Bioinformatics Advance Access originally published online on October 17, 2007
Bioinformatics 2007 23(23):3147-3154; doi:10.1093/bioinformatics/btm505
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Predicting disulfide connectivity from protein sequence using multiple sequence feature vectors and secondary structure
1Advanced Computational Modelling Centre, 2ARC Centre in Bioinformatics and Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia, 3Caulfield School of Information Technology, Monash University, Caulfield, East VIC 3145 and 4School of Molecular & Microbial Sciences and Australian Institute for Bioengineering & Nanotechnology, The University of Queensland, Brisbane, QLD 4072, Australia
*To whom correspondence should be addressed.
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Abstract |
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Motivation: Disulfide bonds are primary covalent crosslinks between two cysteine residues in proteins that play critical roles in stabilizing the protein structures and are commonly found in extracy-toplasmatic or secreted proteins. In protein folding prediction, the localization of disulfide bonds can greatly reduce the search in conformational space. Therefore, there is a great need to develop computational methods capable of accurately predicting disulfide connectivity patterns in proteins that could have potentially important applications.
Results: We have developed a novel method to predict disulfide connectivity patterns from protein primary sequence, using a support vector regression (SVR) approach based on multiple sequence feature vectors and predicted secondary structure by the PSIPRED program. The results indicate that our method could achieve a prediction accuracy of 74.4% and 77.9%, respectively, when averaged on proteins with two to five disulfide bridges using 4-fold cross-validation, measured on the protein and cysteine pair on a well-defined non-homologous dataset. We assessed the effects of different sequence encoding schemes on the prediction performance of disulfide connectivity. It has been shown that the sequence encoding scheme based on multiple sequence feature vectors coupled with predicted secondary structure can significantly improve the prediction accuracy, thus enabling our method to outperform most of other currently available predictors. Our work provides a complementary approach to the current algorithms that should be useful in computationally assigning disulfide connectivity patterns and helps in the annotation of protein sequences generated by large-scale whole-genome projects.
Availability: The prediction web server and Supplementary Material are accessible at http://foo.maths.uq.edu.au/~huber/disulfide
Contact: kb{at}maths.uq.edu.au
Supplementary information: Supplementary data are available at Bioinformatics online.
Associate Editor: Anna Tramontano
Received on June 18, 2007; revised on October 3, 2007; accepted on October 4, 2007
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