HydRA: Deep-learning models for predicting RNA-binding capacity from protein interaction association context and protein sequence

Abstract

RNA-binding proteins (RBPs) control RNA metabolism to orchestrate gene expression, and dysfunctional RBPs underlie many human diseases. Proteome-wide discovery efforts predict thousands of novel RBPs, many of which lack canonical RNA-binding domains. Here, we present a hybrid ensemble RBP classifier (HydRA) that leverages information from both intermolecular protein interactions and internal protein sequence patterns to predict RNA-binding capacity with unparalleled specificity and sensitivity using support vector machine, convolutional neural networks and transformer-based protein language models. HydRA enables Occlusion Mapping to robustly detect known RNA-binding domains and to predict hundreds of uncharacterized RNA-binding domains. Enhanced CLIP validation for a diverse collection of RBP candidates reveals genome-wide targets and confirms RNA-binding activity for HydRA-predicted domains. The HydRA computational framework accelerates construction of a comprehensive RBP catalogue and expands the set of known RNA-binding protein domains. HighlightsO_LIHydRA combines protein-protein interaction and amino acid sequence information to predict RNA binding activity for 1,487 candidate genes. C_LIO_LIHydRA predicts RNA binding with higher specificity and sensitivity than current approaches, notably for RBPs without well-defined RNA-binding domains. C_LIO_LIOcclusion Mapping with HydRA enables RNA-binding domain discovery. C_LIO_LIEnhanced CLIP confirms HydRA RBP predictions with RNA-binding domain resolution. C_LI