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Prediction of protein interaction hot spots using rough set-based multiple criteria linear programming
Chen, Ruoying1,2; Zhang, Zhiwang3; Wu, Di4; Zhang, Peng5; Zhang, Xinyang1; Wang, Yong6; Shi, Yong1,7
2011-01-21
Source PublicationJOURNAL OF THEORETICAL BIOLOGY
ISSN0022-5193
Volume269Issue:1Pages:174-180
AbstractProtein-protein interactions are fundamentally important in many biological processes and it is in pressing need to understand the principles of protein-protein interactions. Mutagenesis studies have found that only a small fraction of surface residues, known as hot spots, are responsible for the physical binding in protein complexes. However, revealing hot spots by mutagenesis experiments are usually time consuming and expensive. In order to complement the experimental efforts, we propose a new computational approach in this paper to predict hot spots. Our method, Rough Set-based Multiple Criteria Linear Programming (RS-MCLP), integrates rough sets theory and multiple criteria linear programming to choose dominant features and computationally predict hot spots. Our approach is benchmarked by a dataset of 904 alanine-mutated residues and the results show that our RS-MCLP method performs better than other methods, e.g., MCLP, Decision Tree, Bayes Net, and the existing HotSprint database. In addition, we reveal several biological insights based on our analysis. We find that four features (the change of accessible surface area, percentage of the change of accessible surface area, size of a residue, and atomic contacts) are critical in predicting hot spots. Furthermore, we find that three residues (Tyr, Trp, and Phe) are abundant in hot spots through analyzing the distribution of amino acids. (C) 2010 Elsevier Ltd. All rights reserved.
KeywordBinding free energy Alanine mutation Residue's features Combined model Predictive performance
DOI10.1016/j.jtbi.2010.10.021
Language英语
Funding ProjectNational Natural Science Foundation of China[70531040] ; National Natural Science Foundation of China[70921061] ; National Natural Science Foundation of China[70621001] ; Overseas Collaboration Group of Chinese Academy of Sciences ; Chinese Ministry of Science and Technology[2004CB720103] ; BHP Billiton Cooperation of Australia
WOS Research AreaLife Sciences & Biomedicine - Other Topics ; Mathematical & Computational Biology
WOS SubjectBiology ; Mathematical & Computational Biology
WOS IDWOS:000286173000018
PublisherACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
Citation statistics
Cited Times:12[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/12388
Collection应用数学研究所
Affiliation1.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.Ludong Univ, Coll Informat Sci & Engn, Yantai 264025, Shandong, Peoples R China
4.Tongji Univ, Dept Biomed Engn, Coll Life Sci & Technol, Shanghai 200092, Peoples R China
5.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
6.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
7.Univ Nebraska, Coll Informat Sci & Technol, Omaha, NE 68182 USA
Recommended Citation
GB/T 7714
Chen, Ruoying,Zhang, Zhiwang,Wu, Di,et al. Prediction of protein interaction hot spots using rough set-based multiple criteria linear programming[J]. JOURNAL OF THEORETICAL BIOLOGY,2011,269(1):174-180.
APA Chen, Ruoying.,Zhang, Zhiwang.,Wu, Di.,Zhang, Peng.,Zhang, Xinyang.,...&Shi, Yong.(2011).Prediction of protein interaction hot spots using rough set-based multiple criteria linear programming.JOURNAL OF THEORETICAL BIOLOGY,269(1),174-180.
MLA Chen, Ruoying,et al."Prediction of protein interaction hot spots using rough set-based multiple criteria linear programming".JOURNAL OF THEORETICAL BIOLOGY 269.1(2011):174-180.
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