Disulfide Connectivity Prediction with 70% Accuracy Using Two-level Models.
From: Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Republic of China.
Proteins
- Publish Date: Jul 2006
- ISSN: 1097-0134
- Volume: 64
- Issue: 1
- Pages: 246-52
- Medium: Internet
- Language: English
- Citation (JAMA): Chen Bo-Juen, Tsai Chi-Hung, Chan Chen-hsiung, et al. Disulfide Connectivity Prediction with 70% Accuracy Using Two-level Models.. Proteins Jul 2006;64:246-52
Abstract
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.
Mesh Headings (Keywords): Amino Acid Sequence, Binding Sites, Cysteine, Databases, Protein, Disulfides, Entropy, Hydrogen Bonding, Models, Molecular, Molecular Sequence Data, Predictive Value of Tests, Protein Conformation, Protein Folding, Proteins
Check for Full Text / PubMed Unique Identifier (PMID): 16615141
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