Improving prediction of burial state of residues by exploiting correlation among residues

doi:10.1186/s12859-017-1475-5

	Improving prediction of burial state of residues by exploiting correlation among residues
	Gong, Hai'e 1,2; Zhang, Haicang 1,2; Zhu, Jianwei 1,2; Wang, Chao 1,2; Sun, Shiwei 1; Zheng, Wei-Mou 3; Bu, Dongbo 1
	2017-03-14
发表期刊	BMC BIOINFORMATICS
ISSN	1471-2105
卷号	18 页码:11
摘要	Background: Residues in a protein might be buried inside or exposed to the solvent surrounding the protein. The buried residues usually form hydrophobic cores to maintain the structural integrity of proteins while the exposed residues are tightly related to protein functions. Thus, the accurate prediction of solvent accessibility of residues will greatly facilitate our understanding of both structure and functionalities of proteins. Most of the state-of-the-art prediction approaches consider the burial state of each residue independently, thus neglecting the correlations among residues. Results: In this study, we present a high-order conditional random field model that considers burial states of all residues in a protein simultaneously. Our approach exploits not only the correlation among adjacent residues but also the correlation among long-range residues. Experimental results showed that by exploiting the correlation among residues, our approach outperformed the state-of-the-art approaches in prediction accuracy. In-depth case studies also showed that by using the high-order statistical model, the errors committed by the bidirectional recurrent neural network and chain conditional random field models were successfully corrected. Conclusions: Our methods enable the accurate prediction of residue burial states, which should greatly facilitate protein structure prediction and evaluation.
关键词	Protein structure Burial states of residue Conditional random field Residue correlation
DOI	10.1186/s12859-017-1475-5
收录类别	SCI
语种	英语
资助项目	National Basic Research Program of China[2012CB316502] ; National Natural Science Foundation of China[11175224] ; National Natural Science Foundation of China[11121403] ; National Natural Science Foundation of China[31270834] ; National Natural Science Foundation of China[61272318] ; National Natural Science Foundation of China[31671369]
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
WOS类目	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
WOS记录号	WOS:000397505300002
出版者	BIOMED CENTRAL LTD
引用统计	被引频次：8[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/7322
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zheng, Wei-Mou; Bu, Dongbo
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Proc, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Comp Sci, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Theoret Phys, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Gong, Hai'e,Zhang, Haicang,Zhu, Jianwei,et al. Improving prediction of burial state of residues by exploiting correlation among residues[J]. BMC BIOINFORMATICS,2017,18:11.
APA	Gong, Hai'e.,Zhang, Haicang.,Zhu, Jianwei.,Wang, Chao.,Sun, Shiwei.,...&Bu, Dongbo.(2017).Improving prediction of burial state of residues by exploiting correlation among residues.BMC BIOINFORMATICS,18,11.
MLA	Gong, Hai'e,et al."Improving prediction of burial state of residues by exploiting correlation among residues".BMC BIOINFORMATICS 18(2017):11.