Disulfide connectivity prediction with 70% accuracy using two-level models

Disulfide bridges stabilize protein structures covalently and play an important role in protein folding. Predicting disulfide connectivity precisely helps towards the solution of protein structure prediction. Previous methods for disulfide connectivity prediction either infer the bonding potential o...

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Bibliographic Details
Published in:Proteins, structure, function, and bioinformatics structure, function, and bioinformatics, 2006-07, Vol.64 (1), p.246-252
Main Authors: Chen, Bo-Juen, Tsai, Chi-Hung, Chan, Chen-hsiung, Kao, Cheng-Yan
Format: Article
Language:English
Subjects:
Amino Acid Sequence
Binding Sites
Cysteine
Databases, Protein
disulfide bonding pattern
disulfide connectivity
Disulfides - chemistry
Disulfides - metabolism
Entropy
hierarchical model
Hydrogen Bonding
Models, Molecular
Molecular Sequence Data
Predictive Value of Tests
Protein Conformation
Protein Folding
Proteins - chemistry
Proteins - metabolism
support vector machine
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Summary:Disulfide bridges stabilize protein structures covalently and play an important role in protein folding. Predicting disulfide connectivity precisely helps towards the solution of protein structure prediction. Previous methods for disulfide connectivity prediction either infer the bonding potential of cysteine pairs or rank alternative disulfide bonding patterns. As a result, these methods encode data according to cysteine pairs (pair‐wise) or disulfide bonding patterns (pattern‐wise). However, using either encoding scheme alone cannot fully utilize the local and global information of proteins, so the accuracies of previous methods are limited. In this work, we propose a novel two‐level framework to predict disulfide connectivity. With this framework, both the pair‐wise and pattern‐wise encoding schemes are considered. Our models were validated on the datasets derived from SWISS‐PROT 39 and 43, and the results demonstrate that our models can combine both local and global information. Compared to previous methods, significant improvements were obtained by our models. Our work may also provide insights to further improvements of disulfide connectivity prediction and increase its applicability in protein structure analysis and prediction. Proteins 2006. © 2006 Wiley‐Liss, Inc.
ISSN:0887-3585
1097-0134
DOI:10.1002/prot.20972