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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 .

journal_name

Mol Biosyst

journal_title

Molecular bioSystems

authors

Chai H,Zhang J,Yang G,Ma Z

doi

10.1039/c6mb00626d

subject

Has Abstract

pub_date

2016-11-15 00:00:00

pages

3643-3650

issue

12

eissn

1742-206X

issn

1742-2051

journal_volume

12

pub_type

杂志文章

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