Modeling and Predicting the Activities of Trans-Acting Splicing Factors with Machine Learning

Alternative splicing (AS) is generally regulated by trans-splicing factors that specifically bind to cis-elements in pre-mRNAs. The human genome encodes similar to 1,500 RNA binding proteins (RBPs) that potentially regulate AS, yet their functions remain largely unknown. To explore their potential activities, we fused the putative functional domains of RBPs to a sequence-specific RNA-binding domain and systemically analyzed how these engineered factors affect splicing. We discovered that similar to 80% of low-complexity domains in endogenous RBPs displayed distinct context-dependent activities in regulating splicing, indicating that AS is under more extensive regulation than previously expected. We developed a machine learning approach to classify and predict the activities of RBPs based on their sequence compositions and further validated this model using endogenous RBPs and synthetic polypeptides. These results represent a systematic inspection, modeling, prediction, and validation of how RBP sequences affect their activities in controlling splicing, paving the way for de novo engineering of artificial splicing factors.