title: Prediction of RNA binding sites in proteins from amino acid sequence.
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.).
authors.affiliation: Bioinformatics and Computationa Biology, Graduate Program, Iowa State University, Ames, Iowa 50010, USA. terrible@iastate.edu
authors.forename: Michael
authors.initials: M
authors.lastname: Terribilini
authors.forename: Jae-Hyung
authors.initials: JH
authors.lastname: Lee
authors.forename: Changhui
authors.initials: C
authors.lastname: Yan
authors.forename: Robert L
authors.initials: RL
authors.lastname: Jernigan
authors.forename: Vasant
authors.initials: V
authors.lastname: Honavar
authors.forename: Drena
authors.initials: D
authors.lastname: Dobbs
mesh_terms: D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry*; D000818:Animals; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D006801:Humans; D008958:Models, Molecular; D008969:Molecular Sequence Data; D011237:Predictive Value of Tests; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D012313:RNA / Q000378:metabolism*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D020539:Sequence Analysis, Protein; D012984:Software
chemical_list: D000596:Amino Acids; D011506:Proteins; D012313:RNA