MotivationBacterial 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.ResultsIn 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 implementationModels 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.

These results illuminate a broader phenotypic spectrum associated with CARD11 mutations in human subjects and underscore the need for functional studies to demonstrate that rare gene variants encountered in expected and unexpected phenotypes must nonetheless be validated for pathogenic activity.