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This article is part of the supplement: Selected articles from the 2009 IEEE International Conference on Bioinformatics and Biomedicine

Open Access Proceedings

SeqRate: sequence-based protein folding type classification and rates prediction

Guan Ning Lin1, Zheng Wang2, Dong Xu12 and Jianlin Cheng12*

Author Affiliations

1 Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA

2 Department of Computer Science, University of Missouri, Columbia, Missouri, 65211, USA

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BMC Bioinformatics 2010, 11(Suppl 3):S1  doi:10.1186/1471-2105-11-S3-S1

Published: 29 April 2010

Abstract

Background

Protein folding rate is an important property of a protein. Predicting protein folding rate is useful for understanding protein folding process and guiding protein design. Most previous methods of predicting protein folding rate require the tertiary structure of a protein as an input. And most methods do not distinguish the different kinetic nature (two-state folding or multi-state folding) of the proteins. Here we developed a method, SeqRate, to predict both protein folding kinetic type (two-state versus multi-state) and real-value folding rate using sequence length, amino acid composition, contact order, contact number, and secondary structure information predicted from only protein sequence with support vector machines.

Results

We systematically studied the contributions of individual features to folding rate prediction. On a standard benchmark dataset, the accuracy of folding kinetic type classification is 80%. The Pearson correlation coefficient and the mean absolute difference between predicted and experimental folding rates (sec-1) in the base-10 logarithmic scale are 0.81 and 0.79 for two-state protein folders, and 0.80 and 0.68 for three-state protein folders. SeqRate is the first sequence-based method for protein folding type classification and its accuracy of fold rate prediction is improved over previous sequence-based methods. Its performance can be further enhanced with additional information, such as structure-based geometric contacts, as inputs.

Conclusions

Both the web server and software of predicting folding rate are publicly available at http://casp.rnet.missouri.edu/fold_rate/index.html webcite.