中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
Prediction of protein interaction hot spots using rough set-based multiple criteria linear programming

文献类型：期刊论文

作者Chen, Ruoying2,3; Zhang, Zhiwang4; Wu, Di5; Zhang, Peng6; Zhang, Xinyang2; Wang, Yong7; Shi, Yong1,2
刊名JOURNAL OF THEORETICAL BIOLOGY
出版日期2011-01-21
卷号269期号:1页码:174-180
关键词Binding free energy Alanine mutation Residue's features Combined model Predictive performance
ISSN号0022-5193
DOI10.1016/j.jtbi.2010.10.021
英文摘要Protein-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.
资助项目National 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研究方向Life Sciences & Biomedicine - Other Topics ; Mathematical & Computational Biology
语种英语
WOS记录号WOS:000286173000018
出版者ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
源URL[http://119.78.100.204/handle/2XEOYT63/12956]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Shi, Yong
作者单位1.Univ Nebraska, Coll Informat Sci & Technol, Omaha, NE 68182 USA
2.Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Grad Univ, Coll Life Sci, Beijing 100049, Peoples R China
4.Ludong Univ, Coll Informat Sci & Engn, Yantai 264025, Shandong, Peoples R China
5.Tongji Univ, Dept Biomed Engn, Coll Life Sci & Technol, Shanghai 200092, Peoples R China
6.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
7.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
推荐引用方式
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.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。