APIS: accurate prediction of hot spots in protein interfaces by combining protrusion index with solvent accessibility

BMC Bioinformatics

Research Article

Jiangning Song¹  Xing-Ming Zhao²  De-Shuang Huang³  Jun-Feng Xia⁴ 

[1] Bioinformatics Center, Institute for Chemical Research, Kyoto University, 611-0011, Uji, Kyoto, Japan;Department of Biochemistry and Molecular Biology, Monash University, 3800, Melbourne, VIC, Australia;Institute of Systems Biology, Shanghai University, 200444, Shanghai, China;Intelligent Computing Laboratory, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, P.O. Box 1130, 230031, Hefei, Anhui, China;Intelligent Computing Laboratory, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, P.O. Box 1130, 230031, Hefei, Anhui, China;School of Life Science, University of Science and Technology of China, 230027, Hefei, Anhui, China;

关键词: Support Vector Machine;    Support Vector Machine Model;    Solvent Accessibility;    Accessible Surface Area;    Interface Residue;   

DOI  :  10.1186/1471-2105-11-174

 received in 2009-12-21, accepted in 2010-04-08,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundIt is well known that most of the binding free energy of protein interaction is contributed by a few key hot spot residues. These residues are crucial for understanding the function of proteins and studying their interactions. Experimental hot spots detection methods such as alanine scanning mutagenesis are not applicable on a large scale since they are time consuming and expensive. Therefore, reliable and efficient computational methods for identifying hot spots are greatly desired and urgently required.ResultsIn this work, we introduce an efficient approach that uses support vector machine (SVM) to predict hot spot residues in protein interfaces. We systematically investigate a wide variety of 62 features from a combination of protein sequence and structure information. Then, to remove redundant and irrelevant features and improve the prediction performance, feature selection is employed using the F-score method. Based on the selected features, nine individual-feature based predictors are developed to identify hot spots using SVMs. Furthermore, a new ensemble classifier, namely APIS (A combined model based on Protrusion Index and Solvent accessibility), is developed to further improve the prediction accuracy. The results on two benchmark datasets, ASEdb and BID, show that this proposed method yields significantly better prediction accuracy than those previously published in the literature. In addition, we also demonstrate the predictive power of our proposed method by modelling two protein complexes: the calmodulin/myosin light chain kinase complex and the heat shock locus gene products U and V complex, which indicate that our method can identify more hot spots in these two complexes compared with other state-of-the-art methods.ConclusionWe have developed an accurate prediction model for hot spot residues, given the structure of a protein complex. A major contribution of this study is to propose several new features based on the protrusion index of amino acid residues, which has been shown to significantly improve the prediction performance of hot spots. Moreover, we identify a compact and useful feature subset that has an important implication for identifying hot spot residues. Our results indicate that these features are more effective than the conventional evolutionary conservation, pairwise residue potentials and other traditional features considered previously, and that the combination of our and traditional features may support the creation of a discriminative feature set for efficient prediction of hot spot residues. The data and source code are available on web site http://home.ustc.edu.cn/~jfxia/hotspot.html.

【 授权许可】

Unknown   
© Xia et al; licensee BioMed Central Ltd. 2010. 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.