DEPfold: RNA Secondary Structure Prediction as Dependency Parsing.
KE WANG Shay B Cohen
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
RNA secondary structure prediction, Dependency parsing, Biaffine attention, Pseudoknots, Pretrained Model, Deep learning
DEPfold reframes RNA secondary structure prediction as dependency parsing. Using structure transformation, Biaffine parser, and optimal decoding, it outperforms existing methods, especially for pseudoknots and long-range interactions.