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

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.

Figure 1

Examples of (A) standard PSSM and (B) smoothed PSSM generated by PSI-BLAST (e-value = 0.001, iteration number = 3).

Figure 2

System architecture of RNAProB.

Figure 3

ROC curves and AUC of the (A) RBP86, (B) RBP109, and (C) RBP107 data sets.

Figure 4

(A) Accuracy with respect to different sliding window sizes using five-fold cross-validation and three-way data split for the RBP86 data set, respectively. (B) The performance of the RBP86 data set with different smoothing window sizes by five-fold cross-validation. (C) The performance of the RBP86 data set with different smoothing window sizes by three-way data split.

Figure 5

(A) Accuracy with respect to different sliding window sizes using five-fold cross-validation and three-way data split for the RBP109 data set, respectively. (B) The performance of the RBP109 data set with different smoothing window sizes by five-fold cross-validation. (C) The performance of the RBP109 data set with different smoothing window sizes by three-way data split.

Figure 6

(A) Accuracy with respect to different sliding window sizes using five-fold cross-validation and three-way data split for the RBP107 data set, respectively. (B) The performance of the RBP107 data set with different smoothing window sizes by five-fold cross-validation. (C) The performance of the RBP107 data set with different smoothing window sizes by three-way data split.

Figure 7

Amino acid compositions of interacting and non-interacting residues in the benchmark data sets.

Figure 8

Grouped amino acid compositions of interacting and non-interacting residues in the benchmark data sets.

Figure 9

Amino acid compositions of interacting and non-interacting residues in four different RNA groups of the RBP109 data set.

Figure 10

Pearson correlation coefficient between interacting and non-interacting evolutionary vectors generated by different PSSM encoding schemes in the benchmark data sets.