title: An evolution-based DNA-binding residue predictor using a dynamic query-driven learning scheme.
abstract: DNA-binding proteins play a pivotal role in various biological activities. Identification of DNA-binding residues (DBRs) is of great importance for understanding the mechanism of gene regulations and chromatin remodeling. Most traditional computational methods usually construct their predictors on static non-redundant datasets. They excluded many homologous DNA-binding proteins so as to guarantee the generalization capability of their models. However, those ignored samples may potentially provide useful clues when studying protein-DNA interactions, which have not obtained enough attention. In view of this, we propose a novel method, namely DQPred-DBR, to fill the gap of DBR predictions. First, a large-scale extensible sample pool was compiled. Second, evolution-based features in the form of a relative position specific score matrix and covariant evolutionary conservation descriptors were used to encode the feature space. Third, a dynamic query-driven learning scheme was designed to make more use of proteins with known structure and functions. In comparison with a traditional static model, the introduction of dynamic models could obviously improve the prediction performance. Experimental results from the benchmark and independent datasets proved that our DQPred-DBR had promising generalization capability. It was capable of producing decent predictions and outperforms many state-of-the-art methods. For the convenience of academic use, our proposed method was also implemented as a web server at .
authors.affiliation: School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, P. R. China. guifuyang.nenu@gmail.com.
authors.forename: H
authors.initials: H
authors.lastname: Chai
authors.affiliation: School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, P. R. China. guifuyang.nenu@gmail.com.
authors.forename: J
authors.initials: J
authors.lastname: Zhang
authors.affiliation: School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, P. R. China. guifuyang.nenu@gmail.com and Office of Informatization Management and Planning, Northeast Normal University, Changchun, 130117, P. R. China.
authors.forename: G
authors.initials: G
authors.lastname: Yang
authors.affiliation: School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, P. R. China. guifuyang.nenu@gmail.com.
authors.forename: Z
authors.initials: Z
authors.lastname: Ma
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D004268:DNA-Binding Proteins / Q000737:chemistry*; D019143:Evolution, Molecular; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D004268:DNA-Binding Proteins