Abstract
Efforts to predict interfacial residues in protein-RNA complexes have largely focused on predicting RNA-binding residues in proteins. Computational methods for predicting protein-binding residues in RNA sequences, however, are a problem that has received relatively little attention to date. Although the value of sequence motifs for classifying and annotating protein sequences is well established, sequence motifs have not been widely applied to predicting interfacial residues in macromolecular complexes. Here, we propose a novel sequence motif-based method for "partner-specific" interfacial residue prediction. Given a specific protein-RNA pair, the goal is to simultaneously predict RNA binding residues in the protein sequence and protein-binding residues in the RNA sequence. In 5-fold cross validation experiments, our method, PS-PRIP, achieved 92% Specificity and 61% Sensitivity, with a Matthews correlation coefficient (MCC) of 0.58 in predicting RNA-binding sites in proteins. The method achieved 69% Specificity and 75% Sensitivity, but with a low MCC of 0.13 in predicting protein binding sites in RNAs. Similar performance results were obtained when PS-PRIP was tested on two independent "blind" datasets of experimentally validated protein- RNA interactions, suggesting the method should be widely applicable and valuable for identifying potential interfacial residues in protein-RNA complexes for which structural information is not available. The PS-PRIP webserver and datasets are available at: http://pridb.gdcb.iastate.edu/PSPRIP/.
Highlights
Despite the important roles of protein-RNA interactions in many biological processes, including transcription, translation, viral replication and pathogen resistance [1,2], the mechanisms and regulation of protein-RNA recognition are not yet fully understood
To evaluate whether an interface motif lookup table can be used to predict interfacial residues in specific protein-RNA pairs, we first performed preliminary experiments in which we tested the effect of varying the length of protein motifs from 4 to 6 amino acids, and the length of RNA motifs from 4 to 8 ribonucleotides
As expected, using shorter motifs resulted in a larger number of false positive predictions, whereas using longer motifs resulted in larger number of false negative predictions
Summary
Despite the important roles of protein-RNA interactions in many biological processes, including transcription, translation, viral replication and pathogen resistance [1,2], the mechanisms and regulation of protein-RNA recognition are not yet fully understood. Highthroughput (HTP) methods for identifying the in vivo targets of specific RNA binding proteins and the RNA motifs they bind - have provided a wealth of information about the determinants of sequence recognition in protein-RNA complexes [4,5,6]. Data from both the PDB and HTP experiments have been exploited to develop several computational methods for predicting interfacial residues in protein-RNA complexes [reviewed in 7-10] as well as a few methods for predicting interaction partners in protein-RNA complexes and interaction networks [reviewed in 11-13]. Taken in the current study, is to exploit short sequence motifs that occur in the interfaces of known protein-RNA complexes
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