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

文献类型：期刊论文

作者Chen, Ruoying1,2; Chen, Wenjing1; Yang, Sixiao1; Wu, Di3; Wang, Yong4; Tian, Yingjie1; Shi, Yong1,5
刊名BMC BIOINFORMATICS
出版日期2011-07-29
卷号12页码:14
ISSN号1471-2105
DOI10.1186/1471-2105-12-311
英文摘要Background: Systematic 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. Results: In 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. Conclusion: Experimental 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.
资助项目National Natural Science Foundation of China[70921061] ; National Natural Science Foundation of China[10601064] ; National Natural Science Foundation of China[70531040] ; National Natural Science Foundation of China[10801131] ; Chinese Ministry of Science and Technology[2004CB720103] ; Overseas Collaboration Group of Chinese Academy of Sciences ; BHP Billiton Cooperation of Australia ; GUCAS
WOS研究方向Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
语种英语
WOS记录号WOS:000295224200001
出版者BIOMED CENTRAL LTD
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/187]  
专题应用数学研究所
通讯作者Tian, Yingjie
作者单位1.Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Grad Univ, Coll Life Sci, Beijing 100049, Peoples R China
3.Tongji Univ, Coll Life Sci & Technol, Dept Biomed Engn, Shanghai 200092, Peoples R China
4.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
5.Univ Nebraska, Coll Informat Sci & Technol, Omaha, NE 68182 USA
推荐引用方式
GB/T 7714		 Chen, Ruoying,Chen, Wenjing,Yang, Sixiao,et al. Rigorous assessment and integration of the sequence and structure based features to predict hot spots[J]. BMC BIOINFORMATICS,2011,12:14.
APA Chen, Ruoying.,Chen, Wenjing.,Yang, Sixiao.,Wu, Di.,Wang, Yong.,...&Shi, Yong.(2011).Rigorous assessment and integration of the sequence and structure based features to predict hot spots.BMC BIOINFORMATICS,12,14.
MLA Chen, Ruoying,et al."Rigorous assessment and integration of the sequence and structure based features to predict hot spots".BMC BIOINFORMATICS 12(2011):14.

入库方式： OAI收割

来源：数学与系统科学研究院

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