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首页 > 过刊浏览>2019年第30卷第11期 >3413-3426. DOI:10.13328/j.cnki.jos.005571
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引入序列信息的残基相互作用网络比对算法
作者:
作者简介:

陶斯涵(1993-),女,湖南株洲人,硕士,主要研究领域为计算智能,生物信息学;丁彦蕊(1976-),女,博士,教授,CCF专业会员,主要研究领域为计算智能,生物信息学.

通讯作者:

丁彦蕊,E-mail:yr_ding@jiangnan.edu.cn

中图分类号:

TP391

基金项目:

国家自然科学基金（21541006）；留学回国人员科研启动基金


Algorithm Introduced Sequence Information for Residue Interaction Network Alignment
Author:
Fund Project:

National Natural Science Foundation of China (21541006); Scientific Research Start-up Fund for Returned Overseas Chinese Scholars, Ministry of Education

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    摘要:

    残基相互作用网络比对，对于研究蛋白质结构与功能的关系具有重要意义.在基于网络拓扑信息进行网络比对的MAGNA算法基础上，将蛋白质的序列信息（即残基匹配度）引入到其优化函数中，确定拓扑信息和序列信息对比对的影响程度，提出适合于残基相互作用网络比对的SI-MAGNA算法.实验结果表明，SI-MAGNA算法比现有的基于网络拓扑信息的经典比对方法（GRAAL、MI-GRAAL、MAGNA和CytoGEDEVO）具有更高的边正确性（edge correctness，简称EC）.最后，使用SI-MAGNA算法对来自不同耐热温度的生物的同源蛋白质进行网络比对和分析，探索蛋白质结构对其热稳定性的影响.

    Abstract:

    Residue interaction network alignment plays an important role in the research of the relations between protein structure and its function. In this study, protein sequence information (residue matching degree) is introduced to the optimization function of MAGNA algorithm, which carries out network alignment through network topological information, and studied the influence of topological information and sequence information on the residue interaction network alignment. Then, an SI-MAGNA algorithm suitable for residue interaction network alignment is proposed. The experiment showed that SI-MAGNA algorithm has higher accuracy EC (edge correctness) compared with the classical alignment methods (GRAAL, MI-GRAAL, MAGNA, and CytoGEDEVO) based on network topological information. At last, using SI-MAGNA algorithm to align and analyze the residue interaction networks of biological homologous proteins from different heat-resistance temperatures, the influence of protein structure on the thermal stability is studied.

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陶斯涵,丁彦蕊.引入序列信息的残基相互作用网络比对算法.软件学报,2019,30(11):3413-3426

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  • 收稿日期:2017-11-27
  • 最后修改日期:2018-01-03
  • 在线发布日期: 2019-11-06
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