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AMP0 : species-specific prediction of anti-microbial peptides using zero and few shot learning
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Gull, Sadaf and Minhas, Fayyaz ul Amir Afsar (2022) AMP0 : species-specific prediction of anti-microbial peptides using zero and few shot learning. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 19 (1). pp. 275-283. doi:10.1109/TCBB.2020.2999399
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WRAP-AMP0-species-specific-prediction-anti-microbial-peptides-using-zero-few-shot-learning-Minhas-2020.pdf - Accepted Version - Requires a PDF viewer. Download (1266Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/TCBB.2020.2999399
Abstract
Evolution of drug-resistant microbial species is one of the major challenges to global health. Development of new antimicrobial treatments such as antimicrobial peptides needs to be accelerated to combat this threat. However, the discovery of novel antimicrobial peptides is hampered by low-throughput biochemical assays. Computational techniques can be used for rapid screening of promising antimicrobial peptide candidates prior to testing in the wet lab. The vast majority of existing antimicrobial peptide predictors are non-targeted in nature, i.e., they can predict whether a given peptide sequence is antimicrobial, but they are unable to predict whether the sequence can target a particular microbial species. In this work, we have used zero and few shot machine learning to develop a targeted antimicrobial peptide activity predictor called AMP0. The proposed predictor takes the sequence of a peptide and any N/C-termini modifications together with the genomic sequence of a microbial species to generate targeted predictions. Cross-validation results show that the proposed scheme is particularly effective for targeted antimicrobial prediction in comparison to existing approaches and can be used for screening potential antimicrobial peptides in a targeted manner with only a small number of training examples for novel species. AMP0 webserver is available at http://ampzero.pythonanywhere.com.
| Item Type: | Journal Article | ||||||||
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| Subjects: | Q Science > Q Science (General) Q Science > QR Microbiology R Medicine > RS Pharmacy and materia medica |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
| Library of Congress Subject Headings (LCSH): | Drug resistance in microorganisms, Peptide antibiotics, Machine learning, Peptide antibiotics -- Data processing | ||||||||
| Journal or Publication Title: | IEEE/ACM Transactions on Computational Biology and Bioinformatics | ||||||||
| Publisher: | IEEE | ||||||||
| ISSN: | 1545-5963 | ||||||||
| Official Date: | January 2022 | ||||||||
| Dates: |
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| Volume: | 19 | ||||||||
| Number: | 1 | ||||||||
| Page Range: | pp. 275-283 | ||||||||
| DOI: | 10.1109/TCBB.2020.2999399 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Reuse Statement (publisher, data, author rights): | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| RIOXX Funder/Project Grant: |
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