ArticleNature communications2024
AlphaFold predictions of fold-switched conformations are driven by structure memorization.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers.
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90 citing papers in PubMed.
- The Advantages of AI for Computational Protein Studies and Looking Ahead at the Next Challenges: Single Structures Are Not Enough.Journal of molecular biology · 2026Review
- A curated benchmark for cofolding models on kinase conformational states.npj drug discovery · 2026Article
- The partner gene modulates response to asciminib in ABL1-rearranged Acute Lymphoblastic Leukemia.Neoplasia (New York, N.Y.) · 2026Article
- Functional and Structural Characterization of theACS omega · 2026Article
- Explainable AI reveals the allosteric blind spot in protein-ligand binding predictions.Cell reports. Physical science · 2026Article
- ESMDynamic: Fast and accurate prediction of protein dynamic contact maps from single sequences.Nature communications · 2026Article
- Synthetic sequence alignments as programmable probes of learned conformational landscapes in deep learning protein structure predictors.Bioinformatics (Oxford, England) · 2026Article
- Innovative approaches to therapeutic target discovery amid the global challenge of antimicrobial resistance.Journal of computer-aided molecular design · 2026Review
- AlphaFold reveals but sometimes distorts an organizational principle of protein folding.bioRxiv : the preprint server for biology · 2026Article
- Artificial Intelligence and Protein Design: A retrospective study on 20-year emerging trends and core research areas from bibliometric perspectives.Probiotics and antimicrobial proteins · 2026Article
- Benchmarking AI Protein Structure Predictors Reveals a Persistent Bias in Multi-State Proteins.bioRxiv : the preprint server for biology · 2026Article
- The influence of ligands on AlphaFold3 prediction of cryptic pockets.Communications biology · 2026Article
- Improving Protein Structure Prediction Using Integrative Cryo-EM and Ion Mobility Mass Spectrometry Modeling.Journal of chemical theory and computation · 2026Article
- Force-Induced Ankle Opening Reveals Mechanical Stabilization of the Ankle of Human β-Cardiac Myosin.ACS nano · 2026Article
- The latest AI breakthroughs in structural biology: protein binder design and conformational state prediction.Communications biology · 2026Article
- Fold-Switching Proteins.Annual review of biophysics · 2026Review
- Hidden structural states of proteins revealed by conformer selection.Nature communications · 2026Article
- Novel cryptic ADAMTS13 epitopes uncover a distinct open ADAMTS13 conformation in immune-mediated TTP.Haematologica · 2026Article
- Exploration of the structural and functional diversity in the metamorphic RfaH subfamily.bioRxiv : the preprint server for biology · 2026Article
- Bias in the AlphaFold3 prediction of ligand-induced domain motion in enzymes.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
30 more citing papers are in PubMed but not listed here.
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7 authors.
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Abstract
Recent work suggests that AlphaFold (AF)-a deep learning-based model that can accurately infer protein structure from sequence-may discern important features of folded protein energy landscapes, defined by the diversity and frequency of different conformations in the folded state. Here, we test the limits of its predictive power on fold-switching proteins, which assume two structures with regions of distinct secondary and/or tertiary structure. We find that (1) AF is a weak predictor of fold switching and (2) some of its successes result from memorization of training-set structures rather than learned protein energetics. Combining >280,000 models from several implementations of AF2 and AF3, a 35% success rate was achieved for fold switchers likely in AF's training sets. AF2's confidence metrics selected against models consistent with experimentally determined fold-switching structures and failed to discriminate between low and high energy conformations. Further, AF captured only one out of seven experimentally confirmed fold switchers outside of its training sets despite extensive sampling of an additional ~280,000 models. Several observations indicate that AF2 has memorized structural information during training, and AF3 misassigns coevolutionary restraints. These limitations constrain the scope of successful predictions, highlighting the need for physically based methods that readily predict multiple protein conformations.
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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.