Dataset Information
Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins.
Ontology highlight
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.Availability and implementation
http://www.csbio.sjtu.edu.cn/bioinf/Cyscon/Contact
zhng@umich.edu or hbshen@sjtu.edu.cn.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Yang J
PROVIDER: S-EPMC5898604 | biostudies-literature | 2015 Dec
REPOSITORIES: biostudies-literature
Publications
Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins.
Bioinformatics (Oxford, England) 20150807 23
<h4>Motivation</h4>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.<h4>Results</h4>We propose a new hierarchical order reduction protocol called Cyscon for disulfide-bonding prediction ...[more]