Knowledge Management System of Institute of Theoretical Physics, CAS
| 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 Publication | BIOINFORMATICS
 |
| Language | 英语 |
| Abstract | 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. |
| 2015 | |
| Volume | 31Issue:23Pages:3773-3781 |
| Subject Area | Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics |
| DOI | http://dx.doi.org/10.1093/bioinformatics/btv459 |
| Indexed By | SCI |
| Funding Organization | 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] |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.itp.ac.cn/handle/311006/20775 |
| Collection | SCI期刊论文 |
| Corresponding Author | Zhang, 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. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Accurate disulfide-b（435KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | Application Full Text | |
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