title: Sequence-based prediction of protein-peptide binding sites using support vector machine.
abstract: Protein-peptide interactions are essential for all cellular processes including DNA repair, replication, gene-expression, and metabolism. As most protein-peptide interactions are uncharacterized, it is cost effective to investigate them computationally as the first step. All existing approaches for predicting protein-peptide binding sites, however, are based on protein structures despite the fact that the structures for most proteins are not yet solved. This article proposes the first machine-learning method called SPRINT to make Sequence-based prediction of Protein-peptide Residue-level Interactions. SPRINT yields a robust and consistent performance for 10-fold cross validations and independent test. The most important feature is evolution-generated sequence profiles. For the test set (1056 binding and non-binding residues), it yields a Matthews' Correlation Coefficient of 0.326 with a sensitivity of 64% and a specificity of 68%. This sequence-based technique shows comparable or more accurate than structure-based methods for peptide-binding site prediction. SPRINT is available as an online server at: http://sparks-lab.org/. © 2016 Wiley Periodicals, Inc.
authors.affiliation: School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, Queensland, 4215, Australia.
authors.forename: Ghazaleh
authors.initials: G
authors.lastname: Taherzadeh
authors.affiliation: School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, Queensland, 4215, Australia.|Institute for Glycomics, Griffith University, Parklands Dr, Southport, Queensland, 4215, Australia.
authors.forename: Yuedong
authors.initials: Y
authors.lastname: Yang
authors.affiliation: Weill Cornell Medical College, 1300 York Avenue, New York, New York, 10065.
authors.forename: Tuo
authors.initials: T
authors.lastname: Zhang
authors.affiliation: School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, Queensland, 4215, Australia.
authors.forename: Alan Wee-Chung
authors.initials: AW
authors.lastname: Liew
authors.affiliation: School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, Queensland, 4215, Australia.|Institute for Glycomics, Griffith University, Parklands Dr, Southport, Queensland, 4215, Australia.
authors.forename: Yaoqi
authors.initials: Y
authors.lastname: Zhou
mesh_terms: D001665:Binding Sites; D010455:Peptides / Q000737:chemistry* / Q000378:metabolism*; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D060388:Support Vector Machine
chemical_list: D010455:Peptides; D011506:Proteins
keywords: binding site; features; machine learning; prediction; protein-peptide; sequence-based; support vector machine