期刊
BMC BIOINFORMATICS
卷 15, 期 -, 页码 -出版社
BIOMED CENTRAL LTD
DOI: 10.1186/1471-2105-15-297
关键词
Protein-vitamin binding residue; Feature subspace; Heterogeneous SVM; Classifier ensemble
类别
资金
- National Natural Science Foundation of China [61373062, 61175024, 61222306, 61100116, 61233011]
- Natural Science Foundation of Jiangsu [BK20141403]
- China Postdoctoral Science Foundation [2013M530260, 2014T70526]
- The Six Top Talents of Jiangsu Province [2013-XXRJ-022]
- Fundamental Research Funds for the Central Universities [30920130111010]
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.
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