ClassAMP: A Prediction Tool for Classification of Antimicrobial Peptides

Author

Joseph, Shaini ; Karnik, Shreyas ; Nilawe, Pravin ; Jayaraman, V.K. ; Idicula-Thomas, Susan

Author_Institution

Biomed. Inf. Center of Indian Council of Med. Res., Nat. Inst. for Res. in Reproductive Health, Mumbai, India

Volume

9

Issue

5

fYear

2012

Firstpage

1535

Lastpage

1538

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/.

Keywords

antibacterial activity; biology computing; drugs; molecular biophysics; proteins; random sequences; support vector machines; ClassAMP; SVM; antibacterial activity; antifungal activity; antiinfective agents; antimicrobial peptide classification; antiviral activity; drug discovery programs; protein sequence; random forests; sequence features; support vector machines; Anti-bacterial; Anti-fungal; Peptides; Predictive models; Radio frequency; Support vector machines; Training; Antibacterial; SVM.; antifungal; antimicrobial; antiviral; prediction algorithm; random forests; Algorithms; Anti-Infective Agents; Peptides; Support Vector Machines;

fLanguage

English

Journal_Title

Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Publisher

ieee

ISSN

1545-5963

Type

jour

DOI

10.1109/TCBB.2012.89

Filename

6226353