ArticleBriefings in bioinformatics2022
Protein-RNA interaction prediction with deep learning: structure matters.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed, 86 citations in OpenAlex.
- Bridging technical innovation and computational advances in studies of RNA-protein assemblies.Nature reviews. Genetics · 2026Review
- Review
- Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity.Journal of cheminformatics · 2026Article
- DGSite: A Well-Calibrated Deep Learning Framework for Protein-RNA Binding Site Prediction.ACS omega · 2026Article
- Differentiation of RNA-protein docking structures through molecular dynamics simulation and machine learning methods.Briefings in bioinformatics · 2026Article
- Deep learning for regulatory genomics: a survey of models, challenges, and applications.Bioinformatics advances · 2026Review
- Computational understanding of non-coding RNA pairwise interactions.Frontiers in artificial intelligence · 2026Article
- Interpretable prediction of nucleic acid-binding proteins using a protein language model.Bioinformatics advances · 2026Article
- RNApedia: a database of structural protein-RNA interactions.Frontiers in bioinformatics · 2026Article
- Mapping evolutionary paradigm of Oropouche virus driven by dinucleotide bias and context-dependent codon bias.Virulence · 2025Article
- Evolutionary paradigm of distinct viral coding regions of BVDV associated with different nucleotide usage patterns.BMC veterinary research · 2025Article
- diPaRIS: Dynamic and Interpretable Protein-RNA Interactions Prediction With U-Shaped Network and Novel Structure Encoding.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Molecular dynamics simulations of proteins: an in-depth review of computational strategies, structural insights, and their role in medicinal chemistry and drug development.Biological cybernetics · 2025Review
- Gaining insights into epigenetic memories through artificial intelligence and omics science in plants.Journal of integrative plant biology · 2025Review
- Review
- Evaluation of novel computational methods to identify RNA-binding protein footprints from structural data.RNA (New York, N.Y.) · 2025Article
- Learning Universal Representations of Intermolecular Interactions with ATOMICA.bioRxiv : the preprint server for biology · 2025Article
- LncRNA-Protein Interactions: A Key to Deciphering LncRNA Mechanisms.Biomolecules · 2025Review
- LncRNA-Protein Interactions: A Key to Deciphering LncRNA Mechanisms.Biomolecules · 2025Review
- Research on Plant RNA-Binding Protein Prediction Method Based on Improved Ensemble Learning.Biology · 2025Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein-RNA interactions are of vital importance to a variety of cellular activities. Both experimental and computational techniques have been developed to study the interactions. Because of the limitation of the previous database, especially the lack of protein structure data, most of the existing computational methods rely heavily on the sequence data, with only a small portion of the methods utilizing the structural information. Recently, AlphaFold has revolutionized the entire protein and biology field. Foreseeably, the protein-RNA interaction prediction will also be promoted significantly in the upcoming years. In this work, we give a thorough review of this field, surveying both the binding site and binding preference prediction problems and covering the commonly used datasets, features and models. We also point out the potential challenges and opportunities in this field. This survey summarizes the development of the RNA-binding protein-RNA interaction field in the past and foresees its future development in the post-AlphaFold era.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.