Title :
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
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;
Journal_Title :
Computational Biology and Bioinformatics, IEEE/ACM Transactions on
DOI :
10.1109/TCBB.2012.89
