Improving the prediction of protein binding sites by combining heterogeneous data and Voronoi diagrams

BMC Bioinformatics

Methodology Article

Narcis Fernandez-Fuentes¹  Joan Segura¹  Pamela F Jones² 

[1] Leeds Institute of Molecular Medicine, Section of Experimental Therapeutics, University of Leeds, LS9 7TF, Leeds, UK;Leeds Institute of Molecular Medicine, Section of Molecular Gastroenterology, University of Leeds, LS9 7TF, Leeds, UK;

关键词: Random Forest;    Voronoi Diagram;    Protein Binding Site;    Ensemble Classifier;    Matthews Correlation Coefficient;   

DOI  :  10.1186/1471-2105-12-352

 received in 2011-05-10, accepted in 2011-08-23,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundProtein binding site prediction by computational means can yield valuable information that complements and guides experimental approaches to determine the structure of protein complexes. Predictions become even more relevant and timely given the current resolution of protein interaction maps, where there is a very large and still expanding gap between the available information on: (i) which proteins interact and (ii) how proteins interact. Proteins interact through exposed residues that present differential physicochemical properties, and these can be exploited to identify protein interfaces.ResultsHere we present VORFFIP, a novel method for protein binding site prediction. The method makes use of broad set of heterogeneous data and defined of residue environment, by means of Voronoi Diagrams that are integrated by a two-steps Random Forest ensemble classifier. Four sets of residue features (structural, energy terms, sequence conservation, and crystallographic B-factors) used in different combinations together with three definitions of residue environment (Voronoi Diagrams, sequence sliding window, and Euclidian distance) have been analyzed in order to maximize the performance of the method.ConclusionsThe integration of different forms information such as structural features, energy term, evolutionary conservation and crystallographic B-factors, improves the performance of binding site prediction. Including the information of neighbouring residues also improves the prediction of protein interfaces. Among the different approaches that can be used to define the environment of exposed residues, Voronoi Diagrams provide the most accurate description. Finally, VORFFIP compares favourably to other methods reported in the recent literature.

【 授权许可】

Unknown   
© Segura et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.