Aug 2026· Current Opinion in Structural Biology· Vol 101, pp.
103362
· 0 citations· 55 references
Medicine
TL;DR
These approaches improve generalisability, reduce reliance on deep evolutionary information, and enable proteome-scale prediction of RNA-binding residues, providing a route to map and interpret the molecular logic of protein-RNA interactions.
Abstract
RNA-binding proteins (RBPs) are essential across biology, from viruses to complex multicellular organisms. They regulate gene expression and cellular responses, making RNA recognition central to understanding health and disease. Biochemical, biophysical, and structural studies have defined core principles of RNA binding, but recent RNA interactome surveys have expanded the RBP repertoire and revealed many noncanonical RNA-binding regions. This diversity demands highly scalable predictive methods. Here, we review machine learning predictors built on protein language models and structure-aware representations. These approaches improve generalisability, reduce reliance on deep evolutionary information, and enable proteome-scale prediction of RNA-binding residues, providing a route to map and interpret the molecular logic of protein-RNA interactions.
The state of the art in RNA structure prediction is reviewed, covering key training datasets, community benchmarks, and the performance of current models, as well as perspectives on integrating other data modalities, such as chemical probing signals and RNA modifications, as well as the emerging role of generative mode...
Lambert Moyon, Annalisa Marsico· Current Opinion in Structura...· 0 citations
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...
This mini review traces the evolution of AI-driven methods in protein research, from early residue-contact prediction using coevolutionary information to transformative breakthroughs, the rise of protein language models (PLMs), and the emerging era of generative design and functional modeling.
Guodong Min, Huan Peng· Methods in molecular biology· 0 citations
DeepPNI is a deep learning regression model that integrates sequence- and structure-based features to estimate mutation-induced changes in binding free energy in protein–nucleic acid complexes, developed using a comprehensive dataset of 1754 mutations spanning protein–DNA and protein–RNA complexes.
A long-context protein language model is introduced, trained on entire viral genomes, leveraging a biologically informed sparse attention mechanism in which inter-protein links are inferred computationally and used as sparsity priors.
T. Dejean, Barbra D. Ferrell, Zachary D. Schreiber et al.· GigaScience· 0 citations
This work proposes an experimentally tuned protein-RNA score function that can be directly implemented into ROSETTA and establishes a framework to efficiently fine-tune ROSETTA score functions for any protein-class interaction using Bayesian Optimization.
Joe Bailey, Nathan Phan, Søren C. Spina et al.· bioRxiv· 0 citations
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