From binary labels to dynamic landscapes: The evolving computational prediction of protein-RNA interactions through tasks and deep learning paradigms.
TL;DR
This study reviews the methodological evolution from conventional machine learning to deep learning, graph neural networks and large-scale pre-trained language models, and compares their differences in data preparation, evaluation protocols and generalization behavior, and places particular emphasis on recent advances in structure-aware and condition-aware models.
Abstract
Protein-RNA interactions (RPIs) stand for the central process in post-transcriptional regulation and have catalyzed a fast proliferation of computational approaches in recent years. Adopting a task-oriented classification method, RPIs calculation prediction schemes proposed over the period 2010-2025 fall into five primary categories: RNA-binding protein (RBP) classification, RPIs prediction, binding site and binding profile modeling on RNA, residue-level RNA-binding interface prediction on proteins, and quantitative estimation of binding affinity and mutation effects. This study reviews the methodological evolution from conventional machine learning to deep learning, graph neural networks and large-scale pre-trained language models, and compares their differences in data preparation, evaluation protocols and generalization behavior. Particular emphasis is placed on recent advances in structure-aware and condition-aware models, as well as learning in low-data regimes. Finally, the study outlines practical recommendations for field-wide benchmarking and looks ahead to the integration with spatial omics and the development of dynamic, generative landscapes of RPIs to better empower biomedical research.