ITP OpenIR  > SCI期刊论文
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.
Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins
Source PublicationBIOINFORMATICS
Language英语
AbstractMotivation: 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.
2015
Volume31Issue:23Pages:3773-3781
Subject AreaBiochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
DOIhttp://dx.doi.org/10.1093/bioinformatics/btv459
Indexed BySCI
Funding OrganizationNational 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]
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Document Type期刊论文
Identifierhttp://ir.itp.ac.cn/handle/311006/20775
CollectionSCI期刊论文
Corresponding AuthorZhang, Y (reprint author), Univ Michigan, Dept Computat Med & Bioinformat, Ann Arbor, MI 48109 USA.
Recommended Citation
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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