Bioinformatics Advance Access originally published online on December 7, 2004
Bioinformatics 2005 21(8):1415-1420; doi:10.1093/bioinformatics/bti179
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Cysteine separations profiles on protein sequences infer disulfide connectivity
1Bioinformatics Laboratory, Department of Computer Science and Information Engineering, National Taiwan University No. 1, Sec. 4, Roosevelt Rd., Taipei, Taiwan 106
2Department of Chemical Engineering and Graduate Institute of Biotechnology, National Taipei University of Technology No. 1, Sec. 3, Chung-Hsiao E. Rd., Taipei, Taiwan 10608
*To whom correspondence should be addressed.
Motivation: Disulfide bonds play an important role in protein folding. A precise prediction of disulfide connectivity can strongly reduce the conformational search space and increase the accuracy in protein structure prediction. Conventional disulfide connectivity predictions use sequence information, and prediction accuracy is limited. Here, by using an alternative scheme with global information for disulfide connectivity prediction, higher performance is obtained with respect to other approaches.
Result: Cysteine separation profiles have been used to predict the disulfide connectivity of proteins. The separations among oxidized cysteine residues on a protein sequence have been encoded into vectors named cysteine separation profiles (CSPs). Through comparisons of their CSPs, the disulfide connectivity of a test protein is inferred from a non-redundant template set. For non-redundant proteins in SwissProt 39 (SP39) sharing less than 30% sequence identity, the prediction accuracy of a fourfold cross-validation is 49%. The prediction accuracy of disulfide connectivity for proteins in SwissProt 43 (SP43) is even higher (53%). The relationship between the similarity of CSPs and the prediction accuracy is also discussed. The method proposed in this work is relatively simple and can generate higher accuracies compared to conventional methods. It may be also combined with other algorithms for further improvements in protein structure prediction.
Availability: The program and datasets are available from the authors upon request.
Contact: cykao{at}csie.ntu.edu.tw
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