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

BMC Bioinformatics

Proceedings

Jerome Lin¹  Chi-Wei Chen¹  Yen-Wei Chu² 

[1] Institute of Genomics and Bioinformatics, National Chung Hsing University, 250, Kuo Kuang Rd., 402, Taichung, Taiwan;Institute of Genomics and Bioinformatics, National Chung Hsing University, 250, Kuo Kuang Rd., 402, Taichung, Taiwan;Biotechnology Center, National Chung Hsing University, 250, Kuo Kuang Rd., 402, Taichung, Taiwan;Agricultural Biotechnology Center, National Chung Hsing University, 250, Kuo Kuang Rd., 402, Taichung, Taiwan;Institute of Molecular Biology, National Chung Hsing University, 250, Kuo Kuang Rd., 402, Taichung, Taiwan;Graduate Institute of Biotechnology, National Chung Hsing University, 250, Kuo Kuang Rd., 402, Taichung, Taiwan;

关键词: Support Vector Machine;    Protein Data Bank;    Support Vector Machine Model;    Matthews Correlation Coefficient;    Stability Change;   

DOI  :  10.1186/1471-2105-14-S2-S5

来源: Springer

【 摘 要 】

BackgroundMutation 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.ResultsWe 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.ConclusionsThe 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.

【 授权许可】

Unknown   
© Chen et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.