Predicting exon criticality from protein sequence

Abstract

Alternative splicing is frequently involved in the diversification of protein function and can also be modulated for therapeutic purposes. Here we develop a predictive model, called Exon ByPASS (predicting Exon skipping Based on Protein amino acid SequenceS), to assess the criticality of exon inclusion based solely on information contained in the amino acid sequence upstream and downstream of the exon junctions. By focusing on protein sequence, Exon ByPASS predicts exon skipping independent of tissue and species in the absence of any intronic information. We validate model predictions using transcriptomic and proteomic data and show that the model can capture exon skipping in different tissues and species. Additionally, we reveal potential therapeutic opportunities by predicting synthetically skippable exons and neo-junctions arising in cancer cells.