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Deep learning improves antimicrobial peptide recognition.

ABSTRACT: Motivation:Bacterial resistance to antibiotics is a growing concern. Antimicrobial peptides (AMPs), natural components of innate immunity, are popular targets for developing new drugs. Machine learning methods are now commonly adopted by wet-laboratory researchers to screen for promising candidates. Results:In this work, we utilize deep learning to recognize antimicrobial activity. We propose a neural network model with convolutional and recurrent layers that leverage primary sequence composition. Results show that the proposed model outperforms state-of-the-art classification models on a comprehensive dataset. By utilizing the embedding weights, we also present a reduced-alphabet representation and show that reasonable AMP recognition can be maintained using nine amino acid types. Availability and implementation:Models and datasets are made freely available through the Antimicrobial Peptide Scanner vr.2 web server at www.ampscanner.com. Supplementary information:Supplementary data are available at Bioinformatics online.

SUBMITTER: Veltri D

PROVIDER: S-EPMC6084614 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Deep learning improves antimicrobial peptide recognition.

Veltri Daniel D Kamath Uday U Shehu Amarda A

Bioinformatics (Oxford, England) 20180801 16

<h4>Motivation</h4>Bacterial resistance to antibiotics is a growing concern. Antimicrobial peptides (AMPs), natural components of innate immunity, are popular targets for developing new drugs. Machine learning methods are now commonly adopted by wet-laboratory researchers to screen for promising candidates.<h4>Results</h4>In this work, we utilize deep learning to recognize antimicrobial activity. We propose a neural network model with convolutional and recurrent layers that leverage primary sequ ...[more]

PMID: 29590297

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