title: Identification of Peptide Inhibitors of Enveloped Viruses Using Support Vector Machine.
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.
authors.affiliation: School of Chemistry and Chemical Engineering, Queen's University Belfast, David Keir Building, Stranmillis Road, Belfast, Northern Ireland, United Kingdom.|School of Basic Medical Sciences, Xinxiang Medical University, Xinxiang, Henan, China.
authors.forename: Yongtao
authors.initials: Y
authors.lastname: Xu
authors.affiliation: School of Chemistry and Chemical Engineering, Queen's University Belfast, David Keir Building, Stranmillis Road, Belfast, Northern Ireland, United Kingdom.
authors.forename: Shui
authors.initials: S
authors.lastname: Yu
authors.affiliation: School of Biotechnology and Chemical Engineering, Ningbo Institute of Technology, Zhejiang University, Ningbo, China.
authors.forename: Jian-Wei
authors.initials: JW
authors.lastname: Zou
authors.affiliation: School of Biotechnology and Chemical Engineering, Ningbo Institute of Technology, Zhejiang University, Ningbo, China.
authors.forename: Guixiang
authors.initials: G
authors.lastname: Hu
authors.affiliation: Department of Chemistry, Faculty of Sciences, University of Malaya, Kuala Lumpur, Malaysia.|Drug Design & Development Research Group, University of Malaya, Kuala Lumpur, Malaysia.
authors.forename: Noorsaadah A B D
authors.initials: NA
authors.lastname: Rahman
authors.affiliation: Drug Design & Development Research Group, University of Malaya, Kuala Lumpur, Malaysia.|Department of Pharmacy, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.
authors.forename: Rozana Binti
authors.initials: RB
authors.lastname: Othman
authors.affiliation: State Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing, China.
authors.forename: Xia
authors.initials: X
authors.lastname: Tao
authors.affiliation: School of Chemistry and Chemical Engineering, Queen's University Belfast, David Keir Building, Stranmillis Road, Belfast, Northern Ireland, United Kingdom.
authors.forename: Meilan
authors.initials: M
authors.lastname: Huang
mesh_terms: D010446:Peptide Fragments / Q000737:chemistry* / Q000494:pharmacology; D011485:Protein Binding; D020539:Sequence Analysis, Protein / Q000379:methods; D060388:Support Vector Machine; D014759:Viral Envelope Proteins / Q000037:antagonists & inhibitors* / Q000737:chemistry / Q000378:metabolism; D065147:Viral Fusion Protein Inhibitors / Q000737:chemistry* / Q000494:pharmacology
chemical_list: D010446:Peptide Fragments; D014759:Viral Envelope Proteins; D065147:Viral Fusion Protein Inhibitors