Machine learning for antimicrobial peptide discovery and design
department or school: Science, Faculty of
data provider: DSpace
degree campus: UBCV
creator: Li, Chenkai
issued: 2025-04-30T07:00:00Z
issued: 2024
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: The growing global health concern of antibiotic resistance is prompting researchers to seek substitutes for conventional antibiotics. Antimicrobial peptides (AMPs), a diverse class of short and often cationic biological molecules, are gaining attention as promising candidates. While direct, large-scale wet lab screening is time-consuming and costly, using high-throughput bioinformatics tools to discover and design novel AMPs is an attractive approach. In this thesis, I introduce in silico tools for AMP discovery and design with machine learning models, and present the novel AMPs revealed by those methods. The in silico discovery of AMPs typically involves the investigation of huge genomics, transcriptomics, or protein datasets, and accurate methods to sift through such large volumes of candidate sequences are required. In this thesis, I introduce AMPlify, a deep learning based tool for AMP prediction, improving upon the state-of-the-art methods by incorporating attention mechanisms. By integrating AMPlify into bioinformatics pipelines or workflows, four novel AMPs with proven antimicrobial activity have been identified from the Rana [Lithobates] catesbeiana (bullfrog) genome, as well as 13 other novel AMPs mined from the UniProtKB/Swiss-Prot database. On the other hand, the potential sequence space of amino acids is combinatorially vast, allowing for the exploration of more AMPs that may not exist in nature to further expand the current arsenal of peptide-based therapeutics. However, manual design of novel synthetic AMPs requires prior field knowledge, restricting its throughput. In silico sequence generation methods for de novo AMP design stand out to be a high-throughput way to unearth novel synthetic AMPs. In this thesis, I introduce a recurrent neural network based tool, named AMPd-Up, for AMP sequence generation, and demonstrate its performance over existing methods. With AMPd-Up, 40 novel synthetic AMPs have been designed with proven antimicrobial activity aga…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/87512
note: MACHINE LEARNING FOR ANTIMICROBIAL PEPTIDE DISCOVERY AND DESIGN by Chenkai Li M.Sc., University College London, 2016 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Bioinformatics) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) February 2024 © Chenkai Li, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Machine learning for antimicrobial peptide discovery and design submitted by Chenkai Li in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Bioinformatics Examining Committee: Inanç Birol, Professor, Medical Genetics, University of British Columbia Supervisor Raymond Ng, Professor, Computer Science, University of British Columbia Supervisory Committee Member Linda M.N. Hoang, Clinical Professor, Pathology and Laboratory Medicine, University of British Columbia Supervisory Committee Member Leonard J. Foster, Professor, Biochemistry and Molecular Biology, University of British Columbia University Examiner Colin Ross, Professor, Pharmaceutical Sciences, University of British Columbia University Examiner Additional Supervisory Committee Members: Sara Mostafavi, Associate Professor, Computer Science and Engineering, University of Washington Supervisory Committee Member iii Abstract The growing global health concern of antibiotic resistance is prompting researchers to seek substitutes for conventional antibiotics. Antimicrobial peptides (AMPs), a diverse class of short and often cationic biological molecules, are gaining attention as promising candidates. While direct, large-scale wet lab screening is time-consuming and costly, using high-throughput bioinformatics tools to discover and design novel AMPs is an attractive approach. In this thesis, I introduce in silico tools for AMP discovery and design with machine le…
has type: Thesis/Dissertation
date issued: 2024-05
is shown at: 10.14288/1.0440537
language: eng
degree discipline: Bioinformatics
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Birol, Inanc
type: Text
identifier URI: http://hdl.handle.net/2429/87512