DEPfold: RNA Secondary Structure Prediction as Dependency Parsing.

Abstract

RNA secondary structure prediction is critical for understanding RNA function but remains challenging due to complex structural elements like pseudoknots and limited training data. We introduce DEPfold, a novel deep learning approach that re-frames RNA secondary structure prediction as a dependency parsing problem. DEPfold presents three key innovations: (1) a biologically motivated transformation of RNA structures into labeled dependency trees, (2) a biaffine attention mechanism for joint prediction of base pairings and their types, and (3) an optimal tree decoding algorithm that enforces valid RNA structural constraints. Unlike traditional energy-based methods, DEPfold learns directly from annotated data and leverages pretrained language models to predict RNA structure. We evaluate DEPfold on both within-family and cross-family RNA datasets, demonstrating significant performance improvements over existing methods. DEPfold shows strong performance in cross-family generalization when trained on data augmented by traditional energy-based models, outperforming existing methods on the bpRNAnew dataset. This demonstrates DEPfold’s ability to effectively learn structural information beyond what traditional methods capture. Our approach bridges natural language processing (NLP) with RNA biology, providing a computationally efficient and adaptable tool for advancing RNA structure prediction and analysis