ReviewCurrent opinion in structural biology2025
Proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction.
Review in Current opinion in structural biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- Evaluation of methods for AlphaFold-based integrative modeling.bioRxiv : the preprint server for biology · 2026Article
- Biasing Conformational Sampling in AlphaFold 3 and Boltz-2 via Pair Representation Scaling.Journal of chemical information and modeling · 2026Article
- PETIMOT: a novel framework for inferring protein motions from sparse data using SE(3)-equivariant graph neural networks.Acta crystallographica. Section D, Structural biology · 2026Article
- AlphaFold reveals but sometimes distorts an organizational principle of protein folding.bioRxiv : the preprint server for biology · 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
- Article
- The intrinsic disorder challenge for AlphaFold: A case study of G3BP1 and pathogenic peptide.iScience · 2026Article
- Fold-Switching Proteins.Annual review of biophysics · 2026Review
- Conformational Flexibility of Transmembrane Helices: How it Works and Where it Matters.Chemical reviews · 2026Review
- 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
- CPE-Pro: A Structure-Sensitive Deep Learning Method for Protein Representation and Origin Evaluation.Interdisciplinary sciences, computational life sciences · 2026Article
- Comparative Analysis of Deep Learning-Based Algorithms for Peptide Structure Prediction.Proteins · 2026Article
- Cafestol-derivatives as potential FXR agonists and CYP7A1 inhibitors and their impact on hypercholesterolemia: an in silico study.Scientific reports · 2026Article
- A Quantum Framework for Protein Binding-Site Structure Prediction on Utility-Level Quantum Processors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Comparative Analysis of AlphaFold2 Models and Intrinsic Disorder Illuminates Structural Divergence as a Symptom of Functional Divergence Across the Calmodulin Superfamily.Journal of molecular evolution · 2026Article
- Article
- Fine-tuning AlphaFold with limited cryo-EM observations.Communications chemistry · 2026Article
- Article
- Enhancing protein structure prediction: evaluating the role of amino acid physicochemical features in homology search.Briefings in bioinformatics · 2026Article
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3 authors.
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Abstract
In recent years, advances in artificial intelligence (AI) have transformed structural biology, particularly protein structure prediction. Though AI-based methods, such as AlphaFold (AF), often predict single conformations of proteins with high accuracy and confidence, predictions of alternative folds are often inaccurate, low-confidence, or simply not predicted at all. Here, we review three blind spots that alternative conformations reveal about AF-based protein structure prediction. First, proteins that assume conformations distinct from their training-set homologs can be mispredicted. Second, AF overrelies on its training set to predict alternative conformations. Third, degeneracies in pairwise representations can lead to high-confidence predictions inconsistent with experiment. These weaknesses suggest approaches to predict alternative folds more reliably.
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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.