Robust and accurate prediction of protein self-interactions from amino acids sequence using evolutionary information

doi:10.1039/c6mb00599c

CSpace > 应用数学研究所

	Robust and accurate prediction of protein self-interactions from amino acids sequence using evolutionary information
	An, Ji-Yong 1; You, Zhu-Hong 2; Chen, Xing 3; Huang, De-Shuang 4; Yan, Guiying5 ; Wang, Da-Fu 1
	2016
发表期刊	MOLECULAR BIOSYSTEMS
ISSN	1742-206X
卷号	12 期号:12 页码:3702-3710
摘要	Self-interacting proteins (SIPs) play an essential role in cellular functions and the evolution of protein interaction networks (PINs). Due to the limitations of experimental self-interaction proteins detection technology, it is a very important task to develop a robust and accurate computational approach for SIPs prediction. In this study, we propose a novel computational method for predicting SIPs from protein amino acids sequence. Firstly, a novel feature representation scheme based on Local Binary Pattern (LBP) is developed, in which the evolutionary information, in the form of multiple sequence alignments, is taken into account. Then, by employing the Relevance Vector Machine (RVM) classifier, the performance of our proposed method is evaluated on yeast and human datasets using a five-fold cross-validation test. The experimental results show that the proposed method can achieve high accuracies of 94.82% and 97.28% on yeast and human datasets, respectively. For further assessing the performance of our method, we compared it with the state-of-the-art Support Vector Machine (SVM) classifier, and other existing methods, on the same datasets. Comparison results demonstrate that the proposed method is very promising and could provide a cost-effective alternative for predicting SIPs. In addition, to facilitate extensive studies for future proteomics research, a web server is freely available for academic use at http://219.219.62.123:8888/HASIPP.
DOI	10.1039/c6mb00599c
语种	英语
资助项目	National Science Foundation of China[61373086] ; National Science Foundation of China[11301517] ; National Science Foundation of China[61572506] ; National Science Foundation of China[11631014] ; Guangdong Natural Science Foundation[2014A030313555] ; Pioneer Hundred Talents Program of Chinese Academy of Sciences
WOS研究方向	Biochemistry & Molecular Biology
WOS类目	Biochemistry & Molecular Biology
WOS记录号	WOS:000388946800019
出版者	ROYAL SOC CHEMISTRY
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/24181
专题	应用数学研究所
通讯作者	You, Zhu-Hong; Chen, Xing
作者单位	1.China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 21116, Peoples R China 2.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China 3.China Univ Min & Technol, Sch Informat & Elect Engn, Xuzhou 221116, Jiangsu, Peoples R China 4.Tongji Univ, Sch Elect & Informat Engn, Shanghai 201804, Peoples R China 5.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	An, Ji-Yong,You, Zhu-Hong,Chen, Xing,et al. Robust and accurate prediction of protein self-interactions from amino acids sequence using evolutionary information[J]. MOLECULAR BIOSYSTEMS,2016,12(12):3702-3710.
APA	An, Ji-Yong,You, Zhu-Hong,Chen, Xing,Huang, De-Shuang,Yan, Guiying,&Wang, Da-Fu.(2016).Robust and accurate prediction of protein self-interactions from amino acids sequence using evolutionary information.MOLECULAR BIOSYSTEMS,12(12),3702-3710.
MLA	An, Ji-Yong,et al."Robust and accurate prediction of protein self-interactions from amino acids sequence using evolutionary information".MOLECULAR BIOSYSTEMS 12.12(2016):3702-3710.