title: Enhancing protein-vitamin binding residues prediction by multiple heterogeneous subspace SVMs ensemble.
abstract: BACKGROUND
  Vitamins are typical ligands that play critical roles in various metabolic processes. The accurate identification of the vitamin-binding residues solely based on a protein sequence is of significant importance for the functional annotation of proteins, especially in the post-genomic era, when large volumes of protein sequences are accumulating quickly without being functionally annotated.
  
  
  RESULTS
  In this paper, a new predictor called TargetVita is designed and implemented for predicting protein-vitamin binding residues using protein sequences. In TargetVita, features derived from the position-specific scoring matrix (PSSM), predicted protein secondary structure, and vitamin binding propensity are combined to form the original feature space; then, several feature subspaces are selected by performing different feature selection methods. Finally, based on the selected feature subspaces, heterogeneous SVMs are trained and then ensembled for performing prediction.
  
  
  CONCLUSIONS
  The experimental results obtained with four separate vitamin-binding benchmark datasets demonstrate that the proposed TargetVita is superior to the state-of-the-art vitamin-specific predictor, and an average improvement of 10% in terms of the Matthews correlation coefficient (MCC) was achieved over independent validation tests. The TargetVita web server and the datasets used are freely available for academic use at http://csbio.njust.edu.cn/bioinf/TargetVita or http://www.csbio.sjtu.edu.cn/bioinf/TargetVita.
authors.affiliation: School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing 210094, China. njyudj@njust.edu.cn.
authors.forename: Dong-Jun
authors.initials: DJ
authors.lastname: Yu
authors.forename: Jun
authors.initials: J
authors.lastname: Hu
authors.forename: Hui
authors.initials: H
authors.lastname: Yan
authors.forename: Xi-Bei
authors.initials: XB
authors.lastname: Yang
authors.forename: Jing-Yu
authors.initials: JY
authors.lastname: Yang
authors.forename: Hong-Bin
authors.initials: HB
authors.lastname: Shen
mesh_terms: D001665:Binding Sites; D008954:Models, Biological; D056510:Position-Specific Scoring Matrices; D011485:Protein Binding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*; D012984:Software; D060388:Support Vector Machine; D014815:Vitamins / Q000378:metabolism*
chemical_list: D011506:Proteins; D014815:Vitamins