Improving the prediction of protein-nucleic acids binding residues via multiple sequence profiles and the consensus of complementary methods.

MOTIVATION

The interactions between protein and nucleic acids play a key role in various biological processes. Accurate recognition of the residues that bind nucleic acids can facilitate the study of uncharacterized protein-nucleic acids interactions. The accuracy of existing nucleic acids-binding residues prediction methods is relatively low.

RESULTS

In this work, we introduce NucBind, a novel method for the prediction of nucleic acids-binding residues. NucBind combines the predictions from a support vector machine-based ab-initio method SVMnuc and a template-based method COACH-D. SVMnuc was trained with features from three complementary sequence profiles. COACH-D predicts the binding residues based on homologous templates identified from a nucleic acids-binding library. The proposed methods were assessed and compared with other peering methods on three benchmark datasets. Experimental results show that NucBind consistently outperforms other state-of-the-art methods. Though with higher accuracy, similar to many other ab-initio methods, cross prediction between DNA and RNA-binding residues was also observed in SVMnuc and NucBind. We attribute the success of NucBind to two folds. The first is the utilization of improved features extracted from three complementary sequence profiles in SVMnuc. The second is the combination of two complementary methods: the ab-initio method SVMnuc and the template-based method COACH-D.

AVAILABILITY AND IMPLEMENTATION

http://yanglab.nankai.edu.cn/NucBind.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.