ClassAMP: A Prediction Tool for Classification of Antimicrobial Peptides

Authors:
Shaini Joseph;Shreyas Karnik;Pravin Nilawe;V. K. Jayaraman;Susan Idicula-Thomas
Affiliations:
National Institute for Research in Reproductive Health, Mumbai;Indiana University, Indianapolis;National Institute for Research in Reproductive Health, Mumbai;Pune University Campus, Pune;National Institute for Research in Reproductive Health, Mumbai
Venue:
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Year:
2012

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Abstract

Antimicrobial peptides (AMPs) are gaining popularity as anti-infective agents. Information on sequence features that contribute to target specificity of AMPs will aid in accelerating drug discovery programs involving them. In this study, an algorithm called ClassAMP using Random Forests (RFs) and Support Vector Machines (SVMs) has been developed to predict the propensity of a protein sequence to have antibacterial, antifungal, or antiviral activity. ClassAMP is available at http://www.bicnirrh.res.in/classamp/.