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Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins
Yang, J; He, BJ; Jang, R; Zhang, Y; Shen, HB; Zhang, Y (reprint author), Univ Michigan, Dept Computat Med & Bioinformat, Ann Arbor, MI 48109 USA.
2015
发表期刊BIOINFORMATICS
卷号31期号:23页码:3773-3781
文章类型Article
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.
学科领域Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
资助者National Natural Science Foundation of China [61222306, 91130033, 61175024] ; National Natural Science Foundation of China [61222306, 91130033, 61175024] ; National Natural Science Foundation of China [61222306, 91130033, 61175024] ; National Natural Science Foundation of China [61222306, 91130033, 61175024] ; Shanghai Science and Technology Commission [11JC1404800] ; Shanghai Science and Technology Commission [11JC1404800] ; Shanghai Science and Technology Commission [11JC1404800] ; Shanghai Science and Technology Commission [11JC1404800] ; National Institute of General Medical Sciences [GM083107] ; National Institute of General Medical Sciences [GM083107] ; National Institute of General Medical Sciences [GM083107] ; National Institute of General Medical Sciences [GM083107] ; National Natural Science Foundation of China [61222306, 91130033, 61175024] ; National Natural Science Foundation of China [61222306, 91130033, 61175024] ; National Natural Science Foundation of China [61222306, 91130033, 61175024] ; National Natural Science Foundation of China [61222306, 91130033, 61175024] ; Shanghai Science and Technology Commission [11JC1404800] ; Shanghai Science and Technology Commission [11JC1404800] ; Shanghai Science and Technology Commission [11JC1404800] ; Shanghai Science and Technology Commission [11JC1404800] ; National Institute of General Medical Sciences [GM083107] ; National Institute of General Medical Sciences [GM083107] ; National Institute of General Medical Sciences [GM083107] ; National Institute of General Medical Sciences [GM083107]
DOIhttp://dx.doi.org/10.1093/bioinformatics/btv459
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
语种英语
资助者National Natural Science Foundation of China [61222306, 91130033, 61175024] ; National Natural Science Foundation of China [61222306, 91130033, 61175024] ; National Natural Science Foundation of China [61222306, 91130033, 61175024] ; National Natural Science Foundation of China [61222306, 91130033, 61175024] ; Shanghai Science and Technology Commission [11JC1404800] ; Shanghai Science and Technology Commission [11JC1404800] ; Shanghai Science and Technology Commission [11JC1404800] ; Shanghai Science and Technology Commission [11JC1404800] ; National Institute of General Medical Sciences [GM083107] ; National Institute of General Medical Sciences [GM083107] ; National Institute of General Medical Sciences [GM083107] ; National Institute of General Medical Sciences [GM083107] ; National Natural Science Foundation of China [61222306, 91130033, 61175024] ; National Natural Science Foundation of China [61222306, 91130033, 61175024] ; National Natural Science Foundation of China [61222306, 91130033, 61175024] ; National Natural Science Foundation of China [61222306, 91130033, 61175024] ; Shanghai Science and Technology Commission [11JC1404800] ; Shanghai Science and Technology Commission [11JC1404800] ; Shanghai Science and Technology Commission [11JC1404800] ; Shanghai Science and Technology Commission [11JC1404800] ; National Institute of General Medical Sciences [GM083107] ; National Institute of General Medical Sciences [GM083107] ; National Institute of General Medical Sciences [GM083107] ; National Institute of General Medical Sciences [GM083107]
WOS类目Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
引用统计
文献类型期刊论文
条目标识符http://ir.itp.ac.cn/handle/311006/20775
专题理论物理所SCI论文
通讯作者Zhang, Y (reprint author), Univ Michigan, Dept Computat Med & Bioinformat, Ann Arbor, MI 48109 USA.
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GB/T 7714
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.
APA Yang, J,He, BJ,Jang, R,Zhang, Y,Shen, HB,&Zhang, Y .(2015).Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins.BIOINFORMATICS,31(23),3773-3781.
MLA Yang, J,et al."Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins".BIOINFORMATICS 31.23(2015):3773-3781.
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