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

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, accelerating construction of a comprehensive RBP catalog and expands the diversity of RNA-binding associated domains.