ArticleActa crystallographica. Section D, Structural biology2025
Has AlphaFold3 achieved success for RNA?
Article in Acta crystallographica. Section D, Structural biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
26 citing papers in PubMed.
- Hierarchical breakdown of RNA structure prediction in CASP16: from reliable local helices to speculative multimer assembly.Bioinformatics (Oxford, England) · 2026Article
- 4MRNA: a new approach for nucleic acid molecular replacement using models with diverse parameter patterns.RNA (New York, N.Y.) · 2026Article
- Building RNA coarse-grained force fields: Design principles and training strategies.Biophysical journal · 2026Review
- Fast activity prediction of chemically modified siRNAs via structure-based energy calculations and inference-augmented tabular deep learning.Molecular therapy. Nucleic acids · 2026Article
- Integrated NMR/MD investigation reveals differences after reweighting in conformational ensembles of the GAAG and GCAA tetraloops.RNA (New York, N.Y.) · 2026Article
- Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families.BioData mining · 2026Review
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Review
- Computational approaches for RNA structure prediction and design.Cell reports. Physical science · 2026Article
- Cat_Wiz: a stereochemistry-guided toolkit for locating, diagnosing, and annotating Mg2+ ions in RNA structures.Nucleic acids research · 2026Article
- Crystal structure of a tRNA acceptor-stem mimic at 1.94 Å resolution.Acta crystallographica. Section F, Structural biology communications · 2026Article
- Review
- Deep Learning in Modeling Tools for Structural Insights into Protein-RNA Complexes, Bridging Computational and Spectroscopic Approaches.Methods in molecular biology (Clifton, N.J.) · 2026Article
- The RNA-Puzzles Assessments of RNA-Only Targets in CASP16.Proteins · 2026Article
- Integrating rna structure and protein interactions to uncover the mechanisms of viral and cellular ires function.Biology direct · 2025Article
- Benchmarking all-atom biomolecular structure prediction with FoldBench.Nature communications · 2025Article
- Disease-related miRNA mutations are associated with mature miRNA secondary structure changes.Biophysical journal · 2025Article
- Crystal structure of the class V GTP-binding RNA aptamer bound to its ligand: GTP recognition by a topologically complex intermolecular G-quadruplex.Nucleic acids research · 2025Article
- Influence of contact map topology on RNA structure prediction.Nucleic acids research · 2025Article
- RNAdvisor 2: A unified platform for RNA 3D model quality assessment using metrics, scoring functions, and meta-metrics.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Predicting the 3D structure of RNA is a significant challenge despite ongoing advancements in the field. Although AlphaFold has successfully addressed this problem for proteins, RNA structure prediction raises difficulties due to the fundamental differences between proteins and RNA, which hinder its direct adaptation. The latest release of AlphaFold, AlphaFold3, has broadened its scope to include multiple different molecules such as DNA, ligands and RNA. While the AlphaFold3 article discussed the results for the last CASP-RNA data set, the scope of its performance and the limitations for RNA are unclear. In this article, we provide a comprehensive analysis of the performance of AlphaFold3 in the prediction of 3D structures of RNA. Through an extensive benchmark over five different test sets, we discuss the performance and limitations of AlphaFold3. We also compare its performance with ten existing state-of-the-art ab initio, template-based and deep-learning approaches. Our results are freely available on the EvryRNA platform at https://evryrna.ibisc.univ-evry.fr/evryrna/alphafold3/.
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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 Socratic graph.