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This article is part of the supplement: Seventh International Conference on Bioinformatics (InCoB2008)

Open Access Research

Predicting RNA-binding sites of proteins using support vector machines and evolutionary information

Cheng-Wei Cheng14, Emily Chia-Yu Su234, Jenn-Kang Hwang2, Ting-Yi Sung4* and Wen-Lian Hsu14*

Author Affiliations

1 Institute of Information Systems and Applications, National Tsing Hua University, Hsinchu, Taiwan

2 Institute of Bioinformatics, National Chiao Tung University, Hsinchu, Taiwan

3 Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan

4 Bioinformatics Lab., Institute of Information Science, Academia Sinica, Taipei, Taiwan

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BMC Bioinformatics 2008, 9(Suppl 12):S6  doi:10.1186/1471-2105-9-S12-S6

Published: 12 December 2008

Abstract

Background

RNA-protein interaction plays an essential role in several biological processes, such as protein synthesis, gene expression, posttranscriptional regulation and viral infectivity. Identification of RNA-binding sites in proteins provides valuable insights for biologists. However, experimental determination of RNA-protein interaction remains time-consuming and labor-intensive. Thus, computational approaches for prediction of RNA-binding sites in proteins have become highly desirable. Extensive studies of RNA-binding site prediction have led to the development of several methods. However, they could yield low sensitivities in trade-off for high specificities.

Results

We propose a method, RNAProB, which incorporates a new smoothed position-specific scoring matrix (PSSM) encoding scheme with a support vector machine model to predict RNA-binding sites in proteins. Besides the incorporation of evolutionary information from standard PSSM profiles, the proposed smoothed PSSM encoding scheme also considers the correlation and dependency from the neighboring residues for each amino acid in a protein. Experimental results show that smoothed PSSM encoding significantly enhances the prediction performance, especially for sensitivity. Using five-fold cross-validation, our method performs better than the state-of-the-art systems by 4.90%~6.83%, 0.88%~5.33%, and 0.10~0.23 in terms of overall accuracy, specificity, and Matthew's correlation coefficient, respectively. Most notably, compared to other approaches, RNAProB significantly improves sensitivity by 7.0%~26.9% over the benchmark data sets. To prevent data over fitting, a three-way data split procedure is incorporated to estimate the prediction performance. Moreover, physicochemical properties and amino acid preferences of RNA-binding proteins are examined and analyzed.

Conclusion

Our results demonstrate that smoothed PSSM encoding scheme significantly enhances the performance of RNA-binding site prediction in proteins. This also supports our assumption that smoothed PSSM encoding can better resolve the ambiguity of discriminating between interacting and non-interacting residues by modelling the dependency from surrounding residues. The proposed method can be used in other research areas, such as DNA-binding site prediction, protein-protein interaction, and prediction of posttranslational modification sites.