RNAcontext: A New Method for Learning the Sequence and Structure Binding Preferences of RNA-Binding Proteins

Metazoan genomes encode hundreds of RNA-binding proteins (RBPs). These proteins regulate post-transcriptional gene expression and have critical roles in numerous cellular processes including mRNA splicing, export, stability and translation. Despite their ubiquity and importance, the binding preferences for most RBPs are not well characterized. In vitro and in vivo studies, using affinity selection-based approaches, have successfully identified RNA sequence associated with specific RBPs; however, it is difficult to infer RBP sequence and structural preferences without specifically designed motif finding methods. In this study, we introduce a new motif-finding method, RNAcontext, designed to elucidate RBP-specific sequence and structural preferences with greater accuracy than existing approaches. We evaluated RNAcontext on recently published in vitro and in vivo RNA affinity selected data and demonstrate that RNAcontext identifies known binding preferences for several control proteins including HuR, PTB, and Vts1p and predicts new RNA structure preferences for SF2/ASF, RBM4, FUSIP1 and SLM2. The predicted preferences for SF2/ASF are consistent with its recently reported in vivo binding sites. RNAcontext is an accurate and efficient motif finding method ideally suited for using large-scale RNA-binding affinity datasets to determine the relative binding preferences of RBPs for a wide range of RNA sequences and structures.Author Summary: Many disease-associated mutations do not change the protein sequence of genes; instead they change the instructions on how a gene's mRNA transcript should be processed. Translating these instructions allows us to better understand the connection between these mutations and disease. RNA-binding proteins (RBP) perform this translation by recognizing particular “phrases” that occupy short regions of the transcript. Recognition occurs by the binding of the RBP to the phrase. The set of phrases bound by a particular RBP is defined by the RNA base content of the binding site as well as the 3D configuration of these bases. Because it is impossible to assess RBP binding to every possible phrase, we have developed a mathematical model called RNAcontext that can be trained by measuring RBP binding strength on one set of phrases. Once trained, this model can then be used to accurately predict binding strength to any possible phrase. Compared to previously described methods, RNAcontext learns a more precise description of the 3D shapes of binding sites. This precision translates into more accurate generalization of RBP binding preferences to new phrases and allows us to make new discoveries about the binding preferences of well-studied RBPs.