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/. © 2004-2012 IEEE.

Valadi Krishnamoorthy Jayaraman

Center for Informatics

S. Joseph

S. Karnik

P. Nilawe

S. Idicula-Thomas

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Shiv Nadar University

ClassAMP: A Prediction Tool for Classification of Antimicrobial Peptides

IEEE/ACM Transactions on Computational Biology and Bioinformatics

ClassAMP: A prediction tool for classification of antimicrobial peptides