Deep learning improves antimicrobial peptide recognition

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.

# Antimicrobial Peptides and Activities # Immunology and Microbiology # Machine Learning in Bioinformatics # Biochemistry, Genetics and Molecular Biology # Computational Drug Discovery Methods # Computer Science

Daniel Veltri, Uday Kamath, Amarda Shehu (2018). "Deep learning improves antimicrobial peptide recognition". Bioinformatics. https://doi.org/10.1093/bioinformatics/bty179