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Prediction of RNA binding sites in proteins from amino acid sequence
RNA
  • Michael Terribilini, Iowa State University
  • Jae-Hyung Lee, Iowa State University
  • Changhui Yan, Utah State University
  • Robert L. Jernigan, Iowa State University
  • Vasant Honavar, Iowa State University
  • Drena Dobbs, Iowa State University
Document Type
Article
Publication Version
Published Version
Publication Date
1-1-2006
DOI
10.1261/rna.2197306
Abstract
RNA–protein interactions are vitally important in a wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses. We have developed a computational tool for predicting which amino acids of an RNA binding protein participate in RNA–protein interactions, using only the protein sequence as input. RNABindR was developed using machine learning on a validated nonredundant data set of interfaces from known RNA–protein complexes in the Protein Data Bank. It generates a classifier that captures primary sequence signals sufficient for predicting which amino acids in a given protein are located in the RNA–protein interface. In leave-one-out cross-validation experiments, RNABindR identifies interface residues with >85% overall accuracy. It can be calibrated by the user to obtain either high specificity or high sensitivity for interface residues. RNABindR, implementing a Naive Bayes classifier, performs as well as a more complex neural network classifier (to our knowledge, the only previously published sequence-based method for RNA binding site prediction) and offers the advantages of speed, simplicity and interpretability of results. RNABindR predictions on the human telomerase protein hTERT are in good agreement with experimental data. The availability of computational tools for predicting which residues in an RNA binding protein are likely to contact RNA should facilitate design of experiments to directly test RNA binding function and contribute to our understanding of the diversity, mechanisms, and regulation of RNA–protein complexes in biological systems. (RNABindR is available as a Web tool from http://bindr.gdcb.iastate.edu.)
Comments

This article is from RNA 12 (2006): 1450, doi: 10.1261/rna.2197306. Posted with permission.

Copyright Owner
RNA Society
Language
en
File Format
application/pdf
Citation Information
Michael Terribilini, Jae-Hyung Lee, Changhui Yan, Robert L. Jernigan, et al.. "Prediction of RNA binding sites in proteins from amino acid sequence" RNA Vol. 12 (2006) p. 1450 - 1462
Available at: http://works.bepress.com/drena-dobbs/32/