Daniel Veltri, Uday Kamath, Amarda Shehu; Deep Learning Improves Antimicrobial Peptide Recognition, Bioinformatics, , bty179, https://doi.org/10.1093/bioinformatics/bty179 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.
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 data set. 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.
Models and data sets are made freely available through the Antimicrobial Peptide Scanner vr.2 web server at: www.ampscanner.com. firstname.lastname@example.org for general inquiries and email@example.com for web server information.
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