De Novo Prediction of PTBP1 Binding and Splicing Targets Reveals Unexpected Features of Its RNA Recognition and Function

The splicing regulator Polypyrimidine Tract Binding Protein (PTBP1) has four RNA binding domains that each binds a short pyrimidine element, allowing recognition of diverse pyrimidine-rich sequences. This variation makes it difficult to evaluate PTBP1 binding to particular sites based on sequence alone and thus to identify target RNAs. Conversely, transcriptome-wide binding assays such as CLIP identify many in vivo targets, but do not provide a quantitative assessment of binding and are informative only for the cells where the analysis is performed. A general method of predicting PTBP1 binding and possible targets in any cell type is needed. We developed computational models that predict the binding and splicing targets of PTBP1. A Hidden Markov Model (HMM), trained on CLIP-seq data, was used to score probable PTBP1 binding sites. Scores from this model are highly correlated (ρ = −0.9) with experimentally determined dissociation constants. Notably, we find that the protein is not strictly pyrimidine specific, as interspersed Guanosine residues are well tolerated within PTBP1 binding sites. This model identifies many previously unrecognized PTBP1 binding sites, and can score PTBP1 binding across the transcriptome in the absence of CLIP data. Using this model to examine the placement of PTBP1 binding sites in controlling splicing, we trained a multinomial logistic model on sets of PTBP1 regulated and unregulated exons. Applying this model to rank exons across the mouse transcriptome identifies known PTBP1 targets and many new exons that were confirmed as PTBP1-repressed by RT-PCR and RNA-seq after PTBP1 depletion. We find that PTBP1 dependent exons are diverse in structure and do not all fit previous descriptions of the placement of PTBP1 binding sites. Our study uncovers new features of RNA recognition and splicing regulation by PTBP1. This approach can be applied to other multi-RRM domain proteins to assess binding site degeneracy and multifactorial splicing regulation.Author Summary: A key step in the regulation of mammalian genes is the splicing of the messenger RNA precursor to produce a mature mRNA that can be translated into a particular protein needed by the cell. Through the process of alternative splicing, mRNAs encoding different proteins can be derived from the same primary gene transcript. The regulation of this process plays essential roles in the development of differentiated tissues and is mediated by special pre-mRNA binding proteins. To understand how these proteins control gene expression, one must characterize what they recognize in RNA and identify these binding sites across the genome in order to predict their targets. Models that allow this prediction are essential to understanding developmental regulatory programs and their perturbation by disease causing mutations. In this study, we use statistical methods to build models of RNA recognition by the important splicing regulator PTBP1 and then apply these models to predict PTBP1 regulation of new gene transcripts. We show that PTBP1 has different specificity for RNA than was previously recognized and that its target exons are more diverse than was known before. There are many similar splicing regulators in mammalian cells, and these analyses provide a general framework for the computational analysis of their RNA binding and target identification.