Prediction of RNA binding sites in proteins from amino acid sequence
- 21 June 2006
- journal article
- research article
- Published by Cold Spring Harbor Laboratory in RNA
- Vol. 12 (8), 1450-1462
- https://doi.org/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.)Keywords
This publication has 55 references indexed in Scilit:
- An algorithm for predicting protein–protein interaction sites: Abnormally exposed amino acid residues and secondary structure elementsProtein Science, 2006
- Predicting rRNA-, RNA-, and DNA-binding proteins from primary structure with support vector machinesJournal of Theoretical Biology, 2005
- A two-stage classifier for identification of protein–protein interface residuesBioinformatics, 2004
- Identification of interface residues in protease-inhibitor and antigen-antibody complexes: a support vector machine approachNeural Computing & Applications, 2004
- ProMate: A Structure Based Prediction Program to Identify the Location of Protein–Protein Binding SitesJournal of Molecular Biology, 2004
- Automatic prediction of protein functionCellular and Molecular Life Sciences, 2003
- The kink-turn: a new RNA secondary structure motifThe EMBO Journal, 2001
- RNA Binding Domain of Telomerase Reverse TranscriptaseMolecular and Cellular Biology, 2001
- The Protein Data BankNucleic Acids Research, 2000
- RNA–protein complexesCurrent Opinion in Structural Biology, 1999