AMP 0 : Species-Specific Prediction of Anti-microbial Peptides Using Zero and Few Shot Learning

• Published in: IEEE/ACM transactions on computational biology and bioinformatics 2022-01, Vol.19 (1), p.275-283
• Main Authors: Gull, Sadaf, Minhas, Fayyaz
• Format: Article
• Language: English
• Subjects: Amino Acid Sequence; Anti-Bacterial Agents; Machine Learning; Peptides
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2020.2999399

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 AMP . 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. AMP webserver is available at http://ampzero.pythonanywhere.com.