HydRA: Deep-learning models for predicting RNA-binding capacity from protein interaction association context and protein sequence
Wenhao Jin, Kristopher W. Brannan, Katannya Kapeli, Samuel S. Park, Hui Tan, Maya L. Gosztyla, Mayuresh Mujumdar, Joshua Ahdout, Bryce Henroid, Katherine Rothamel, Joy S. Xiang, Limsoon Wong, G Yeo
Abstract
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.