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

RNA-binding proteins (RBPs) control RNA metabolism to orchestrate gene expression and, when dysfunctional, underlie human diseases. Proteome-wide discovery efforts predict thousands of RBP candidates, many of which lack canonical RNA-binding domains (RBDs). Here, we present a hybrid ensemble RBP classifier (HydRA), which 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 machines (SVMs), convolutional neural networks (CNNs), and Transformer-based protein language models. Occlusion mapping by HydRA robustly detects known RBDs and predicts hundreds of uncharacterized RNA-binding associated domains. Enhanced CLIP (eCLIP) for HydRA-predicted RBP candidates reveals transcriptome-wide RNA targets and confirms RNA-binding activity for HydRA-predicted RNA-binding associated domains. HydRA accelerates construction of a comprehensive RBP catalog and expands the diversity of RNA-binding associated domains. [Display omitted] •HydRA is a deep-learning model combining PPI and sequence features to predict RBPs•Occlusion mapping with HydRA enables RBD discovery•HydRA predicts RNA-binding activity for 1,487 candidate proteins and 76 candidate RBDs•Enhanced CLIP confirms HydRA RBP predictions with RBD resolution Jin et al. developed HydRA, an RBP classifier that leverages protein interactions and sequence patterns. Utilizing machine-learning and deep-learning techniques, HydRA accurately predicts RNA-binding capacity and identifies numerous uncharacterized RNA-binding proteins and domains. eCLIP validation confirms the RNA-binding activity, expanding the catalog of known RNA-binding proteins and domains.