title: Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins.
abstract: BACKGROUND
  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.
  
  
  METHODS
  http://www.csbio.sjtu.edu.cn/bioinf/Cyscon/
  
  
  BACKGROUND
  zhng@umich.edu or hbshen@sjtu.edu.cn.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Yang
authors.affiliation: State Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China, Department of Computational Medicine and Bioinformatics and.
authors.forename: Bao-Ji
authors.initials: BJ
authors.lastname: He
authors.affiliation: Department of Computational Medicine and Bioinformatics and.
authors.forename: Richard
authors.initials: R
authors.lastname: Jang
authors.affiliation: Department of Computational Medicine and Bioinformatics and Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Yang
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China, Department of Computational Medicine and Bioinformatics and.
authors.forename: Hong-Bin
authors.initials: HB
authors.lastname: Shen
mesh_terms: D000465:Algorithms; D003545:Cysteine / Q000737:chemistry*; D004220:Disulfides / Q000737:chemistry*; D011506:Proteins / Q000737:chemistry*; D020539:Sequence Analysis, Protein; D060388:Support Vector Machine
chemical_list: D004220:Disulfides; D011506:Proteins; D003545:Cysteine