. Numerous studies have found that overexpression of Lin28A/B leads to reduced levels of let-7 members that stimulate tumour cells to gain stemness and invasiveness. Not surprisingly, clinical studies have confirmed that Lin28 overexpression correlates with higher chances of metastasis and overall poor survival rates as we have summarized the main findings of Lin28 in various cancer types shown in Table 1. The broad range of cancer types and cancer cell lines that rely on Lin28/let-7 regulation indicates a central role of the pathway in cancer biology. Indeed, a comprehensive analysis of Lin28A and Lin28B expression over a panel of 527 human cancer cell lines revealed that the paralogs are overexpressed in roughly 15% of cancer types (352). The plethora of Lin28A/B functions is explained by the abundance of downstream genes including direct targets of let-7, Sox2, HMGA2, and c-Myc (369). Furthermore, various studies where therapeutic inhibition, gene knock out or RNA-silencing of Lin28 have shown to reverse the stemness and invasive phenotypes (Table 1), highlighting that aberrant Lin28 expression controls tumour cell aggressiveness. Additionally, overexpression of Lin28B in transgenic murine models was sufficient to induce hepatoblastoma and hepatocellular carcinoma while deletion or silencing of the Lin28B gene prolonged the survival (370). These studies collectively demonstrate the key importance of Lin28A/B in the development and progression of cancers. Table 4-1. Lin28 Roles in Cancers. Lin28A and B expression in human cancer cell lines were confirmed from Cancer Cell Line Encyclopedia (CCLC) Dependency Map database Primary Body Site Tumour Name Samples Studied Lin28A or B expression Results Ref Lung Non-Small Cell Lung Cancer (NSCLC) Lung tissues; Cell lines: A549, NHBE A549: both NHBE: n/a (1) Lin28B and metabolic enzyme glycine decarboxylase (GLDC) â in tumour initiating cancer cells (TICs) are necessary and sufficient to induce tumour sphere formation (371) 84 Small Cell Lung Cancer (SCLC) Cell lines: NCI-H446 NCI-H446: both (1) Lin28A â with c-Myc â causes pri-let-7 âand mature let-7 â (2) Lin28A inhibition triggers cell cycle arrest and â cell growth (3) Lin28B LoF results in CDC25A â, an activator of G1 to S phase transformation, thus preventing cell division (372) Brain Glioblastoma Multiforme (GBM) Glioma tissues; Cell lines: U251, U373 All cell lines: Lin28A (1) Lin28A â leads to poor diagnosis and â survival rate (2) Lin28A â induces cell cycle arrest in G1 phase, â apoptosis and â colony count (373) Atypical teratoid Rhabdoid Tumour (AT/RT) AT/RT primary tumour tissues; Cell lines: BT12/37, CHLA-06/04/06 All cell lines: both (1) High levels of Lin28A and Lin28B mRNA coupled with low levels of let-7 in the majority of tumours (2) Lin28A KO â let-7 mRNA, downregulates KRAS mRNA, reduces cell growth and proliferation, â clonogenicity and induces apoptosis in cells (3) Lin28A â in xenograft results in a two-fold â in median survival rate (374) Prostate Androgen-independent Prostate Cancer (AIPC) Cell lines: VCaP, LNCaP, PC3, Du145 VCap: both LNCap: Lin28B PC3: Lin28B Du145: Lin28B (1) Lin28B positively correlates with c-Myc protein but not mRNA level (2) Lin28B negatively correlates with miR-212, a tumour suppressor (3) Computational predictions suggest that c-Myc protein activates Lin28B, which in turn â miR212 miRNA activity (375) Therapy-induced neuroendocrine prostate cancer (t-NEPC) Cell lines: LNCaP, C4-2, RWPE-1, 22RV1, PC-3, VCaP, Du145, NCI-H660/H82/H69, LASCPC LNCap: Lin28B C4-2: n/a RWPE-1: n/a 22RV1: n/a PC-3: n/a VCaP: both Du145: Lin28B NCL-H660/H82/H69: Lin28B LASCPC:Lin28B (1) Lin28B â and Sox2 â mRNAs and protein levels in tumours, xenografts, and cells (2) Let-7d â results in HMGA2 â and Sox2 â (3) Lin28B induces cancer stem cell and neuroendocrine biomarkers (341) Prostate cancer (PC) Cell lines: PrEC, RPWE-1, LNCaP, Du145, and PC3 All cell lines: both (1) ESE3/ EHF binds Lin28A and B promoter deactivating its expression and thereby â let-7 miRNA levels (2) Lin28A and B inhibition resembles the effects of ESE3/EHF as it â CSC phenotype (376) Gastrointestinal Colon carcinoma Colon cancer tissues; Cell lines: SW480, HCT116 All cell lines: both (1) Lin28B â in colon cancer tissues â metastasis and correlates with poor patient survival rate (2) Lin28B inhibition leads to âmigration and â drug-induced cytotoxicity in cells (377) Colon Adenocarcinoma Colon carcinoma tissues; Cell lines: DLD-1, LoVo DLD-1: Lin28B LoVo: both (1) Lin28B â reduces patient survival, â tumour resistance and chances of relapse (2) Lin28B â LGR4 and PROM1 colonic cancer cell markers in vivo (377) 85 Gastric Adenocarcinoma (GAC) Gastric cancer tissues Lin28B (1) Lin28B â in GAC tissues (2) Lin28B expression level is positively correlated with clinicopathological parameters of GAC patients (i.e., lymph node status, TNM, tumour invasion, and GAC cell differentiation) and negatively correlated with survival rates (378) Colorectal cancer (CRC) Colon adenocarcinoma tissues Both (1) Lin28A/B levels are not related to liver metastasis development (2) Lin28A/B expression is negatively correlated with Sox2 expression. (379) Wnt- activated Esophageal squamous cell carcinoma (ESCC) ESCC tissues; Cell lines: TE-1, ECA109, KYSE-150 TE-1: both ECA109: Lin28A KYSE-150: both (1) Let-7a âcorrelates with metastasis and cancer recurrence (2) Let-7a-mimic and Lin28A/B silencing reduced EMT Let-7a âreduces protein levels of EMT divers, Snail and SLUG (3) Wnt/β-catenin â increases the expression of its Lin28A/B (380) Oral Squamous Cell Carcinoma (OSCC) OSCC tissues; Cell lines: SCC9, SCC15, and SCC25 SCC9: both SCC15: both SCC25: Lin28B (1) Lin28B expression correlates with earlier disease recurrence and promotes cancer cell migration (2) Lin28B but not Lin28A is associated with poor patient prognosis (3) Lin28B â migration, invasion, proliferation and clonogenicity (4) Lin28B â IL-6, HMGA2, Snail, Twist, VEGF, and Survivin expression (381) Kidney Renal Cell Carcinoma (RCC) Nephrectomy tissues Lin28A (1) Lin28A expression is normal (382) Wilms Tumour Kidney tissues of mice embryo Embryonic stem cells: both (1) Lin28A and B â blocks differentiation of embryonic kidney cells transforming them into nephrogenic progenitors that initiate Wilms tumour (2) Lin28B/let-7 pathway promotes glomerulus-like structure tumour formation (383) Circulation Mixed-lineage leukemia (MLL) Bone marrow tissue; MLL-AF6/9/10/ENL/Gas7 Lin28B (1) Lin28B â due to c-Myc â (2) Lin28B inhibits let-7a/g and â tumour growth (3) Let-7 levels restoration results in differentiation of leukemia blasts (384) Acute myeloid Leukemia (AML) AML xenograft mice model; AML cell lines Lin28A and B (1) A small molecule inhibitor (1638) of Lin28 â cell growth and clonogenicity (2) In xenograft models, 1638 leads to â let-7 levels, â LCS counts and tumour growth (385) Juvenile Myelomonocytic Leukemia (JMML) subtype JMML tissues Lin28B (1) Lin28B is a characteristic of a novel molecular subgroup of JMML (2) Lin28B correlates with HbF, a prognostic JMML marker (3) Lin28B is a marker of poor survival (386) 86 Liver Hepatocellular Carcinoma (HCC) HCC tissues; Cell lines: Hek293, Cos-7 HepG2/3B, Li-7, HLE, Huh6/7, MCF-7 All cell lines: Lin28B; Huh6/7: both (1) First report of Lin28B (2) Lin28B â in HCC cell lines and tissue samples (3) Lin28B localizes in the cytoplasm (4) Lin28B lacking CSD domain is expressed in normal liver cells (387) Cell lines: HeLa, HepG2, Huh7, Hep3B HeLa: Lin28B HepG2: both Huh7: both Hep3B: both (1) Lin28B â let-7 biogenesis by inhibiting its maturation through 3â uridylation that blocks Dicer processing (367) Hepatoblastoma (HB) Mouse models Lin28B (1) Lin28B alone is sufficient to induce liver tumourigenesis in mice model (2) Lin28B â in Myc-driven mouse models (3) Lin28B directly â an lgf2 mRNA binding protein that âtumour growth (370) Ovarian Ovarian Carcinoma A panel of 527 human both (1) Lin28A and Lin28B â in ovarian carcinoma histological grade 2 or 3 (352) Epithelial Ovarian Cancer Epithelial ovarian cancer patient tissues both (1) Lin28B âin patients leads to short progression-free and low survival rates (2) Lin28B block let-7a maturation and positively correlates with pri/ pre-let-7a (3) A positive correlation between Lin28B and insulin-like growth factor II (IGF-II) may contribute to the adverse effect in ovarian cancer (388) Ovarian Primitive Germ Cell Tumours (GCTs) Ovarian cancer tumour tissues both (1) Lin28A â significantly contributes to primitive ovarian GCTs (primary and metastatic dysgerminomas, gonadoblastoma, Yolk Sac tumour, and Embryonal carcinoma), immature teratomas, and neuroepithelial tissues (2) Lin28A can be used as a diagnostic marker for GCTs (389) Testicle Testicular Germ Cell Tumours (GCTs) Testicular cancer tumour tissues both (1) Lin28A and Lin28B are strongly detected and have specificity in metastatic testicular GCTs, including classic seminoma, embryonal carcinoma, and yolk sac tumour (2) Lin28A, Lin28B, and SALL4 act as sensitive markers for extragonadal yolk sac tumours (351) Breast Breast Cancers Breast cancer tissues; HB22, T47D, MCF7, Bcap-37, SK-BR-3, MDA-231 HB22: n/a T47D: both MCF7: Lin28A Bcap-37: SK-BR-3: Lin28A MDA-231: n/a (1) Lin28A/B âconfers resistance to paclitaxel (2) Lin28A/B KO causes â sensitivity to paclitaxel in T47D cell line (3) Lin28A/B â in breast cancer tissues of relapsed cancer. (4) Lin28A/B induces p21 and Rb expression (390) 87 Triple Negative Breast Cancer subtype Primary breast cancer tissue; 4TO7, MDA-MB-231, 293 T All cell lines: Lin28B (1) Lin28B â pre-metastatic genes in lung tissues, facilitating neutrophil accumulation, N2 conversion and the development of the immune-suppressive pre-metastatic niche (2) let-7s exosomes â in pre-metastatic niches â IL-6 and IL-10 contributing to the incidence of lung metastasis and increased tumour size (3) Lin28B and let-7s are markers for poor survival and lung metastasis (391) Breast cancer Mouse models; MDA-MB-231, MCF10A, bone (1833) and lung (4175) metastatic breast cancer cells MDA-MB-231: Lin28B MCF10A: both 1833: both 4175: both (1) The dysfunction of Raf kinase inhibitor protein (RKIP), a metastasis suppressor, facilitates Myc binding to the Lin28A and B promoter (2) Lin28A and B suppression leads to â let-7, inhibits HMGA2 (an activator of metastatic genes) and â bone metastasis (392) Lymph Node Papillary thyroid carcinoma (PTC) PTC tissues both (1) Lin28A and B was expressed in 40.5% of PTC samples (2) Patients with Lin28A and B expression had larger tumour size, more frequent lymph node metastasis, and aggressive tumour characteristics (3) Lin28A and B serves as a prognostic marker in PTC (393) Spinal Cord MYCN-amplified Neuroblastoma CHP-212, SK-N-AS, SY5Y, BE2C, Kelly CHP-212: both SK-N-AS: Lin28B SY5Y: Lin28B BE2C: Lin28B Kelly: Lin28B (1) Lin28B â cell migration in vitro and metastatic abilities in vivo (2) Lin28B KO â tumour size in mice xenograft (3) Lin28B mediates liver metastasis MYCN amplification significantly induces Lin28B expression (394) Bladder Bladder Cancer (BLCa) BLCa tissues; Cell line: T24, UM-UC-3, J82, SV-HUC-1 All cell lines: both (1) LINC01451 IncRNA directly binds to the promoters of Lin28A and Lin28B in tumour tissues (2) LINC01451 â EMT by â biomarkers of TGF-β/Smad signalling pathways through Lin28A/B regulation (395) 88 4.1.4 Inhibition of Lin28 with Small Molecules Despite a widespread view that RNA-binding proteins are undruggable, there is a growing body of research on targeting Lin28A and Lin28B with small molecules (396, 397). These small molecules bind to the surface of either CSD or ZKD of Lin28 and prevent Lin28/let-7 interaction from increasing let-7 biogenesis in cancer cells, resulting in the suppression of cancer cancer stem cell (CSC) and epithelialâmesenchymal transition (EMT) phenotypes (Table 2). Notably, the studies outlined in this section employed novel high-throughput screening techniques, including fluorescence polarization (FP) and fluorescence resonance energy transfer (FRET), that gain popularity in screenings for RNA-binding protein inhibitors (398, 399). These novel technologies enabled screenings of libraries containing up to 100,000 compounds (Table 2). The basic principle of FP involves measuring the rotational motion of fluorescent molecules, where higher polarization indicates slower rotation, often due to binding events or increased molecular size, while FRET involves a distance-dependent energy transfer between a donor and acceptor fluorophore, indicating close proximity and useful for studying molecular interactions and conformations. (400). In this way, an event of inhibition between Lin28 and let-7 RNA could be tracked. Table 4-2. Lin28 Inhibitors. Lin28A and B expression in human cancer cell lines were confirmed from Cancer Cell Line Encyclopedia (CCLC) Dependency Map database Target Domain Lin28 inhibitor Identification method Activities Cold shock domain (CSD) LI71 FP-based HTS of 101,017 compounds from 17 libraries (1) Oligouridylation TUT IC50 = 27 μM (2) FP IC50 = 7 μM (3) Direct binding: NMR spectroscopy (4) Let-7 qPCR: 2â5 times (let-7b/c/d/f/g/i) in K562 (Lin28B+) at 100 μM 3â7 times (let-7a/b/c/d/e/f/g/i) in DKO+A (Lin28A+) mESCs at 100 μM (338) 89 KCB3602 FRET-based HTS of 8,400 compounds from Korea Chemical Bank (KCB) (1) EMSA IC50 = 4.8 μM (2) Direct binding: SPR kD = 5.9 ± 2.3 μM (3) Let-7 qRT-PCR: 1.5â2.5 times (let-7a/b/d/f/g/i/miR-98) in JAR cells (Lin28A+ , Lin28B+) at 10 μM (401) Compound 1 FRET-based HTS of 4,500 drug-like compounds (1) FRET IC50 = 4.03 μM (2) EMSA IC50 = 6.75â11.8 μM (Lin28A/B: let-7a-1/g) (3) Direct binding: SPR kD = 3.51 μM (4) Let-7 qPCR: ~1.5â2.5 times (let-7a/d/f/g/i/miR-98) in JAR cells (Lin28A+ , Lin28B+) at 40 μM ~1.5â2.3 times (let-7a/g) in PA-1 cells (Lin28A+ , Lin28B+) at 40 μM (402) C902 FP-based HTS of 15,000 natural product-inspired compounds (1) FP IC50 = 5.0 μM (2) Let-7 RT-qPCR: ~2 times (let-7a/g) in JAR cells (Lin28A+ , Lin28B+) at 20 μM (403) GG-43 Custom chemistry modifications of LI71 (1) EMSA IC50 = 21.9 μM (LI71 EMSA IC50 = 41.6 μM) (2) FP IC50 = 4 μM (LI71 FP IC50 = 7 μM) (404) Zin c finger domain (ZKD) TPEN Fluorescence polarization (1) FP IC50 = 2.5 μM (2) Toxic in Lin28+ mESCs and Lin28- HeLa cells (Lin28B+) (3) Direct binding: NMR spectroscopy (338) Nb-S2A4 Functional Epitope Nanobody Selection Platform (1) Oligouridylation TUT IC50 = 0.2 μM (2) Let-7 qPCR: 3â12 times (let-7b/c/d/e/f/g/i/miR-20b) in HeLa cells (Lin28B+) (405) 90 Unknown SB/ZW/0065 FP-based HTS of 2,768 compounds from Sigma LOPAC1280 library, NCI diversity set II and a targeted nucleic acid structure library (1) FP IC50 = 7.05± 0.13 μM (340) 6-hydroxy-DL-DOPA (1) FP IC50 = 4.71 ± 0.16 μM (340) CCG-234459 Cat-ELCCA-based HTS of 127,007 compounds from LOPAC, Prestwick, Maybridge, ChemDiv, and UMich libraries (1) Cat-ELCCA IC50 = 8.3 μM (406) CCG-233094 (1) Cat-ELCCA IC50= 10.3 μM 91 4.2 CADD-based discovery of Novel Lin28 Inhibitors 4.2.1 Identifying Druggable Lin28 Pockets To identify a potential druggable site on the surface of Lin28, we examined both CSD and ZKD (Figure 4-2). These domains recognize pre-Let-7 in a bi-partite mode, where CSD binds the GNGAY motif and ZKD binds the GGAG motif of Let-7 (407). First, we computationally searched for plausible drug-binding sites on the surface of both ZKD and CDS (PDB: 5UDZ) (366). A cavity where the GGAG motif binds to the ZKD domain was scored at the top of potential druggable sites. We visually examined the cavity and concluded that it is large enough to accommodate a wide range of small molecules and deep enough to provide the potential for strong ligand anchoring. Thus, we chose the ZKD GGAG-binding site as the target, expecting that a small molecule would displace the GGAG motif and block pre-Let-7 binding (Figure 4-2). Second, we examined the literature and found that the GGAG motif is critical for pre-Let-7 processing as shown in deletion and substitution experiments (408). Although both the CSD and ZKD are essential for pre-Let-7 degradation (408, 409), the ZKD is the domain that directly recruits terminal uridylyltransferases (TUTases), TUT4 and TUT7, to oligouridylate the Let-7 RNA for degradation (410). Additionally, ZKD recognition of the GGAG motif is required for TUTase recruitment, which is followed by the Dicer complex-mediated Let-7 degradation (411). These studies indicated that disruption of Lin28 binding to GGAG would effectively block Lin28 activity in suppressing Let-7 biosynthesis. To summarize, both our computational observations and previous studies support that the docking pocket at the interface between Lin28 ZKD and the Let-7 GGAG motif is an ideal drug-binding site. 92 4.2.2 Virtual Screening for Lin28B ZKD Inhibitors A CADD campaign was initiated against the identified docking pocket on the surface of Lin28 ZKD. The crystal structure of Lin28 was downloaded from Protein Data Bank (PDB: 5UDZ) (366). Around the selected binding pocket there was no amino acid sequence difference between Lin28a and Lin28b forms, therefore, the published structure of Lin28 was considered a model suitable for both forms. We performed an initial virtual screening of 1 billion compounds from the Enamine library using the in-house developed DeepDocking tool (47) coupled with Glide docking software (99). Briefly, DeepDocking leverages the speed of machine-learning docking score prediction to screen large libraries of compounds (Figure 1-4). The whole ZINC20 database (29) was sampled to perform docking and then the obtained docking scores were used to train models and predict the undocked compounds. In this way, we eliminated predicted poor binders and the top 18 million compounds scored with DeepDocking were re-docked with Glide to retain 1 million compounds (Figure 4-4A). These compounds were then re-docked with FRED software (OEDocking by OpenEye) (412) and the spatial difference between the docking poses produced by the two software was computed. We removed compounds with significantly different docking poses (RMSD >2Ã ) retaining ~275,000 compounds that were then passed through ADMET filtering to eliminate potentially toxic compounds. Additionally, we screened the remaining library for molecules that would mimic the Lin28-GGAG interactions. We speculated that an active compound should form similar contacts with Lin28 residues as the native ligand, i.e. GGAG motif. To identify which residues contribute the most to the Lin28-GGAG binding, we run a molecular dynamics simulation of the protein-RNA complex for 30 ns. We observed that residues Lys159, Lys160, His162 and Val171 form the most stable bonds with the ribonucleotides (Figure 4-4B). Therefore, these residues were selected as pharmacophore features, i.e. requirements to act as 93 hydrogen donors or acceptors. The developed pharmacophore features were used to filter the remaining 275,000 compounds yielding around 150,000. Additional scoring with MOE SVL scripts (e.g. number of hydrogen bonds, number of rings, logP) was performed to guide the expert selection of 163 compounds to be purchased for experimental validation. Figure 4-4. CADD pipeline of ZKD-Lin28B protein inhibitors. A) The CADD pipeline employed to find 163 potential ZKD-Lin28B protein inhibitors. B) Interaction of ZKD-Lin28B protein and two let-7 nucleotides (first guanine and the adenosine of the GGAG motif). The residues that form hydrogen bonds (in red dotted lines) used for pharmacophore features are in red boxes. 94 4.3 Experimental Evaluation of Lead Lin28 Inhibitors 4.3.1 Activity in Protein-Based Assays We then tested all 163 molecules predicted by the CADD pipeline in in-house developed FP assays using 10µM of Lin28B ZKD protein and 1nM of the pre-Let-7d-FAM probe. Compounds Li71 and 1632 were also included in our assays and served as positive Lin28 inhibitors. We used a cutoff of 40% remaining FP signal in the presence of 20 µM of the tested compounds to identify positive hits. Using this criterion, we found 11 active compounds out of 163 tested, resulting in a hit rate of 9% (Figure 4-5 left). We then repeated the FP assays using Lin28 CSD+ZKD and found that no compounds achieved greater than 60% inhibition (Figure 4-5 right). This could be explained by the fact that pre-let-7 first binds to the CSD and only after to the ZKD. While inhibitors prevent the function of Lin28 by targeting the ZKD, they do not fully block the interaction between Lin28 and let-7 because the CSD binds pre-let-7 first and with higher affinity. As a result, the ZKD inhibitors are less effective when both domains are present, even though the ZKD is essential for preventing let-7 maturation (366). Therefore, even though we still observe binding to the full complex, the interaction with the ZKD results in functional inhibition. Interestingly, the CSD inhibitor, Li71, showed ~30% inhibition of FP signals, while compounds Ln7, L15, and Ln115 resulted in about ~50, 50 and 20% inhibition in FP signals. These data suggested that the identified 15 ZKD inhibitors can be sufficient to block the interaction between ZKD and Let-7. Their inhibitory effects were relatively specific to ZKD, but not CSD, consistent with our rational drug using CADD. 95 Figure 4-5. FP screen of 163 compounds. On the left: Lin28B ZKD. On the right: full length Lin28B. Xâaxis represents 20µM of the treatment of each chemicals (Ln1-Ln193) with 10µM of protein and 1nM of FAM labelled probe, while Yâaxis represents the percentage of binding left after the treatment of compounds. Positive control (P-Ctrl) contained ZKD-Lin28B protein with pre-let-7d-FAM. Negative control (N-Ctrl) contained ZKD-Lin28B protein with pre-let-7d-FAM and pre-let-7d probe. Compounds with an average remaining fluorescence below 40% were considered as potential candidates for ZKD-Lin28B protein inhibitors (9 replicates). To further prove that our identified compounds disrupt Lin28b ZKD association with Let-7, we performed electrophoresis mobility shift assays (EMSA). We observed robust inhibition of ZKD-Let-7 complex formation with Li71 but not 1632 at concentrations from 0 to 1200uM (Figure 4-6A). While Ln7 only inhibited ZKD-Let-7 interaction at a concentration as high as 1000 µM, Ln15 effectively disrupted the ZKD-Let-7 complex at 400uM, and Ln115 showed the strongest efficacy that blocked ZKD-Let-7 interaction at 100 µM concentration. We were not able to show by EMSA that other identified inhibitor compounds blocked ZKD-Let-7 interaction likely due to lower sensitivity EMSA than FP assays, dose-dependent inhibitions of FP signals by other identified Lin28 inhibitors were observed. Our following works, therefore, focused on characterizing Ln7, Ln15, and Ln115. We have titrated all three compounds in FP assays to compare their potency in disrupting ZKD and Let-7 interactions. Compounds Ln7, Ln15, and Ln115 exhibited IC50 values of approximately 45 µM, 9 µM, and 21 µM, respectively (Figure 4-6B). In contrast, Li71 had an IC50 of 55 µM, and compound 1632 achieved only a maximum of 96 50% inhibition of the FP signal even at a concentration of 200 µM. In summary, using two independent techniques we validated three lead compounds as Lin28b inhibitors. Figure 4-6. Direct binding and dose-dependent assays of the selected compounds. A) EMSA. The set up contains 0.5µM of ZKD-Lin28B protein with 10nM IR800DyeCWN-pre-let-7d probe and non-labeled probe. Positive control includes only IR800-Pre-let-7d probe, while negative control contains both the labelled probe and Lin28 ZKD. âCompâ band corresponds to competition set up where both labelled and unlabeled probe present together with the protein. Numbers represent chemical concentrations in µM. B) Dose-dependent effect of the selected hit compounds in FP titration (1nM-100µM) with 10µM ZKD-Lin28B and 1nM of pre-let-7d-FAM. C) Bio-layer interferometry (BLI) assay performed on Ln115 and ZKD-Lin28B. D) Chemical structures of the selected compounds. To further elucidate the direct binding of our lead Lin28 inhibitors, Ln7, Ln15, and Ln115, Bio-Layer Interferometry (BLI) assays were employed. These assays demonstrated that these compounds bind to the Lin28 zinc knuckle domain (ZKD) in a reversible and dose-dependent manner, aligning with our computational simulations (Figure 4-7C). This non-covalent binding indicates their potential to disrupt the Lin28-Let-7 RNA interaction. 97 Additionally, in cellular assays, these lead compounds, alongside established Lin28 inhibitors, were found to significantly elevate Let-7d microRNA levels in neuroendocrine prostate cancer cells. This increase in Let-7d was associated with a reduction in HMGA2 and SOX2 mRNA levels in DuNE cell line (Figure 4-7), suggesting inhibition of Lin28's activity in regulating critical oncogenes and influencing cancer cell stemness. Figure 4-7. Selected inhibitors restore let-7d levels and downregulate SOX2 and HMGA2 genes. Top: RNA expression levels of let-7d, SOX2 and HMGA2 in DuNE cell line. Bottom: RNA expression levels of let-7d, SOX2 and HMGA2 in DuNE Lin28BKO. All measurement were taken after 48 hours of 20 µM chemical treatments. Xâaxis represents treatment of different compounds, while Yâaxis represents relative gene expression in fold-change (n=9 replicates). Three independent technical replicates were performed for each real-time PCR experiment. All results are presented as mean +/- SD. To further prove that the suppressive effects of the Ln28 inhibitors were through blocking the Lin28b protein, we have repeated our real-time RT-PCR studies using the DUNE(KO) cells where the Lin28b gene was knocked out using CRISPR Cas9 technology. We showed that our identified Lin28 inhibitors have either marginal or no impacts on Let-7d, HMGA2 and SOX2 expressions, highlighting the on-target effects of these inhibitors in cancer cells 98 4.3.2 Cancer Cell Stemness Reduction The key roles of Lin28 in regulating cancer cell stemness and promoting therapy-resistant tumour progression have been demonstrated in many types of tumours (346, 413-415). We have shown that Lin28b drives cancer stemness phenotypes and promotes cancer cell lineage switch to neuroendocrine lineage. We showed that Lin28b gene disruption inhibited cancer cell stemness and inhibited NEPC tumour progression. Therefore, we set out to test whether our identified compounds inhibit cancer cell stemness using our DUNE cell model. Four techniques were used to assess the capability of our Lin28 inhibitors to CSC phenotype. First, we measured the expression of a panel of CSC markers including IGF2BP1, CDH2, FOXD3, HEY1, ALDH1A2, CDK6, FOXC1, SIX2, and ID4 in DUNE cells by real-time RT-PCR. We confirmed reductions of these CSC markers by Ln7, Ln15, and Ln115 (Figure 4-9A). Notably, Ln115 remarkably suppressed the expression of CDH2 and FOXD3 by up to 90%. In contrast, Li71 and 1632 showed variable mild inhibitory effects or no inhibition to several CSC markers. Furthermore, we also demonstrated that Ln7, Ln15, and Ln115 blocked the expression of several neuroendocrine markers including ASCL1, CHGB, SCGN, SYP, and STT4 (Figure 4-9B). Notably, Ln15 and Ln115 suppressed not only the mRNA expression but also protein expression of SYP (Fig. S2). Second, we performed FACS to measure CSC biomarkers of CD133 and CD44 using DUNE cells treated with Lin28 inhibitors. We observed significant reductions of CD44 and CD133 positive cell populations in the presence of Ln7, Ln15, and Ln115. These changes were consistent with those from DUNE cells with Lin28b gene knockout (Figure 4-9C). Third, we measured cell proliferation of DUNE (Lin28b positive) and IGROV1 (Lin28a positive) cells. All three lead 99 compounds resulted in up to 80% inhibition of cell growth. In contrast, no impacts were observed in DUNE cells with Lin28b knockout, further validating that the inhibitory effects of our lead compounds act through targeting Lin28 proteins (Figure 4-9D). Fourth, we performed colony formation assays to evaluate the suppressive activities of Lin28 inhibitors to self-renewal and tumourigenesis capability of DUNE cells. We found that not only Ln7, ln15, and Ln115 but also Li71 and C1632 strongly suppressed the formation of cancer cell colonies (Figure 4-8E). Ln15 showed ~90% suppressive effects. However, no inhibitory effects were observed using Lin28b knockout DUNE cells. Together, these studies confirmed that Ln7, Ln15, and Ln115 block cancer cell stemness by targeting Lin28 proteins. 100 mRNA expression levels of A) Cancer stem cell markers and B) neuroendocrine markers in DuNE cell line. Xâaxis represents treatment of different compounds, while Yâaxis represents relative gene expression in fold-change (n=9 replicates). C) FACS analysis of CD44+ and CD133+ stem cell biomarkers in DuNE cells treated with 20 µM of potential ZKD-Lin28B inhibitors Ln7, Ln15, and Ln115 for 48 hours. D) Cell proliferation measured at days 1, 3 and 5 after treatment with ZKD-Lin28B inhibitors, Ln7, Ln15, and Ln115 at 20 µM in DuNE, DuNE Lin28KO and IGROV1 cell lines. E) Quantification of colony formation assays (colonies > 100 µm were counted). Ln7, Ln15, Ln115 at 20 µM concentration treatment after one day of cell seeding. Figure 4-8. Selected ZKD-Lin28B inhibitors regulate stem-like pluripotency gene network and neuroendocrine markers in t-NEPC cells. 101 4.4 Optimization of Ln7, Ln15 and Ln115 lead compounds To develop a new round of compounds, we utilized the structures of four lead compounds â Ln7, Ln15, Ln250 and Ln115. We employed two different strategies to develop new analogs with improved potency â nucleobase-inspired design and structure-activity relationship-guided design. In our pursuit to disrupt the protein's interaction with key RNA motifs, we designed several compounds tailored to block interactions between Lin28 and the GGAG motif of pre-let-7. There is a lot of evidence that the GGAG-Lin28 interaction is crucial for protein function, underscoring the importance of the GGAG motif and its attractiveness as a drug target. First, all let-7 members that are suppressed with Lin28 share the GGAG motif (368), emphasizing the pocket's specificity and its crucial role in the binding of most let-7 types. Second, the functional importance of the ZKD-GGAG binding was demonstrated by a deletion of the GGAG motif that impaired the suppression of let-7 maturation (416). Third, the specificity of the ZKD binding was underlined when substitutions of the first and second Gs in the GGAG motif to A (resulting in AGAG/GAAG) led to a significant 4- and 13-fold reduction in let-7 binding affinity, suggesting these nucleotides are crucial for forming key contacts with the protein (365). Fourth, TUTase recruitment, which is essential for the blockage of Dicer processing, happens via recognition of GGAG (417). Finally, the selected binding site also contains residue Tyr140 which was shown to be essential for GGAG recognition (418). Given the critical evidence, we believe that disrupting the GGAG motif emerges as a promising strategy for Lin28 drug targeting. We hypothesize that our designed compounds hinder the vital interaction between pre-let-7 and Tyr140, disrupting the recognition mechanism. 102 4.4.1 Nucleobase-Inspired Design To effectively disrupt the RNA-protein binding, we turned to a strategic approach grounded in nucleobase-inspired design that relies on the modifications of the lead compounds to resemble nucleoside or nucleobase of the RNA molecule natively bound to Lin28. Nucleoside or nucleobase analogs, widely used as antiviral and anticancer drugs exhibit antiproliferative and cytotoxic properties due to their ability to compete with natural nucleosides (419). Thus, the design of nucleoside and nucleobase analogs relies on the central dogma of drug discovery stating that compounds with similar structures share molecular targets. Taking this into consideration, we utilized the structure of pre-let-7 bound to Lin28 to develop novel analogs of Ln115 and Ln250. To do so, we overlaid the structures of the compounds docked into the binding site and pre-let-7 from the crystal structure (PDB: 5UDZ) (366). The guanine and adenosine bases of the GGAG motif lie within the binding site of the lead compounds. We analyzed the overlaid structures and added structural modifications to achieve higher similarity between the nucleosides and the selected compounds. For instance, the hydroxypyridine ring of Ln115 was modified into various analogues of hydroxyopurines to resemble the guanine residue binding at that region (Figure 4-3). Similarly, compound Ln250 was modified by adding an imidazole ring to resemble the purine base of guanine. Additionally, we added a sugar moiety to the modified Ln250 structure to replicate the guanine nucleoside-Lin28 interactions. Using this we designed and purchased six analogs of Ln115 outlined in Table 4-3. 103 Table 4-3. Table of analogs of Ln115 designed with nucleobase-inspired design. Structure ID Glide Score Glide LE Fred Score ICM Score MMGBSA Hbond MMGBSA Solv GB MMGBSA dG 250278 -8.2 -0.4 -9.3 -43.9 -0.4 -0.2 -56.4 250279 -8.2 -0.3 -8.8 -42.5 -0.4 -0.1 -57.3 250285 -8.6 -0.4 -9.1 -45.9 -0.4 -0.1 -56.5 250287 -8.4 -0.4 -9.5 -38.5 -0.4 -0.2 -50.9 250288 -8.3 -0.3 -8.4 -35.6 -0.4 0.0 -59.1 250304 -8.5 -0.4 -4.3 -34.4 -0.4 -0.7 -54.3 4.4.2 Structure-Activity Relationship (SAR) Optimization Besides our nucleoside-based design, we further harnessed a structure-activity relationship (SAR) strategy, elucidated from previously tested close analogs of Ln7, Ln15, and Ln115. We first analyzed closed analogs of Ln15 and observed crucial SAR (Table 4-4). For instance, we observed that compound Ln245, a close analog of Ln15 with methyl and isopropyl groups added on the phenol ring, showed slightly higher potency than Ln15. Similarly, compound Ln250 closely resembling Ln15 with tetrabutyl attached to the phenol ring also showed some superior activity to 104 Ln15. Thus, we concluded that large hydrophobic moieties placed on the phenol ring could enhance the activity of newly designed compounds. These could be explained by the enhanced ability of the compound to repel the RNA molecule with the protruding hydrophobic moieties. Table 4-4. Analogs of Ln15 used to derive SAR SMILES ID Glide Score Glide LE Activity FP (Inhibition %) 250015 -8.16 -0.58 82 250202 -6.82 -0.52 2 250245 -6.78 -0.38 48 250248 -6.15 -0.41 9 250250 -9.11 -0.48 35 250256 -5.93 -0.37 11 250257 -7.25 -0.48 4 250258 -7.79 -0.56 7 105 Thus, we modified Ln7, Ln15, and Ln115 based on these structure-activity relationships. Several analogs of Ln7, Ln15, and Ln250 were designed by adding hydrophobic methyl, ethyl, isopropyl, and tetrabutyl groups (Figure 4-9). Additionally, we designed several chimera compounds by combining features of Ln15 and Ln250. For instance, we replaced the thiazole ring of compound Ln15 with a bulkier thiazine ring that closely resembles the binding mode of Ln250 (Figure 4-9). Figure 4-9. Structure-activity relationship guided design of Ln15 and Ln250. Compounds Ln15 and Ln7 were previously modified, and the derivatives were testes. Active derivatives Ln245 and Ln250 indicate that hydrophobic moieties might be beneficial for pre-let-7 binding inhibition. Importantly, custom-designed compounds derived from both strategies - nucleotide-based and SAR-based - were subjected to docking and scoring to ensure effective binding to the Lin28 site. To validate the accuracy of our scoring metrics, we compared the scores from previously measured compounds to their experimentally determined activity levels. The two most distinguishing metrics that accurately differentiated active compounds from inactive ones were the Molecular Mechanics Generalized Born Surface Area (MM-GBSA) ligand efficiency (with a correlation of R = -0.44) 106 and the Glide docking ligand efficiency (with a correlation of R = -0.53). The MM-GBSA method is used to estimate binding free energies of ligands and had been largely deemed as a more accurate method than docking (420). All the newly designed analogs were assessed with these scoring methods and only compounds that showed better scores than their parent compounds were selected for purchase. Additionally, all compounds were docked with Glide (99), ICM (91) and FRED (92) docking software to ensure that the predicted binding poses of the designed compounds are reliable (i.e. check if docking software produces consensus poses). Finally, the compounds were screened with an ADMET predictor platform to ensure that the designed compounds are metabolically safe. All the selected compounds had an acceptable ADMET profile, and the docking poses did not differ significantly between the docking programs (i.e., < 3Ã ). We finalized our purchasing list with 36 designed compounds and subsequently received 32 of them that were then experimentally tested. 107 4.5 Experimental Evaluation of Optimized Derivatives of Lin28 Inhibitors 4.5.1 Activity in Protein-Based Assays We conducted two biochemical assays, FP and EMSA, to validate the predicted compounds. To optimize the FP assays, we performed a series of titrations using: 1) FAM-labeled let-7d RNA probe, 2) Lin28b ZKD protein, and 3) non-labeled let-7 probe as a competitor. These assays yielded the optimal concentrations required for the subsequent FP validation assays, namely 10nM for the let-7d probe and 1ï­M for Lin28b ZKD protein. The FP signal from the free probe was set as 0%, while the signal from the probe-ZKD complex in the absence of compounds was set as 100%. C1632 was used as a control Lin28 inhibitor. Out of the 32 compounds tested, 13 showed at least 40% inhibition of the Lin28b ZKD-let-7 interaction (Figure 4-10). Furthermore, all 13 compounds exhibited dose-dependent inhibition of Lin28b ZKD-let-7 interactions. We observed that Ln267 and Ln302 had IC50s of 4.67ï­M and 3.63ï­M, respectively, which were lower than the parent compounds Ln15 and Ln115 with respective IC50s of 9 and 21ï­M. Since the docking pocket is almost identical between Lin28a and Lin28b homologs, we repeated the FP assays using Lin28a ZKD. We found that at least 8 compounds (Ln267, Ln272, Ln279, Ln287, Ln298, Ln300, Ln302, and Ln306) were more potent than Ln15 and Ln115 in blocking the Lin28a ZKD-let-7 108 interaction. These results indicate that many of these newly designed compounds have improved potency in disrupting ZKD-let-7 interactions. Figure 4-10. Derivatives screening by fluorescent polarization. FP assays were used to screen 32 compounds at the concentration of 20 uM using 10 nM of FAM-labeled Let-7 probe with 1 uM Lin28B ZKD protein. We also validated the newly developed compounds using EMSA, a non-florescent based assay to detect ZKD-let-7 complex formation. Similar to the FP assays, we first optimized the experimental conditions by titrating: 1) IRDye800-labeled let-7d RNA probe, 2) Lin28b ZKD protein, and 3) non-labeled let-7 probe as a competitor (Figure 4-11). For the validation experiments, 1ï­M Lin28 ZKD and 10nM of IRDye800-labeled Let-7d probe were mixed with each candidate compound. Compounds C1632 and Ln15 were used as control Lin28 inhibitors, respectively. While the 13 compounds validated by FP assays showed inhibition of the ZKD-let-7 complex, EMSA confirmed that 10 compounds (Ln268, Ln269, Ln274, Ln276, Ln279, Ln287, Ln298, Ln300, Ln302, and Ln306) were capable of inhibiting Lin28-let-7d complex formation in a dose-dependent manner (Figure 4-11). The discrepancy between FP and EMSA results can be attributed to the different sensitivities of each assay (421). It is worth noting that we did not observe any obvious inhibition of C1632 on either the Lin28a or Lin28b protein using the same EMSA conditions as those used for testing our candidate compounds. This indicates that our Lin28 inhibitors have different modes 109 of action (MOA) from C1632. While these inhibitors directly target the Lin28 ZKD, C1632 may bind to domains other than the ZKD or allosterically disrupt Lin28âs capacity to bind its RNA substrates. Together, these results show that the 13 candidate compounds were validated by either FP or EMSA to be Lin28 inhibitors. Figure 4-11. Derivatives screening by Electrophoretic mobility sift assay. A) In EMSA assays, probe titration was performed by using 1 uM protein with 1 nMâ100 uM FAM-labeled Let-7 probe (n = 3 replicates). Protein titration was performed by using 10 nM of FAM-labeled Let-7 probe with 1 nMâ50 uM Lin28B ZKD protein (n = 3 replicates), and competition titration using 1 nMâ50 µM non-labeled Let-7 probe with 1 uM of Lin28B ZKD protein with 10 nM FAM-labeled Let-7 probe (n=3 replicates). B) EMSA assays were used to screen 32 compounds at the concentration of 500 uM using 10 nM of FAM-labeled Let-7 probe with 1 uM Lin28B ZKD protein. All experiments were repeated three times, and one representative image was shown. 110 Table 4-5. IC50 values of Lin28 inhibitors (uM) SMILES ID FP Lin28B-ZKD FP Lin28A-ZKD EMSA Lin28B-ZKD C1632 nd nd nd Ln15 9 13.65 194.45 Ln115 21 20.18 260.89 Ln250 23 89.03 242.3 Ln267 4.67 6.239 >500 Ln268 31.58 39.66 234.4 Ln269 12.33 27.7 21.33 Ln272 46.38 14.13 >500 Ln273 112.2 63.16 >500 Ln274 42.11 44.32 70.7 Ln276 25.81 24.19 20.88 Ln279 12.06 9.518 68.34 Ln287 16.53 9.446 36.3 111 Ln298 6.56 1.967 23.22 Ln300 17.98 12.5 35.71 Ln302 3.62 2.248 24.82 Ln306 11.03 9.303 29.38 FP = Fluorescent polarization EMSA = Electrophoretic mobility sift assay nd = Not Determined 4.5.2 Inhibitory Effects on Cancer Cells We further tested whether the 13 candidate compounds could penetrate the cell membrane and endure cell-mediated metabolism and excretion, and inhibit Lin28 activities inside cancer cells. Lin28b positive DuNE (422) cells were used in this experiment. Since let-7 miRNA is expressed at low levels in DuNE cells due to high Lin28b expression, all compounds blocked Lin28b mediated suppression of let-7 levels by 11-210% (Figure 4-12A). With the exception of Ln267 and Ln272, all compounds showed stronger capacity to increase let-7 expression than C1632. Since let-7 and Lin28b mutually regulate each otherâs expression, increased let-7 expression by Lin28 inhibitors would suppress Lin28b protein levels. We found that 8 out of the 13 compounds at a concentration of 20uM suppressed Lin28b protein expression (Figure 4-12B). Although multiple compounds can block the molecular functions of Lin28b, we found that Ln268 was the only compound that strongly inhibited cell proliferation in both Lin28b-positive DuNE cells and Lin28a-positive IGORV-1 cells (Figure 4-12C). Furthermore, these suppressive effects were more 112 potent than the parent compound Ln15. As a consequence of these results, Ln268 was identified as the lead Lin28 inhibitor and was further characterized. Figure 4-12. Derivatives screening in let-7 miRNA and Lin28b expression assays and cell proliferation assay. A) DUNE cells were treated with 20uM of the indicated compounds. MicroRNA levels were measured after 24 hours. B) LIN28B levels in DUNE cell line was measured by real-time qPCR and immunoblotting. C) Cell proliferation assays were used to screen 13 compounds at the concentration of 20uM using DuNE and IGROV-1 cells 4.5.3 Ln268's Efficacy in a Prostate Cancer Models To evaluate the potential of the new drug Ln268 for further animal studies, we developed a 3D NEPC model using DuNE cells, achieving well-defined spheroid morphology (Figure 4-13A). These spheroids were then treated with Lin28 inhibitors and C1632 as a positive control among which Ln268 exhibited significant activity in both dose- and time-dependent manners (Figure 4-13B,C,D). Remarkably, Ln268 achieved complete spheroid growth inhibition (100%) after 6 113 days at a concentration of 100 μM, outperforming C1632, which only reached 50% inhibition. This demonstrates Ln268's superior efficacy compared to the existing C1632 and its predecessor, Ln15. Figure 4-13. Ln268 inhibits the growth of a model of neuroendocrine prostate cancer spheroids growing in 3D culture. A) Schematic diagram of DuNE spheroids formation. B) DuNE cells were seeded at 1000 cells in a round bottom ultra-low adhesion plate. Cell area for 9 spheroids was tracked over 6 days and then converted to spheroid volume. C) Representative bright-field images at day 6 of spheroids treated with C1632, Ln15, and Ln268 are shown. D) Time- and concentration-dependent effect of C1632. Ln15, and Ln268 on DuNE spheroid growth. 114 To further evaluate its readiness to move into an in vivo experiment, we performed microsomal stability assays of the 13 derivatives (Table 4-6). Ln268 shows a half time of 462 min and a clearance rate of 5 ul/min/mg, compared to Ln15 at 347 min and 46 ul/min/mg, respectively. Collectively, our data prove that Ln268 has improved metabolism stability in comparison to Ln15, thus it became a promising lead compound to validate its biological efficacy in suppressing Lin28 activity. Table 4-6. Microsomal stability profile of Lin28 inhibitors Lin28 inhibitors T1/2 (min) Clint (µl/min/mg) Ln7 51 45 Ln15 347 7 Ln115 495 5 Ln267 4 232 Ln268 462 5 Ln269 nd nd Ln272 48 48 Ln273 99 23 Ln274 204 11 Ln276 147 16 Ln279 107 22 Ln287 14 164 Ln298 nd nd Ln300 3465 1 Ln302 nd nd Ln306 nd nd 115 4.6 Discussions In this chapter, I present a series of small molecule inhibitors that target Lin28 ZKD to disrupt its RNA binding activity resulting in the prevention of Let-7 microRNA degradation. These molecules including initially discovered hits Ln7, Ln15 and Ln115 block the CSC gene network and strongly suppress the clonogenic and self-renewal abilities of both Lin28a and Lin28b positive cancer cells. Additionally, DuNE and IGROV-1 cells treated with the inhibitors show significantly reduced expression of SOX2 and HMGA2, oncogenes that regulate pluripotency and promote treatment-resistant tumour progression (423-425). These results highlight the feasibility of Lin28 target therapy to abolish the CSC phenotype to suppress cancer recurrence, metastasis, and treatment resistance (426). Since a rapidly growing body of research highlights the importance of Lin28 in various types of cancer (343), we propose that our lead inhibitors could be used as a starting point for the development of Lin28 target therapies. There was a notion that RNA-binding proteins are notoriously difficult to target with small molecules due to the lack of defined binding sites and generally disordered structures (400). We dispute this conception and show that targeting an RNA-binding protein is feasible and highly translatable to modulate cellular phenotypes. Importantly, the conceived limitations were tackled with the use of computer-aided drug discovery technologies. Our computational analysis of the Lin28 surface allows the identification of druggable docking sites for small molecule binding. Additionally, the application of the novel machine learning-based tool DeepDocking enabled us to screen an ultra-large library of compounds. The coverage of such a broad chemical space ensured that the library of experimentally tested compounds was as focused as possible and provided a high chance of finding an effective inhibitor. Indeed, the hit rate of the initial FP assay was relatively high, 9% (15 actives out of 163 tested), compared to high-throughput (HT) 116 screenings of non-focused libraries such as HT FP screens on Lin28 protein yielded 0.01% (14/16,000) (339), 0.05% (53/101,017) (338) and 2.3% (64/2,768) (340) hit rates. Most importantly, our inhibitors show superior activity compared to hits from the abovementioned screens (1632 and Li71, respectively). Thus, this study further supports our previous conclusion that structure-based drug discovery enables the targeting of proteins conventionally considered to be undruggable (10). This work further describes the development of novel derivatives based on the lead chemicals Ln7, Ln15, and Ln115; to increase its metabolic properties and inhibitory efficiency against Lin28-Let7 axis activity and cancer cell growth. Thus, Ln268, an effective derivative, has a better pharmacokinetic profile and maintained target potency, allowing for maximum tumour suppression effects on 2D and spheroid 3D models. The observed differences in the activity between closely related compounds revealed interesting SAR. For instance, the pairs of Ln250 derivatives â inactive Ln295 vs. active Ln298 and inactive Ln296 vs. Ln300 â differ by one nitrogen atom added to Ln298 and Ln300, respectively, converting them into quinoline-like structures. The nitrogen atom in Ln298 and Ln300 introduces a different electronic distribution, making the ring system more electron-deficient and enhancing hydrogen bonding with the backbone of Tyr140 and the side chain of Asn141 (Figure 4-4B). In another instance, the inactive compound Ln275, a bioisoster of Ln115, harbored an additional solvent-facing methyl group on the pyridinone ring, potentially affecting the compound's water solubility or causing steric clashes with the charged surface of the Lin28 RNA-binding site, rendering the compound inactive. Similarly, inactive derivatives of Ln115, Ln277, and Ln282 also had modifications on the pyridinone ring, suggesting that this ring is sensitive to structural changes. However, the nucleobase-inspired design that modified the pyridinone ring into a purine-117 like base yielded two active compounds, Ln287 and Ln279. Together, SAR findings represent interesting directions for further optimization of lead compounds, such as modifying the quinoline-like structure to enhance electron deficiency or exploring nucleobase-inspired designs for improved RNA-binding affinity. Although these compounds showed promise in protein-based assays, not all passed the rigor of cell-based assays due to intricate interplays between protein affinity, cell permeability, metabolic instability, or off-target effects. Ln267 and Ln268 are close analogs but exhibit distinct activity profiles in protein-based and cell-based assays. Ln267, featuring an additional hydroxyl group and an isopropyl group, displayed higher potency in FP assays, likely due to the formation of a hydrogen bond between Ln267 and Asn141, enhancing ligand-protein affinity. Its bulky isopropyl group, facing the solvent, may efficiently repel RNA. However, Ln267 possesses a larger topological polar surface area (TPSA) of 113.59 Ã Â² and lower solubility (LogS of ~-4), predicted by SwissADME, compared to Ln15 and Ln268 (TPSA approximately ~94 Ã Â² and LogS approximately ~-3). These properties might hinder the cell permeability of Ln267, resulting in low effectiveness in cell-based assays. Conversely, Ln268, containing an electronegative fluorine atom that enhances cell permeability and membrane penetration, demonstrated strong activity in cell-based assays. However, the fluorine may decrease reactivity with the neighboring hydroxyl group, potentially weakening hydrogen bonding with Tyr140. This could explain why Ln268 performed less effectively than Ln15 in protein-based assays. These observations underscore the intricate interplay among structural modifications, reactivity, solubility, cell permeability, and activity profiles in both 118 protein-based and cell-based assays. Future optimization of Ln268 should consider all perspectives to develop more potent Lin28 inhibitors. Figure 4-14. Predicted docking pose of Ln267 and Ln268. To sum up, using molecular modelling and SAR-based optimizations we created a novel Ln15 derivative named Ln268, which has better pharmacokinetic characteristics and increased inhibitory activity to distrust Lin28-Let7 interaction and cancer cell proliferation. Ln268 has the potential to be improved and used as an anti-cancer treatment for a variety of malignancies, such as prostate cancer. 119 4.7 Conclusions In this research, I have successfully demonstrated the potential of small molecule inhibitors, particularly Ln7, Ln15, and Ln115, in targeting the Lin28 ZKD, thereby disrupting its RNA-binding activity and preventing Let-7 microRNA degradation. The significant suppression of the clonogenic and self-renewal abilities in Lin28-positive cancer cells, coupled with the reduced expression of key oncogenes SOX2 and HMGA2, underscores the viability of Lin28 as a therapeutic target. These findings not only reinforce the concept of targeting CSC to combat cancer recurrence and resistance but also exemplify the promising role of CADD in identifying and optimizing small molecules for complex molecular targets like RNA-binding proteins. The development and optimization of these inhibitors, particularly the novel derivative Ln268, highlight the effectiveness of structure-activity relationship (SAR)-guided optimization in enhancing the pharmacokinetic properties and target specificity. This study challenges the traditional view that RNA-binding proteins are difficult to target, showcasing the transformative impact of CADD in the realm of drug discovery. The high hit rate and superior activity of the identified inhibitors compared to traditional high-throughput screening methods illustrate the power of computational tools in uncovering new therapeutic avenues. Looking forward, this research paves the way for the broader application of CADD in targeting other RNA-binding proteins, potentially leading to breakthroughs in the treatment of various cancers and other diseases marked by aberrant protein-RNA interactions. 120 4.8 Materials and Methods In-silico modelling All the chemical modifications, binding site identification and visualisations of the compounds were performed in Molecular Operating Environment (251). The protein structures utilized for docking underwent preparation using the Protein Preparation Wizard from SchroÌdinger (99). This preparation encompassed several steps, such as assigning hydrogen bonds (utilizing PROPKA with a pH of 7.0), minimizing the structures employing the OPLS3e force field (while constraining heavy atoms to an RMSD of 0.30Ã ), and adding any missing side chains. As for ligand preparation, it was conducted using the Omega toolkit (331) and involved tasks like standardizing SMILES notation, adjusting ligand tautomers, and generating conformers. We performed molecular docking using Glide (99), ICM (91) and FRED (92) using their default settings. To assess ADMET properties (absorption, distribution, metabolism, excretion, and toxicity), we used SwissADME software (427). DeepDocking was performed with default parameters as described in Gentile et al (47). Redocking with GlideSP (99) was performed with default parameters. Docking grids were calculated with the grid generation software from Schrodinger. FRED docking was performed with default parameters. Distances between docking poses were generated using MOE (251) software. ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties were calculated using SwissADME software (427). Chemicals Life Chemicals Ltd. produced Lin28 inhibitor derivatives on demand with a purity of >90%. LC/MS/MS was used to confirm the molecular masses after obtaining the chemicals. 121 Fluorescence Polarization Assay The fluorophore tagged pre-Let-7d-FAM probe with the sequence of UUAGGGCACGAGAUUUUGCCCACAAGGAGUU were purchased from Integrated DNA Technologies (IDT). The probe at the concentration of 1nM-50uM was incubated with Lin28b(ZKD) or Lin28(CSD+ZKD) in a final volume of 30ul at room temperature for 20 minutes. To read the fluorescence, TECAN 500 machine was used. Fluorescence polarization was calculated using the following equation: ð =ð¹ððððððððð â ð¹ðððððððððð¢ðððð¹ððððððððð + ð¹ðððððððððð¢ððð where P is the fluorescence polarization reading, F parallel is the fluorescence intensity acquired from the parallel excitation plane, and F perpendicular is the fluorescence intensity acquired from the perpendicular direction of the excitation plane. The total fluorescence intensity is estimated using the following equation: ð¹ = ð¹ðððððððð + 2ð¹ðððððððððð¢ððð All the raw FP signal data from the TECAN 500 FP results was analyzed using Prism 8.0 (GraphPad); normalized curves were plotted and IC50 values were estimated. All FP data was repeated three to five times. Electrophoresis Mobility Shift Assay (EMSA) EMSA followed the standard protocols we published with minor modifications (428-430). Let-7d miRNA probe (UUAGGGCAGAGAUUUUGCCCACAAGGAGUU) was labeled with IRDye800CWN and purchased from IDT (Coralville, USA). Lin28b or Lin28a proteins was incubated with 300 nM of probe in a standard 20ul EMSA reaction in a reaction buffer containing 122 20 mM HEPES-NaOH, pH 7.4, 2mM DTT, 3 mM MgCl2, 0.05% NP-40, protease inhibitors, and 0.1 ug poly[dI:dC]. After incubation for 30 min at 37C, reactions were loaded onto 4% non-denatured polyacrylamide gel in 0.5X TBE buffer. The RNA oligo bands were visualized by using the Li-Cor Odyssey 9120 Infrared Imaging System. Bio-Layer Interferometry (BLI) Assays BLI assays were performed as we previously reported (431, 432) to qualify the direct and reversible interactions between small molecules and Lin28b ZKD by using OctetRED (ForteBio, Bohemia, NY). Purified Lin28b ZKD (50 μg/mL) was bound to the super-streptavidin sensors over 50 min at room temperature. The sensor was kept in an assay buffer [20 mM N-2-hydroxyethylpiperazine-N0 -2-ethanesulfonic acid (HEPES), 150 mM NaCl, 500 μM tris(2-carboxyethyl) phosphine (TCEP), and 1% dimethylsulfoxide (DMSO)], when small molecules were added. After 200 seconds, a wash buffer was used to flush the sensor plate for 100 seconds. BLI signal was recorded throughout the 300 seconds following the manufactureâs instruction from OctetRED (ForteBio, Bohemia, NY). Cell Line and Cell Maintenance and other Routine Techniques DUNE cell line and DUNE(KO) line with Lin28b knockout by CRISPR were reported in our previous studies (346, 422). IGROV1 cells were obtained from Sigma-Aldrich. DUNE and DUNE and DUNE(KO) cells were cultured in Dulbeccoâs Modified Eagle Medium with high Glucose/L-glutamine (DMEM; Hyclone, Logan, UT, USA) and 10% of fetal bovine serum (FBS; Gibico, Waltham, MA, USA). IGROV1 cells were cultured in Roswell Park Memorial Institute 1640 (RPMI-1640; Hyclone) medium with 10% FBS. All the cell lines were incubated in 5% CO2 at 37°C and were tested for mycoplasma contamination monthly and cell line authentication was tested by short tandem repeat profiling by ATCC. 123 There are several routine molecular techniques, including real-time RT-PCR, flow cytometry, cell proliferation, colony formation, and immunoblotting assays had been reported previously by our lab (346, 431-435). Spheroid formation For a spheroid generation, 1000 cells/well of DuNE cells were seeded 96-well ULA round-bottomed plates, and 3D multicellular spheroids were spontaneously generated after 24 hours culture (Corning B.V. Life Sciences, Amsterdam, The Netherlands) using a multichannel pipette. Plates were incubated for 6 days at 37°C, 5% CO2, and 95% humidity. Fully automated image analysis of tumour spheroids was carried out with the Incucyte® S3 Live-Cell Analysis System. The size and shape of multicellular spheroids were analyzed by using the Incucyte ® Spheroid Analysis Software Module. On day 3, 100â¯Î¼L of the old media was exchanged by 100â¯Î¼L of the fresh media with the correspondent concentration of was added into each well. During media changes, the pipet tip was held at about a 45° angle away from the center of the well to protect spheroids from disruption. Statistics For statistical analysis, we utilized the GraphPad Prism 9.01 software (GraphPad Software, San Diego, CA, USA). Using a student t-test, the differences between the two groups were compared. One-way ANOVA was used to compare differences across several groups, and a t-test was used after that. The thresholds for significance were chosen at p < 0.05 as *, p < 0.01 as **, and p < 0.001 as ***, respectively. 124 HOXB13 target 5.1 Introduction 5.1.1 HOXB13 Structure and Function HOXB13 belongs to the Hox homeobox family of transcription factors that regulate axial regional differentiation during fetal development in tissue- and stage-specific fashion. The homeobox genes, including HOXB13, are organized into chromosomal clusters designated HOXA, HOXB, HOXC, and HOXD, with their genomic arrangement dictating their expression along the body's anterior-posterior (AP) axis. This organization is crucial for defining the identity of body segments during development, highlighting the integral role of HOXB13 and other HOX genes in developmental processes. The HOXB13 paralog has been tightly linked to the developmental processes of urogenital organs including the prostate gland (436). In healthy organisms, HOXB13 plays an important role in posterior organogenesis and its expression is maintained in distal colon tissues in adults (437). HOXB13 is characterized by a highly conserved DNA-binding domain known as the homeobox, which comprises approximately 60 amino acids (Figure 5-1) (438). This domain enables HOXB13 to bind to specific DNA sequences, primarily rich in adenosine and thymidine nucleotides, thereby regulating the expression of target genes. The structural specificity of HOXB13 contributes to its function as a sequence-specific transcription factor, playing a crucial role in cellular and developmental processes. The DNA-binding domain enables HOXB13 to bind to a core DNA sequence predominantly composed of adenosine and thymidine nucleotides (TAAT), although individual homeodomains have specific sequence preferences (439, 440). This specificity underlines HOXB13's role in regulating gene expression tightly. 125 Expressed predominantly in the posterior domains of the spinal cord, digestive tract, and urogenital sinus, HOXB13 is essential for proper prostate development (441). Its role extends beyond developmental stages, impacting cellular processes such as proliferation, differentiation, and migration in adulthood. The spatial and temporal expression of HOXB13, along with other HOX genes, follows a colinear pattern that is critical for specifying the identity of body segments along the anterior-posterior axis. Figure 5-1. Structural homology of HOX members. In red â residues directly facing the DNA. In blue â residues that are different in HOXB13 compared to other HOX members. 5.1.2 HOXB13âs Role in Prostate Cancer Mutations in the HOXB13 gene have been identified as a risk factor for the development of PC (442). Evidence exists that HOXB13 functions through AR both in malignant and normal cells (443). For instance, Norris et al (2009) found that HOXB13 knockout suppressed the growth of PC cells by inhibiting the transcription of AR-regulated genes (438). Crucially they also showed that the homeodomain of HOXB13 interacts with the AR protein. Another study suggested that HOXB13 might be partially responsible for the growth of androgen-independent PC (444). Indeed, a recent study found that HOXB13 stimulates CRPC growth by binding to the truncated AR variant AR-V7 and enhancing the upregulation of various oncogenes (445). Importantly, HOXB13 silencing inhibited AR-V7-driven PC growth both in vivo and in vitro (Figure 5-2) (446). Thus, 126 HOXB13 serves as a pivotal upstream regulator of AR-V7 and represents an attractive target for CRPC therapeutics. Figure 5-2. HOXB13 is selectively essential to PCa. (A) Proliferation assay in PCa and non-prostatic cell lines with HOXB13 knockout via CRISPR/Cas9 gene editing system (n=3; mean ± SEM); and (B) shRNA knockdown system showing the selective essentiality of HOXB13 for PCa (n=4 as biological replicates; mean ± SEM); (C) Tumor growth in vivo characterizing the effect of HOXB13 on PCa initiation in AR-dependent (n=8;mean ± SEM) and (D) AR-independent (n=15; mean ± SEM) subcutaneous xenografts. Besides its prominent role in PC and AR regulation, HOXB13 has been also shown to regulate other cancer-relevant proteins including FOXA1 (447), Cyclin D1 (448) and p21 protein (449). 127 Several studies highlighted the role of HOXB13 in the promotion of tumour growth in breast and ovarian cancers and melanomas (450-452). Therefore, the applicability of HOXB13 targeting could extend beyond PC. The main challenge of HOXB13 targeting comes from its highly conserved structure among other members of the HOX13 family, namely HOXA13, HOXC13 and HOXD13. Herein, we aim to develop a selective inhibitor of HOXB13 with the use of CADD tools. 128 5.2 CADD of Novel HOXB13 Inhibitors 5.2.1 Identifying HOXB13 Potential Binding Sites First, we searched Protein Data Bank for existing crystal structures of HOXB13. The available structures cover only the DNA-binding fragment spanning residues 209-284. The remaining part of the protein predicted by AlphaFold appears to be of disordered structure and thus not suitable for drug targeting (453). Furthermore, there is no information about the functionality of the remaining part of the protein. Therefore, we decided to pursue targeting the DNA-binding region of HOXB1. The atomic coordinates and diffraction data have been deposited to the Protein Data Bank with the accession codes 5EDN and 5EEA, for HOXB13-DNATCG and HOXB13-DNACAA respectively. Interestingly, while most transcription factors (TFs) can only bind a set of very closely related nucleotide sequences representing a single optimal, human HOXB13 may bind to two optimal DNA sequences of CAATAAA and TCGTAAA (454). Importantly, the interaction of HOXB13 with the CAA sequence was driven by a change in enthalpy (DH), whereas the TCG site was bound with similar affinity due to a smaller loss of entropy (DS). While both entropy and enthalpy contributions are essential to achieve strong ligand-protein binding, it is notoriously difficult to optimize for entropy contribution (455). Thus, we reasoned that working with a structure that has been proven to form enthalpy-driven binding would be beneficial to use for small molecule inhibitor design. Thus, we started with the HOXB13-DNACAA structure with a resolution of 2.2Ã (PDB: 5EEA (454)). Second, an analysis of the surface of the DNA-binding domain revealed a deep cavity next to TTA nucleotides (Error! Reference source not found.). We further assessed the surface with pocket identification software from MOE, SiteFinder, and confirmed that the observed cavity was ranked as the top scoring (251). The cavity received a high propensity of ligand binding score of 2.23, 129 which is calculated based on the amino acid composition of the pocket (253). Additionally, the site has a calculated volume of 66 atomic spheres indicating that the cavity can readily accommodate a wide range of small molecules. The cavity is hydrophobic enough as indicated by the green patches on the calculated surface, thus represents a favourable environment for a small molecule binding. Overall, we concluded that the proposed site is suitable for potential ligand binding and thus can be used for docking screening. 5.2.2 Exploring Pocket Flexibility and Selectivity We performed an analysis of the potential selectivity of the chosen site. A desirable inhibitor selectively targets the selected protein. However, the chosen protein provides molecular grounds for selectivity by being significantly distinct from the closest analogs. Thus, we first identified the closest analogs of HOXB13 â HOXA13, HOXC13 and HOXD13 with HOXC13 being the most similar to HOXB13 in the area of the identified binding site. The only amino acid difference in the area of the binding site is isoleucine 211 which is substituted for valine in A/C/D isoforms. Figure 5-3. The identified binding site on the surface of HOXB13. White ribbon â DNA, red ribbon â protein, coloured surface â binding site surface (green â hydrophobic patches, red â solvent exposed patches), spheres â binding site atomic spheres (grey â hydrophobic regions, red â charged). 130 However, the two residues are structurally and functionally very similar and would not provide a mechanistic basis for selective binding to HOXB13. Next, we observed that residues at position 224 and 226 are also different between HOXB13 and the rest of the analogs, serine vs threonine and glycine vs valine, respectively (Figure 5-4). Although these residues are not positioned directly at the interface of the binding site, we speculated that the differences might result in higher conformational flexibility in HOXB13 that can be exploited for a selective targeting. Figure 5-4. Selectivity analysis of HOXB13 targeting. HOXB13 (PDB: 5EEA) in blue and HOXC13 (AlphaFold) in yellow structures overlaid. The spheres and the surface represent the binding site. Residues that might confer selectivity are in bold (Ser224, Gln227 and Gly226). In particular, we noticed that the pocket arm extending from Ser224 is more flexible in HOXB13 compared to other isoforms. This happens because of the differences in amino acids at Ser224 (Thr in HOXC13) and Gly226 (Val in HOXC13). First, the presence of glycine itself provides a higher conformational flexibility due to the lack of any side chains that might constrain residue mobility. Second, we observed that the hydrogen bond between the side chain of Ser224 and Gln227 is much 131 weaker than those in HOXC13 (Thr + Gln), -2.1 vs -4.3 kcal/mol, respectively, as calculated by MOE. To validate our hypothesis, we run molecular dynamics (MD) simulations on both structures. Indeed, we observed that the arm covering residues 224-228 is more flexible in HOXB13 than in HOXC13 throughout a 500ns MD simulation (root mean square fluctuation 2.4Ã vs 1.6Ã , respectively) (Figure 5-5). We then run replica exchange simulations to capture larger conformational changes. We clustered snapshots derived from replica exchange to get representative conformational changes. We run pocket volume analysis on representative structures derived from replica exchange simulations to quantify dynamic pocket changes. The analysis revealed that the region of the selected pocket is more open and more flexible in HOXB13 structure compared to HOXC13. Furthermore, after aligning five representatives of replica exchange with the initial structures of HOXB13 and HOXC13, we visually confirmed that in HOXB13 the arm extending from Gly226 and constituting the pocket departs further from the initial conformation. In particular, residue Pro222 of the pocket is moving significantly more in HOXB13 than in HOXC13 (RMSF 5.76 vs 2.5 A). In certain conformations, Tyr223 moves closer to the pocket in HOXC13 reducing the ligand accessible volume. In contrast, HOXB13 has Tyr223 buried deeper in the pocket because the moving arm drags it away from the pocket surface. Overall, in HOXB13, flexibility conferred by Ser226/Gln227 interaction and Gly224 enables the movement of the pocket arm including Tyr223 and Pro222 further away from the pocket. Thus, the cavity is dynamically deeper in HOXB13 and would more likely accommodate a ligand. We can attempt to achieve HOXB13 selectivity by leveraging pocket arm flexibility. Notably, a study on HSP90 showed that increased pocket flexibility might favourably influence protein-ligand binding due to greater entropic contribution and enable isoform selectivity (456). 132 Figure 5-5. 500ns MD simulations on HOXB13 and its closest analog HOXC13. Residues 224-228 of the HOXB13 pocket are more flexible compared to that of HOXC13 (peak RMSF = 2.4 Ã vs 1.6 Ã ) (shown in red). This flexibility results in a dynamically deeper and more accessible pocket in HOXB13, potentially enhancing its ability to accommodate ligands and providing a basis for isoform-selective targeting. 5.2.3 Virtual Screening for HOXB13 Inhibitors We employed our in-house developed CADD pipeline to screen for potential inhibitors of HOXB13 using a 2 million library composed of various focused libraries (molecules with enriched bioactivity). The VS general procedure resembled the pipeline described for the AR target and outlined in Figure 5-6. We first run the selected library of compounds through Glide and ICM molecular docking software. To refine our search and ensure the accuracy of our docking poses, we applied a stringent consensus criterion, which only accepted those poses with an RMSD of no more than 2Ã between them. This step was crucial to filter out false positives and narrow down our candidates to those with the most consistent docking conformations. After the docking stage, we employed the ADMET predictor software from Simulations-Plus® to exclude any compounds with predicted toxic or harmful properties. This ADMET profiling is an essential step to ensure that the selected 133 compounds have a favourable safety profile and are likely to have good pharmacokinetic properties, enhancing the likelihood of successful development into safe and effective drugs. To address the dynamic nature of the HOXB13 protein and to enhance the selectivity of our potential inhibitors, we incorporated a dynamic pharmacophore model into our CADD pipeline. This was informed by the observation of differential motions between HOXB13 and its closest homolog, HOXC13, from molecular dynamics simulations. The dynamic pharmacophore model allowed us to account for and capitalize on these observed differences in motion and flexibility. Utilizing Grail software (457), we transformed clusters of protein snapshots from the simulations into a series of dynamic pharmacophore models. These models were then used to screen our library of compounds, selecting those that were compatible with the dynamic pharmacophore of HOXB13 but not with the more rigid model of HOXC13. This strategic approach aimed to exploit the distinct, flexible pocket in HOXB13 for exclusive binding, thereby achieving the desired selectivity. Indeed, the utility of the dynamic pharmacophore model was evident as the majority of compounds conformed to the HOXB13 pharmacophore while failing to fit within the shallower and less accommodating pocket of HOXC13. From our refined list, we identified 350 promising Figure 5-6.VS pipeline to identify selective inhibitors of HOXB13 134 compounds. Of these, 213 compounds with the most favourable profiles were procured for subsequent experimental validation. In summary, by integrating a dynamic pharmacophore model into our virtual screening pipeline, we were able to tailor our search for compounds that not only bind to HOXB13 but do so in a way that leverages the unique conformational dynamics of the protein to achieve specificity. This innovative approach underscores the significance of considering protein motion and flexibility in the drug design process, paving the way for the development of highly selective inhibitors for HOXB13. 135 5.3 Experimental Evaluation of HOXB13 Inhibitors The experimental evaluation pipeline for the screening of 213 molecules targeting HOXB13 is illustrated in Figure 5-7 and proceeds through a multi-tiered selection process. The initial phase involves a cell viability assay on HOXB13-dependent cell lines, specifically PC3 and LNCaP, to identify compounds that can effectively inhibit cell growth. Compounds that successfully reduce cell viability below the 60% threshold progress to the next stage. Ensuring non-cytotoxic effects on non-target cells, the subsequent phase involves a cytotoxicity assay in HOXB13-independent HEK293t cells. Here, the aim is to filter out compounds that induce significant cell toxicity, accepting those with less than 10-20% toxicity as suitable for further analysis. Figure 5-7. Screening Pipeline for Selective HOXB13 Inhibitor Evaluation. Following this, a counter-screen on HOXC13-dependent cells, namely JURKAT and MCF-7 cell lines, is conducted to assess off-target effects. Molecules showing less than 10-20% off-target 136 effects continue to the subsequent testing phase. The remaining candidates are then evaluated for their ability to inhibit HOXB13:AR transcription activity, with a benchmark inhibition rate of over 40% as the target for potential drug efficacy. The final step of the pipeline applies a direct binding assay to the most promising molecules, typically narrowing down to approximately two leading candidates. This assay measures the kD for the molecule-protein interaction, and verifies the impact on DNA binding, confirming the specific binding affinity of the small molecules to HOXB13. Each step of this carefully structured pipeline serves to ensure that the molecules not only exhibit the desired inhibitory action on HOXB13-dependent cells but also maintain a low cytotoxic profile in cells where HOXB13 is not implicated, bolstering the selection of potential therapeutic agents with optimal target specificity and minimal side effects. 5.3.1 Inhibitory Effects on Cancer Cell Growth We observed a range of molecules demonstrating selective growth inhibition in the HOXB13-dependent cell lines, as shown in Figure 5-8. Notably, Molecules 5 and 18 exhibit a marked decrease in viability in these cell lines, suggesting potent inhibitory effects. In contrast, the viability impact on HEK cells, which are HOXB13-independent, is considerably less, indicating a lower level of cytotoxicity and a potential specificity for the target protein HOXB13. Molecule 152, for example, while reducing viability in LNCaP and PC3 cells, shows minimal effect on HEK cells, highlighting its potential as a selective inhibitor. Conversely, Molecule 13 shows a high viability percentage across all cell lines, suggesting a lower efficacy in inhibiting cell growth. 137 These findings are pivotal for selecting candidate molecules for further development and validation studies. Figure 5-8. Cell viability response to HOXB13-targeted inhibitors across cell lines. Top: The the viability of LNCaP, PC3, and HEK cell lines after a 72-hour treatment with various small molecule inhibitors at a 25µM concentration, highlighting differential effects indicative of potential selective toxicity. Bottom: Structures of the selected inhibitors. 5.4 Discussions and Future Directions In this chapter, we explore the computational-aided drug design (CADD) techniques employed to develop selective inhibitors of HOXB13. The integration of structural modelling and molecular dynamics simulations provided a solid foundation for identifying a selective interaction mechanism. A pivotal element of our strategy was the innovative use of dynamic pharmacophores, 138 tailored to distinguish HOXB13 from its homologs by exploiting the unique conformational dynamics observed between these closely related proteins. The application of dynamic pharmacophores marks a significant advancement in the field of CADD, especially for proteins like HOXB13 that exhibit significant structural similarity to other family members. This approach proved invaluable in identifying the differential flexibility of the binding sites, enabling the selective targeting of HOXB13. Our investigative journey from computational models to preliminary experimental validation revealed several compounds with promising selective inhibitory effects on HOXB13-dependent cell lines, LNCaP and PC3 while sparing the HOXB13-independent HEK cell line. This differential inhibitory profile hints at the specificity of the compounds' action, suggesting on-target effects. The proposed binding pose for these compounds further elucidates their mechanism of action. Notably, compound 152 was modelled to form hydrogen bonds with Arg220, a key residue implicated in the protein-DNA binding interaction. This interaction is crucial, as Arg220 also engages with the DNA sugar backbone, suggesting that our compounds could competitively inhibit HOXB13 by mimicking its natural DNA interactions. This mechanism of inhibition mirrors that observed in other systems, such as the AR DBD P-box and Lin28, where inhibitors block the protein-nucleic acid interface directly. The direct blockade of the protein-DNA interaction by our 139 compounds underscores a promising therapeutic strategy, targeting essential transcription factor activities. Figure 5-9. Comparison of binding interactions of HOXB13 with DNA and molecule 152. On the right, the figure illustrates HOXB13 (gray) in complex with DNA (yellow), highlighting key interaction points (red blobs) where HOXB13 binds to the DNA. On the left, a proposed binding model showcases molecule 152's interaction with the same critical HOXB13 residues, suggesting a competitive mechanism that obstructs the protein-DNA interaction essential for HOXB13's biological function. However, transitioning from in silico designs to tangible therapeutic agents necessitates extensive experimental validation. Our elaborately planned experimental pipeline aims to further assess these compounds, albeit the absence of positive controls presents a challenge, highlighting the innovative nature of our target. 140 5.5 Conclusions This chapter demonstrates the use of CADD in developing selective inhibitors of HOXB13, an emerging target in PC. Our combined use of structural modelling and dynamic pharmacophores enabled targeted inhibition of HOXB13, distinguishing it from highly homologous proteins. The inhibitors, particularly compound 152, showed promising selective effects on HOXB13-dependent cell lines, indicating potential for targeted therapy. These findings suggest the feasibility of using CADD in developing inhibitors for proteins with complex binding patterns like HOXB13, and they open avenues for further research in selective cancer therapies and protein-DNA interaction studies. More broadly, this work underscores the capability of CADD in pioneering drug discovery for novel targets, particularly those with limited experimental data. It serves as a proof of concept, illustrating the potential to target complex proteins characterized by conserved and solvent-exposed pockets, such as protein-DNA interfaces. The synergy between computational modelling and experimental evaluation is essential, paving the way for future drug discovery efforts. 141 5.6 Materials and Methods In-silico modelling All the chemical modifications, binding site identification and visualizations of the compounds were performed in Molecular Operating Environment (251). The protein structures utilized for docking underwent preparation using the Protein Preparation Wizard from SchroÌdinger (99). This preparation encompassed several steps, such as assigning hydrogen bonds (utilizing PROPKA with a pH of 7.0), minimizing the structures employing the OPLS3e force field (while constraining heavy atoms to an RMSD of 0.30Ã ), and adding any missing side chains. As for ligand preparation, it was conducted using the Omega toolkit (331) and involved tasks like standardizing SMILES notation, adjusting ligand tautomers, and generating conformers. We performed molecular docking using Glide (99), ICM (91) and FRED (92) using their default settings. To assess ADMET properties (absorption, distribution, metabolism, excretion, and toxicity), we used SwissADME software (427). Docking was performed with GlideSP (99) and ICM (91) with default parameters. Glide docking grids were calculated with the grid generation software from Schrodinger. Distances between docking poses were generated using MOE (251) software. ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties were calculated using SwissADME software (427). Molecular dynamics (MD) were performed using the Desmond package from Schrodinger. The structure was solvated using the System Builder package from Schrodinger using a simple point charge (SPC) water model (332) and neutralized by adding Na+ ions. The system was built using the OPLS3e force field (333). The simulations were carried out with a 2-femtosecond time step using a constant-pressure (NPT) ensemble at 300K and a pressure of 1.03 bar. Each simulation 142 recorded 1000 frames that were then analyzed using the Simulation Interactions Diagram module (Schrodinger). GRAIL is a grid-based technique that leverages the wide array of protein conformations obtained from simulations alongside the pharmacophore model to produce visual representations of interaction surfaces within a predefined grid box. For each pharmacophore feature, GRAIL generates a map using the protein's trajectory data in PDB format, which includes aligned residues across all frames. GRAIL (457) maps visualize six types of interaction features: hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), hydrophobic areas (H), positively ionizable areas (PI), negatively ionizable areas (NI), and aromatic rings (AR), as well as halogen bond donors (XBD) and acceptors (XBA). These maps consider only the protein's conformations, excluding ligands from their calculations. To prepare for GRAIL mapping, trajectory files were converted into multi-PDB files, with water molecules and ions either included or removed, followed by residue alignment. Topology files were also processed to exclude water and ions. Initially, a grid file was created from the aligned trajectory and topology data, producing a singular output file. Subsequently, the residues to be included in the grid were specified in a text file, which then guided the generation of the grid from the output file. This process could also be applied to compute an average grid. The resultant grids were decomposed into individual pharmacophore GRAIL maps in KONT file format, compatible with LigandScout software. Along with average grid calculations, scripts were utilized to create GRAIL maps for assessing "happy/unhappy" waters, which were then converted to KONT files for analysis. Within LigandScout, a detailed examination of these GRAIL maps revealed the pharmacophore interaction surfaces. These interactions were visualized through 143 distinct colour patterns, representing potential pharmacophore features, after setting the score threshold of the GRAIL maps above a minimum contour level of 0.5. The analysis could encompass the entire protein or focus solely on the binding pocket by loading specific KONT files, facilitating a comprehensive visualization of the pharmacophore interaction fields. Cell Culture In this study, LNCaP, PC3 and HEK293t cell lines were used. PC3 and LNCaP cells were cultured in RPMI medium (Gibco, 11875093) supplemented with 10% heat-inactivated fetal bovine serum (FBS, Gibco, A5670801). HEK293t cells were cultured in Dulbeccoâs modified Eagleâs medium (DMEM, Gibco) supplemented with 10% FBS. Cultures were maintained in a humidified (85%) incubator with 5% CO2 at 37°C. LNCaP cells were passaged every 3-4 days, while HEK293t cells were passaged every 2-3 days. Cell Viability/Toxicity Assessment LNCaP and PC3 cells were used as HOXB13-dependent PC cell models. HEK293t cell line was used HOXB13 independent controls to test cellular toxicity. All cell viability/toxicity assays were performed in 96-well plate format. For cell viability assay, 5*10^4 PC3 or LNCaP cells were seeded to a 96-well plate in 150ul RPMI medium supplemented with 10% FBS. After 24h, cells were treated with a 50uL medium containing 4x the desired concentration of small molecules. DMSO and docetaxel were included as negative and positive controls, respectively. After 72h incubation at 37°C, cell viability was measured with PrestoBlueâ¢ Cell Viability Reagent (Invitrogen, A13262) following the manufacturerâs protocol. Briefly, 20ul PrestoBlue reagent was added to each well containing 200ul of medium + small molecule mixture. The plates were further incubated at 37°C for 1h in a dark incubator. Cell viability was measured by reading the emission at 612nm (excitation 535nm) with 144 a TECAN Infinite® 200 PRO microplate reader. Relative cell viability was calculated as follows: viability = (FLsample-FLdoce)/ (FLDMSO-FLdoce) Ã 100. The sample was fluorescence obtained from the cells incubated with corresponding small molecules; FLDMSO and FLdoce were fluorescence obtained from DMSO and docetaxel controls, respectively. Each experiment was performed with 3 technical replicates and repeated at least 3 times (n=3). To test the toxicity of small molecules on HOXB13-independent cell lines, the same protocol explained above was performed with HEK293t cells with a few modifications. For HEK293t cells, DMEM medium supplemented with 10% FBS was used; and, cell seeding density was 3.5*10^5 cells per well. This experiment was performed two times to test the effect of small molecules at 25uM and 6.25uM final concentrations. 145 Conclusions In this thesis, a synergetic approach combining computational methodologies and experimental validations is employed to develop small molecule inhibitors targeting "undruggable" protein-nucleic acid interactions. The introductory chapter sets the stage by describing the key CADD tools used throughout this thesis work. It highlights the critical role of recent technological advancements in addressing the challenges posed by protein-nucleic acid interaction targets. In particular, I discuss how AI enables the screening of ultra-large libraries for potential target binders. Chapter 2 delves into a comprehensive literature review of protein-DNA interaction inhibitors designed using CADD, drawing on examples from our and other research groups. This chapter extensively discusses various in silico approaches, showcasing the versatility and broad application of CADD in the development of therapeutic agents. Subsequent chapters 3, 4, and 5 are dedicated to detailed case-studies of targeting the androgen receptor (AR), Lin28, and HOXB13 proteins. Each of those target presents unique challenges in the realm of developing novel cancer therapeutics, and this thesis demonstrates how CADD tools can be effectively harnessed to design them. In particular, Chapter 3 underscores the pivotal role of CADD in the discovery and optimization of novel AR inhibitors by leveraging the existing experimental insights. We specifically utilized previously identified AR binders to benchmark and select the most effective docking tools for accurately scoring interactions on the AR DBD surface. This strategic approach facilitated a refined understanding of the docking accuracy essential for modelling complex protein-DNA interfaces. Further enhancing our CADD strategy, we developed a pharmacophore model based on the docking poses of these known inhibitors, guiding the targeted screening of chemical libraries. This screening identified compounds with unique chemotypes as 146 potential antiandrogens, demonstrating CADD's efficiency in exploring vast chemical spacesâa task unachievable through traditional experimental approaches alone. Thus, Chapter 3 exemplifies the critical synergy between CADD and experimental data. Chapter 4 showcases the comprehensive utilization of CADD in navigating the drug discovery process, highlighting its capability to tackle the intricate challenge of targeting RNA-binding proteins, such as Lin28. Molecular modelling was pivotal in unveiling a previously unexplored binding site on Lin28 surface, marking a significant leap forward in understanding this protein's interaction with RNA. The employment of ultra-large virtual screening was instrumental in sifting through vast chemical databases, leading to the identification of unique chemotypes. This approach resulted in a notable hit rate of 9%, demonstrating the efficiency of ultra-large virtual screening. Further, in-depth visualization of Lin28-RNA interactions via computational tools provided crucial insights that informed both the selection of the binding site and the subsequent optimization of compounds. This process was guided by a nucleotide-based inspired design, allowing for a targeted enhancement of compound efficacy. By marrying the detailed in silico analysis of Lin28-RNA interactions with SAR observations, we were able to refine our lead compounds with greater specificity and potency. Chapter 5 of the thesis delves into the exploration of HOXB13, a novel target in PC therapeutics. This chapter underscores the pivotal role of CADD in addressing the formidable challenge of achieving selectivity among highly homologous proteins, a common obstacle when targeting the generally conserved surfaces of protein-DNA interactions. Specifically, we were able to exploit dynamic differences between HOXB13 and its close analog HOXC13, identified through MD and replica exchange simulations. These differences were then implemented into virtual screening (VS) using a novel technique of dynamic pharmacophore modelling. By employing those CADD 147 approaches we were able to capture the intricate differences between protein structures to achieve the required selectivity. The application of dynamic pharmacophore models, informed by comprehensive MD simulations, allowed for the identification of unique binding site features that are preferentially presented in HOXB13 but not in HOXC13. This innovative approach not only facilitated the discovery of potential inhibitors that could specifically target HOXB13 but also showcased the utility of CADD in navigating the complexities of highly conserved protein domains. The insights gained from this work contribute to a broader understanding of how to approach the design of inhibitors for conserved protein domains, offering a blueprint for future endeavors in targeting proteins previously considered as "undruggable". 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Thesis/Dissertation
eng
Bioinformatics
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Cherkasov, Artem
Dong, Xuesen
Development of small molecule inhibitors of protein nucleic acid interactions with the use of computer-aided drug discovery tools
Text