Predicting protein-protein interface residues using local surface structural similarity

被引:69
作者
Jordan, Rafael A. [1 ,4 ]
EL-Manzalawy, Yasser [1 ,5 ]
Dobbs, Drena [2 ,3 ]
Honavar, Vasant [1 ,3 ]
机构
[1] Iowa State Univ, Dept Comp Sci, Ames, IA 50011 USA
[2] Iowa State Univ, Dept Genet Dev & Cell Biol, Ames, IA 50011 USA
[3] Iowa State Univ, Bioinformat & Computat Biol Program, Ames, IA 50011 USA
[4] Pontificia Univ Javeriana, Dept Syst & Comp Engn, Cali, Colombia
[5] Al Azhar Univ, Dept Syst & Comp Engn, Cairo, Egypt
来源
BMC BIOINFORMATICS | 2012年 / 13卷
基金
美国国家卫生研究院; 美国国家科学基金会;
关键词
Local surface structural similarity based methods offer a simple; efficient; effective approach to predict protein-protein interface residues; BINDING-SITE PREDICTION; WEB SERVER; COMPLEXES; IDENTIFICATION; CONSERVATION; CLASSIFIER; IDENTIFY; NETWORK; MAP;
D O I
10.1186/1471-2105-13-41
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Background: Identification of the residues in protein-protein interaction sites has a significant impact in problems such as drug discovery. Motivated by the observation that the set of interface residues of a protein tend to be conserved even among remote structural homologs, we introduce PrISE, a family of local structural similarity-based computational methods for predicting protein-protein interface residues. Results: We present a novel representation of the surface residues of a protein in the form of structural elements. Each structural element consists of a central residue and its surface neighbors. The PrISE family of interface prediction methods uses a representation of structural elements that captures the atomic composition and accessible surface area of the residues that make up each structural element. Each of the members of the PrISE methods identifies for each structural element in the query protein, a collection of similar structural elements in its repository of structural elements and weights them according to their similarity with the structural element of the query protein. PrISEL relies on the similarity between structural elements (i.e. local structural similarity). PrISEG relies on the similarity between protein surfaces (i.e. general structural similarity). PrISEC, combines local structural similarity and general structural similarity to predict interface residues. These predictors label the central residue of a structural element in a query protein as an interface residue if a weighted majority of the structural elements that are similar to it are interface residues, and as a non-interface residue otherwise. The results of our experiments using three representative benchmark datasets show that the PrISEC outperforms PrISEL and PrISEG; and that PrISEC is highly competitive with state-of-the-art structure-based methods for predicting protein-protein interface residues. Our comparison of PrISEC with PredUs, a recently developed method for predicting interface residues of a query protein based on the known interface residues of its (global) structural homologs, shows that performance superior or comparable to that of PredUs can be obtained using only local surface structural similarity. PrISEC is available as a Web server at http://prise.cs.iastate.edu/
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页数:14
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