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- Title
Enhancing protein-vitamin binding residues prediction by multiple heterogeneous subspace SVMs ensemble.
- Authors
Dong-Jun Yu; Jun Hu; Hui Yan; Xi-Bei Yang; Jing-Yu Yang; Hong-Bin Shen
- 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.
- Publication
BMC Bioinformatics, 2014, Vol 15, Issue 1, p1
- ISSN
1471-2105
- Publication type
Article
- DOI
10.1186/1471-2105-15-297