ClassAMP: a prediction tool for classification of antimicrobial peptides.
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/.
Biomedical Informatics Center of Indian Council of Medical Research, National Institute for Research in Reproductive Health, Parel, Mumbai, Maharashtra, India. shainimarina31@gmail.com
Shaini
S
Joseph
Shreyas
S
Karnik
Pravin
P
Nilawe
V K
VK
Jayaraman
Susan
S
Idicula-Thomas
D000465:Algorithms; D000890:Anti-Infective Agents / Q000737:chemistry* / Q000145:classification*; D010455:Peptides / Q000737:chemistry* / Q000145:classification; D060388:Support Vector Machine
D000890:Anti-Infective Agents; D010455:Peptides