An evolution-based DNA-binding residue predictor using a dynamic query-driven learning scheme.
Mol Biosyst
; 12(12): 3643-3650, 2016 11 15.
Article
en En
| MEDLINE
| ID: mdl-27730230
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 .
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Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Sitios de Unión
/
Biología Computacional
/
Proteínas de Unión al ADN
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Mol Biosyst
Asunto de la revista:
BIOLOGIA MOLECULAR
/
BIOQUIMICA
Año:
2016
Tipo del documento:
Article