Identification of Peptide Inhibitors of Enveloped Viruses Using Support Vector Machine

Yongtao Xu, Shui Yu, Jian-Wei Zou, Guixiang Hu, Noorsaadah A. B. D. Rahman , Rozana Binti Othman, Xia Tao , Meilan Huang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
319 Downloads (Pure)


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.
Original languageEnglish
Article numbere0144171
Number of pages15
JournalPLoS ONE
Issue number11
Publication statusPublished - 04 Dec 2015


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