Identification of Peptide Inhibitors of Enveloped Viruses Using Support Vector Machine
Document Type
Article
Publication Date
1-1-2015
Abstract
The peptides derived from envelope proteins have been shown to inhibit the protein-protein interactions in the virus membrane fusion process and thus have a great potential to be developed into effective antiviral therapies. There are three types of envelope proteins each exhibiting distinct structure folds. Although the exact fusion mechanism remains elusive, it was suggested that the three classes of viral fusion proteins share a similar mechanism of membrane fusion. The common mechanism of action makes it possible to correlate the properties of self-derived peptide inhibitors with their activities. Here we developed a support vector machine model using sequence-based statistical scores of self-derived peptide inhibitors as input features to correlate with their activities. The model displayed 92% prediction accuracy with the Matthew's correlation coefficient of 0.84, obviously superior to those using physicochemical properties and amino acid decomposition as input. The predictive support vector machine model for self-derived peptides of envelope proteins would be useful in development of antiviral peptide inhibitors targeting the virus fusion process.
Keywords
Protein-protein interactions, Membrane-fusion proteins, Type-1 glycoprotein-H, Antimicrobial peptides, Synthetic peptides, Antiviral peptides, Antibacterial peptides, Structure selection, Spike protein, Hiv-1 Gp41
Divisions
fac_med,CHEMISTRY
Publication Title
PLoS ONE
Volume
10
Issue
12
Publisher
Public Library of Science