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This article is part of the supplement: Selected articles from the Eleventh Asia Pacific Bioinformatics Conference (APBC 2013): Bioinformatics

Open Access Proceedings

iStable: off-the-shelf predictor integration for predicting protein stability changes

Chi-Wei Chen1, Jerome Lin1 and Yen-Wei Chu12345*

Author affiliations

1 Institute of Genomics and Bioinformatics, National Chung Hsing University 250, Kuo Kuang Rd., Taichung 402, Taiwan

2 Biotechnology Center, National Chung Hsing University 250, Kuo Kuang Rd., Taichung 402, Taiwan

3 Agricultural Biotechnology Center, National Chung Hsing University 250, Kuo Kuang Rd., Taichung 402, Taiwan

4 Institute of Molecular Biology, National Chung Hsing University 250, Kuo Kuang Rd., Taichung 402, Taiwan

5 Graduate Institute of Biotechnology, National Chung Hsing University 250, Kuo Kuang Rd., Taichung 402, Taiwan

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Citation and License

BMC Bioinformatics 2013, 14(Suppl 2):S5  doi:10.1186/1471-2105-14-S2-S5

Published: 21 January 2013

Abstract

Background

Mutation of a single amino acid residue can cause changes in a protein, which could then lead to a loss of protein function. Predicting the protein stability changes can provide several possible candidates for the novel protein designing. Although many prediction tools are available, the conflicting prediction results from different tools could cause confusion to users.

Results

We proposed an integrated predictor, iStable, with grid computing architecture constructed by using sequence information and prediction results from different element predictors. In the learning model, several machine learning methods were evaluated and adopted the support vector machine as an integrator, while not just choosing the majority answer given by element predictors. Furthermore, the role of the sequence information played was analyzed in our model, and an 11-window size was determined. On the other hand, iStable is available with two different input types: structural and sequential. After training and cross-validation, iStable has better performance than all of the element predictors on several datasets. Under different classifications and conditions for validation, this study has also shown better overall performance in different types of secondary structures, relative solvent accessibility circumstances, protein memberships in different superfamilies, and experimental conditions.

Conclusions

The trained and validated version of iStable provides an accurate approach for prediction of protein stability changes. iStable is freely available online at: http://predictor.nchu.edu.tw/iStable webcite.