title: Predicting RNA-protein interactions using only sequence information.
abstract: BACKGROUND
  RNA-protein interactions (RPIs) play important roles in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulation of gene expression to host defense against pathogens. High throughput experiments to identify RNA-protein interactions are beginning to provide valuable information about the complexity of RNA-protein interaction networks, but are expensive and time consuming. Hence, there is a need for reliable computational methods for predicting RNA-protein interactions.
  
  
  RESULTS
  We propose RPISeq, a family of classifiers for predicting RNA-protein interactions using only sequence information. Given the sequences of an RNA and a protein as input, RPIseq predicts whether or not the RNA-protein pair interact. The RNA sequence is encoded as a normalized vector of its ribonucleotide 4-mer composition, and the protein sequence is encoded as a normalized vector of its 3-mer composition, based on a 7-letter reduced alphabet representation. Two variants of RPISeq are presented: RPISeq-SVM, which uses a Support Vector Machine (SVM) classifier and RPISeq-RF, which uses a Random Forest classifier. On two non-redundant benchmark datasets extracted from the Protein-RNA Interface Database (PRIDB), RPISeq achieved an AUC (Area Under the Receiver Operating Characteristic (ROC) curve) of 0.96 and 0.92. On a third dataset containing only mRNA-protein interactions, the performance of RPISeq was competitive with that of a published method that requires information regarding many different features (e.g., mRNA half-life, GO annotations) of the putative RNA and protein partners. In addition, RPISeq classifiers trained using the PRIDB data correctly predicted the majority (57-99%) of non-coding RNA-protein interactions in NPInter-derived networks from E. coli, S. cerevisiae, D. melanogaster, M. musculus, and H. sapiens.
  
  
  CONCLUSIONS
  Our experiments with RPISeq demonstrate that RNA-protein interactions can be reliably predicted using only sequence-derived information. RPISeq offers an inexpensive method for computational construction of RNA-protein interaction networks, and should provide useful insights into the function of non-coding RNAs. RPISeq is freely available as a web-based server at http://pridb.gdcb.iastate.edu/RPISeq/.
authors.affiliation: Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa, USA. usha@iastate.edu
authors.forename: Usha K
authors.initials: UK
authors.lastname: Muppirala
authors.forename: Vasant G
authors.initials: VG
authors.lastname: Honavar
authors.forename: Drena
authors.initials: D
authors.lastname: Dobbs
mesh_terms: D000465:Algorithms; D000818:Animals; D030562:Databases, Protein; D004331:Drosophila melanogaster / Q000378:metabolism; D004926:Escherichia coli / Q000378:metabolism; D006801:Humans; D051379:Mice; D060066:Protein Interaction Maps; D011506:Proteins / Q000378:metabolism*; D012313:RNA / Q000737:chemistry* / Q000378:metabolism; D020871:RNA Stability; D016601:RNA-Binding Proteins / Q000378:metabolism*; D012441:Saccharomyces cerevisiae / Q000378:metabolism; D017423:Sequence Analysis, RNA; D012984:Software; D060388:Support Vector Machine
chemical_list: D011506:Proteins; D016601:RNA-Binding Proteins; D012313:RNA