SVM based prediction of RNA‐binding proteins using binding residues and evolutionary information

RNA‐binding proteins (RBPs) play crucial role in transcription and gene‐regulation. This paper describes a support vector machine (SVM) based method for discriminating and classifying RNA‐binding and non‐binding proteins using sequence features. With the threshold of 30% interacting residues, RNA‐binding amino acid prediction method PPRINT achieved the Matthews correlation coefficient (MCC) of 0.32. BLAST and PSI‐BLAST identified RBPs with the coverage of 32.63 and 33.16%, respectively, at the e‐value of 1e‐4. The SVM models developed with amino acid, dipeptide and four‐part amino acid compositions showed the MCC of 0.60, 0.46, and 0.53, respectively. This is the first study in which evolutionary information in form of position specific scoring matrix (PSSM) profile has been successfully used for predicting RBPs. We achieved the maximum MCC of 0.62 using SVM model based on PSSM called PSSM‐400. Finally, we developed different hybrid approaches and achieved maximum MCC of 0.66. We also developed a method for predicting three subclasses of RNA binding proteins (e.g., rRNA, tRNA, mRNA binding proteins). The performance of the method was also evaluated on an independent dataset of 69 RBPs and 100 non‐RBPs (NBPs). An additional benchmarking was also performed using gene ontology (GO) based annotation. Based on the hybrid approach a web‐server RNApred has been developed for predicting RNA binding proteins from amino acid sequences (http://www.imtech.res.in/raghava/rnapred/). Copyright © 2010 John Wiley & Sons, Ltd.

• Publication year

  2011

• Venue

  Journal of Molecular Recognition

• Publication date

  2011-02-25

• Fields of study

  Biology, Medicine, Computer Science

• Identifiers
  DOI 10.1002/jmr.1061PMID 20677174
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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