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题名: Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins
作者: Yang, J;  He, BJ;  Jang, R;  Zhang, Y;  Shen, HB
刊名: BIOINFORMATICS
出版日期: 2015
卷号: 31, 期号:23, 页码:3773-3781
学科分类: Biochemistry & Molecular Biology ;  Biotechnology & Applied Microbiology ;  Computer Science ;  Mathematical & Computational Biology ;  Mathematics
DOI: http://dx.doi.org/10.1093/bioinformatics/btv459
通讯作者: Zhang, Y (reprint author), Univ Michigan, Dept Computat Med & Bioinformat, Ann Arbor, MI 48109 USA.
文章类型: Article
英文摘要: Motivation: Cysteine-rich proteins cover many important families in nature but there are currently no methods specifically designed for modeling the structure of these proteins. The accuracy of disulfide connectivity pattern prediction, particularly for the proteins of higher-order connections, e.g. >3 bonds, is too low to effectively assist structure assembly simulations. Results: We propose a new hierarchical order reduction protocol called Cyscon for disulfide-bonding prediction. The most confident disulfide bonds are first identified and bonding prediction is then focused on the remaining cysteine residues based on SVR training. Compared with purely machine learning-based approaches, Cyscon improved the average accuracy of connectivity pattern prediction by 21.9%. For proteins with more than 5 disulfide bonds, Cyscon improved the accuracy by 585% on the benchmark set of PDBCYS. When applied to 158 non-redundant cysteine-rich proteins, Cyscon predictions helped increase (or decrease) the TM-score (or RMSD) of the ab initio QUARK modeling by 12.1% (or 14.4%). This result demonstrates a new avenue to improve the ab initio structure modeling for cysteine-rich proteins.
类目[WOS]: Biochemical Research Methods ;  Biotechnology & Applied Microbiology ;  Computer Science, Interdisciplinary Applications ;  Mathematical & Computational Biology ;  Statistics & Probability
关键词[WOS]: RESIDUE CONTACT PREDICTION ;  MACHINE-LEARNING-METHODS ;  SUPPORT VECTOR MACHINES ;  CONNECTIVITY PREDICTION ;  CORRELATED MUTATIONS ;  SECONDARY STRUCTURE ;  SEQUENCE ;  SCALE ;  CLASSIFICATION ;  CONSEQUENCES
收录类别: SCI
项目资助者: National Natural Science Foundation of China [61222306, 91130033, 61175024] ;  Shanghai Science and Technology Commission [11JC1404800] ;  National Institute of General Medical Sciences [GM083107]
语种: 英语
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内容类型: 期刊论文
URI标识: http://ir.itp.ac.cn/handle/311006/20775
Appears in Collections:理论物理所2015年知识产出_期刊论文

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Recommended Citation:
Yang, J,He, BJ,Jang, R,et al. Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins[J]. BIOINFORMATICS,2015,31(23):3773-3781.
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