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题名: Improving prediction of burial state of residues by exploiting correlation among residues
作者: Gong, HE ;  Zhang, HC ;  Zhu, JW ;  Wang, C ;  Sun, SW ;  Zheng, WM ;  Bu, DB
刊名: BMC BIOINFORMATICS
出版日期: 2017
卷号: 18, 页码:70
关键词: Protein structure ;  Burial states of residue ;  Conditional random field ;  Residue correlation
学科分类: Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Mathematical & Computational Biology
DOI: http://dx.doi.org/10.1186/s12859-017-1475-5
通讯作者: Bu, DB (reprint author), Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Proc, Beijing 100190, Peoples R China. ;  Zheng, WM (reprint author), Chinese Acad Sci, Inst Theoret Phys, Beijing 100190, Peoples R China.
文章类型: Article
英文摘要: 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.
类目[WOS]: Biochemical Research Methods ;  Biotechnology & Applied Microbiology ;  Mathematical & Computational Biology
关键词[WOS]: REAL-VALUE PREDICTION ;  RELATIVE SOLVENT ACCESSIBILITY ;  NEURAL-NETWORKS ;  SECONDARY STRUCTURE ;  AMINO-ACIDS ;  SEQUENCE ;  REGRESSION ;  PROTEINS ;  GENERATION ;  SIMILARITY
项目资助者: National Basic Research Program of China [2012CB316502] ;  National Natural Science Foundation of China [11175224, 11121403, 31270834, 61272318, 31671369]
语种: 英语
Citation statistics: 
内容类型: 期刊论文
URI标识: http://ir.itp.ac.cn/handle/311006/22106
Appears in Collections:理论物理所2017年知识产出_期刊论文

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Recommended Citation:
Gong, HE,Zhang, HC,Zhu, JW,et al. Improving prediction of burial state of residues by exploiting correlation among residues[J]. BMC BIOINFORMATICS,2017,18:70.
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