Identification of Peptide Inhibitors of Enveloped Viruses Using Support Vector Machine

Xu, Y. and Yu, S. and Zou, J.W. and Hu, G. and Rahman, N.A. and Othman, R. and Tao, X. and Huang, M. (2015) Identification of Peptide Inhibitors of Enveloped Viruses Using Support Vector Machine. PLoS ONE, 10 (12). ISSN 1932-6203, DOI

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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.

Item Type: Article
Uncontrolled 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
Subjects: Q Science > Q Science (General)
T Technology > T Technology (General)
Divisions: Faculty of Medicine
Faculty of Science > Department of Chemistry
Depositing User: Mrs. Siti Mawarni Salim
Date Deposited: 12 Aug 2016 04:02
Last Modified: 05 Jul 2017 08:41

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