ArticleNature communications2025
Large-scale predictions of alternative protein conformations by AlphaFold2-based sequence association.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- A Minimalist Model for Navigating Conformational Landscape Uncovers the Activation Mechanism of βACS physical chemistry Au · 2026Article
- Biasing Conformational Sampling in AlphaFold 3 and Boltz-2 via Pair Representation Scaling.Journal of chemical information and modeling · 2026Article
- Explainable AI reveals the allosteric blind spot in protein-ligand binding predictions.Cell reports. Physical science · 2026Article
- Synthetic sequence alignments as programmable probes of learned conformational landscapes in deep learning protein structure predictors.Bioinformatics (Oxford, England) · 2026Article
- Elucidating the Structure and Activation Mechanisms of GPCRs Using Modern Computational and AI Tools.Cell biochemistry and biophysics · 2026Review
- AI-based methods for simulating, sampling, and predicting protein ensembles.Current opinion in structural biology · 2026Review
- Advances and challenges in multiscale biomolecular simulations: artificial intelligence-driven paradigm shift.Quantitative biology (Beijing, China) · 2026Article
- The intrinsic disorder challenge for AlphaFold: A case study of G3BP1 and pathogenic peptide.iScience · 2026Article
- Fold-Switching Proteins.Annual review of biophysics · 2026Review
- Article
- Exploration of the structural and functional diversity in the metamorphic RfaH subfamily.bioRxiv : the preprint server for biology · 2026Article
- Defining the Active Conformation of Typical Protein Kinase Domains from Substrate-Bound PDB Structures Enables Active-State AlphaFold2 Models for All 437 Human Catalytic Protein Kinases.bioRxiv : the preprint server for biology · 2026Article
- Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time.ArXiv · 2026Article
- A dramatic protein fold switch powers a bactericidal nanomachine.bioRxiv : the preprint server for biology · 2026Article
- Article
- The transformative impact of AI-enabled AlphaFold 3: evolution, current status, and future prospects in structural biology.Frontiers in artificial intelligence · 2026Review
- Integrating AI-Based Protein Modeling and Fragment Molecular Orbital Analysis for Structure-Based Drug Design.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Challenging AlphaFold in predicting proteins with large-scale allosteric transitions.Communications chemistry · 2025Article
- Proteome-wide identification of metamorphic protein candidates using mass spectrometry.bioRxiv : the preprint server for biology · 2025Article
- Fold-switching proteins.ArXiv · 2025Article
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6 authors.
Funding
Abstract
The many successes of AlphaFold2 (AF2) have inspired methods to predict multiple protein conformations, many of which have biological significance. These methods often assume that AF2 relies on evolutionary couplings to predict alternative protein conformations, but they perform poorly on fold-switching proteins, which remodel their secondary structures and modulate their functions in response to cellular stimuli. Here we present a method designed to leverage AF2's learning of protein structure more than evolutionary couplings. This method-called CF-random-outperforms other methods for predicting alternative conformations of not only fold switchers but also dozens of other proteins that undergo rigid body motions and local conformational rearrangements. It also enables predictions of fold-switched assemblies unpredicted by AlphaFold3. Several lines of evidence suggest that CF-random sometimes works by sequence association: relating patterns from homologous sequences to a learned structural landscape. Through a blind search of thousands of Escherichia coli proteins, CF-random suggests that up to 5% switch folds.
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