Abstract
BackgroundRNA-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.ResultsWe 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.ConclusionsOur 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/.
Highlights
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
Computational studies of RNA-protein interactions have largely focused on the “interface prediction problem”, i.e., the problem of identifying the amino acid residues in a protein that are likely to bind to an RNA [20,21,22]
As a first step towards computational construction of RPI networks, we focused on the following question: Given the sequence of an RNA-binding protein, can we predict whether it interacts with a given RNA sequence? In developing sequence-based methods to answer this question, we considered several reduced and alternative alphabet representations of the input protein and RNA sequences
Summary
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. Computational studies of RNA-protein interactions have largely focused on the “interface prediction problem”, i.e., the problem of identifying the amino acid residues in a protein that are likely to bind to an RNA [20,21,22]. Large-scale experimental analyses of RPIs such as RNAcompete [23], RIP-Chip [24], HITS-CLIP [25], PAR-CLIP [8] are providing valuable data about networks of RNA-protein interactions, these experiments are expensive and time-consuming. Given the limited number of structurally characterized RNA-protein complexes available in the PDB [26] at present (1,092 as of June 13, 2011) and the current availability of only one database of ncRNA-protein interactions (NPInter [27]), it would be especially valuable to develop sequence-based methods that can be used to identify potential RNA-protein partners in the absence of experimental structural information regarding either partner
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
