ArticleNature methods2024
AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 202 papers.
What it found
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Who cites it
202 citing papers in PubMed.
- A pseudo-labeling approach for automated assignment of unlabeled proteins exceeding 100 amino acid residues.Magnetic resonance letters · 2027Article
- Predicting RNA and Protein:RNA Complexes with AlphaFold: Possibilities, Limitations, and Practical Guidelines.Methods in molecular biology (Clifton, N.J.) · 2027Article
- A bottleneck-first evaluation of antigen delivery platforms for therapeutic cancer vaccines.Materials today. Bio · 2026Review
- Epigenetic insights into extreme longevity in the world's oldest terrestrial animal, Jonathan.Science advances · 2026Article
- The "synthetic validity" problem in AI-generated science.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- DyProL: Dynamic Ensemble Representation Learning for Protein-Nucleic Acid Binding Site Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Comprehensive evaluation of AlphaFold/OpenFold prediction of experimentally unresolved proteins through novel metrics.Briefings in bioinformatics · 2026Article
- Comparative assessment of crystallographic and cryo-EM models in the Protein Data Bank.FEBS open bio · 2026Article
- In silico conformational dynamics of the α-actinin-2 actin-binding domain upon phosphorylation.Biophysical journal · 2026Article
- Reliability of AI Methods in Drug Discovery: Evaluation of Boltz-2 for Structure and Binding Affinity Prediction.Journal of chemical theory and computation · 2026Article
- Deciphering the Function and Structure of PA1216 as an S-Adenosyl-l-Methionine Binding Protein Using Differential Scanning Fluorimetry and Circular Dichroism.Protein science : a publication of the Protein Society · 2026Article
- Virtual Screening of Bacteriocins From Lactic Acid Bacteria Against Monkeypox DNA Polymerase: Sakacin P and Mundticin KS Emerge as Promising Candidates.Food science & nutrition · 2026Article
- Bitter taste TAS2R14 and TAS2R46 receptors bound to G proteins: comparison of cryo-EM, AlphaFold, and molecular dynamics structures.European biophysics journal : EBJ · 2026Article
- Addressing antimicrobial resistance: Current challenges, emerging strategies, and an AI-powered, community-driven approach.PNAS nexus · 2026Review
- AI-Predicted Model-Guided Rebuilding of the Experimental Structure of Mouse δ-Aminolevulinic Acid Dehydratase.International journal of molecular sciences · 2026Article
- Evaluation of AlphaFold3 for Predicting Human Heme-Binding Protein Structures.International journal of molecular sciences · 2026Article
- Challenges and Vision for Standardization of Biopolymer Data Sets for Machine Learning.Biomacromolecules · 2026Review
- PROPTIMUS LIVE: local constrained α-carbon optimization of proteins.Nucleic acids research · 2026Article
- The structural chemistry and biosynthesis of chlorophylls.RSC chemical biology · 2026Review
- Vsb1, Ypq1, and Ypq2 control dynamic cationic amino acid storage in the yeast vacuole.Life science alliance · 2026Article
142 more citing papers are in PubMed but not listed here.
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11 authors.
Funding
Abstract
Artificial intelligence-based protein structure prediction methods such as AlphaFold have revolutionized structural biology. The accuracies of these predictions vary, however, and they do not take into account ligands, covalent modifications or other environmental factors. Here, we evaluate how well AlphaFold predictions can be expected to describe the structure of a protein by comparing predictions directly with experimental crystallographic maps. In many cases, AlphaFold predictions matched experimental maps remarkably closely. In other cases, even very high-confidence predictions differed from experimental maps on a global scale through distortion and domain orientation, and on a local scale in backbone and side-chain conformation. We suggest considering AlphaFold predictions as exceptionally useful hypotheses. We further suggest that it is important to consider the confidence in prediction when interpreting AlphaFold predictions and to carry out experimental structure determination to verify structural details, particularly those that involve interactions not included in the prediction.
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Registered trials
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