Rigorous assessment and integration of the sequence and structure based features to predict hot spots

BMC Bioinformatics

Research Article

Yong Wang¹  Ruoying Chen²  Di Wu³  Sixiao Yang⁴  Wenjing Chen⁴  Yingjie Tian⁴  Yong Shi⁵ 

[1] Academy of Mathematics and Systems Science, Chinese Academy of Sciences, 100190, Beijing, China;College of Life Sciences, Graduate University of Chinese Academy of Sciences, 100049, Beijing, China;Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, 100190, Beijing, China;Department of Biomedical Engineering, College Life Science and Technology, Tongji University, 200092, Shanghai, China;Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, 100190, Beijing, China;Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, 100190, Beijing, China;College of Information Science and Technology, University of Nebraska at Omaha, 68182, Omaha, NE, USA;

关键词: Salt Bridge;    Accessible Surface Area;    Conservation Score;    Interface Residue;    Position Specific Score Matrix;   

DOI  :  10.1186/1471-2105-12-311

 received in 2010-11-03, accepted in 2011-07-29,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundSystematic mutagenesis studies have shown that only a few interface residues termed hot spots contribute significantly to the binding free energy of protein-protein interactions. Therefore, hot spots prediction becomes increasingly important for well understanding the essence of proteins interactions and helping narrow down the search space for drug design. Currently many computational methods have been developed by proposing different features. However comparative assessment of these features and furthermore effective and accurate methods are still in pressing need.ResultsIn this study, we first comprehensively collect the features to discriminate hot spots and non-hot spots and analyze their distributions. We find that hot spots have lower relASA and larger relative change in ASA, suggesting hot spots tend to be protected from bulk solvent. In addition, hot spots have more contacts including hydrogen bonds, salt bridges, and atomic contacts, which favor complexes formation. Interestingly, we find that conservation score and sequence entropy are not significantly different between hot spots and non-hot spots in Ab+ dataset (all complexes). While in Ab- dataset (antigen-antibody complexes are excluded), there are significant differences in two features between hot pots and non-hot spots. Secondly, we explore the predictive ability for each feature and the combinations of features by support vector machines (SVMs). The results indicate that sequence-based feature outperforms other combinations of features with reasonable accuracy, with a precision of 0.69, a recall of 0.68, an F1 score of 0.68, and an AUC of 0.68 on independent test set. Compared with other machine learning methods and two energy-based approaches, our approach achieves the best performance. Moreover, we demonstrate the applicability of our method to predict hot spots of two protein complexes.ConclusionExperimental results show that support vector machine classifiers are quite effective in predicting hot spots based on sequence features. Hot spots cannot be fully predicted through simple analysis based on physicochemical characteristics, but there is reason to believe that integration of features and machine learning methods can remarkably improve the predictive performance for hot spots.

【 授权许可】

Unknown   
© Chen 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.