ArticleNature communications2025
AlphaFold prediction of structural ensembles of disordered proteins.
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 52 papers.
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Who cites it
52 citing papers in PubMed.
- Review
- A practical guide to investigating biomolecular condensates: a comment from the plant community.Science China. Life sciences · 2026Review
- AF-CALVADOS: AlphaFold-guided simulations of multi-domain proteins at the proteome level.Protein science : a publication of the Protein Society · 2026Article
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families.BioData mining · 2026Review
- More protein-ligand data are needed for AlphaFold-like models to enable drug discovery.Current opinion in structural biology · 2026Review
- Advances and challenges in multiscale biomolecular simulations: artificial intelligence-driven paradigm shift.Quantitative biology (Beijing, China) · 2026Article
- Structural and immunogenic evaluation of silk proteins from Bombyx mori using advanced bioinformatics and deep learning for biomaterials applications.Journal, genetic engineering & biotechnology · 2026Article
- In Search of Shape in the Unshaped: Constructing Ensembles of Intrinsically Disordered Proteins.Biophysics reviews · 2026Article
- Role of Small Membrane Proteins in the Green Sulfur Bacterial Reaction Center.ACS physical chemistry Au · 2026Article
- The intrinsic disorder challenge for AlphaFold: A case study of G3BP1 and pathogenic peptide.iScience · 2026Article
- Functional flexibility in bacterial hub proteins is driven by proteome expansion.Scientific reports · 2026Article
- Transient tertiary structure in intrinsically disordered proteins revealed by multithermal enhanced sampling.Nature communications · 2026Article
- MuFGPS: enhancing liquid-liquid phase separation protein prediction through multi-level features and ensemble learning.Briefings in bioinformatics · 2026Article
- Article
- Toward a unified framework for determining conformational ensembles of disordered proteins.Nature methods · 2026Review
- Production and characterization of rNGFSP: a recombinant fusion immunogen eliciting dual anti-NGF and anti-Substance P therapeutic antibodies for Degenerative Joint Disease.Biotechnology reports (Amsterdam, Netherlands) · 2026Article
- Automatic forward model parameterization with Bayesian inference of conformational populations.APL machine learning · 2026Article
- Accurate conformational ensembles of intrinsically disordered proteins using reweighting based on NMR chemical shifts.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Atomic resolution ensembles of intrinsically disordered proteins with Alphafold.Nature communications · 2026Article
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4 authors.
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Abstract
Deep learning methods of predicting protein structures have reached an accuracy comparable to that of high-resolution experimental methods. It is thus possible to generate accurate models of the native states of hundreds of millions of proteins. An open question, however, concerns whether these advances can be translated to disordered proteins, which should be represented as structural ensembles because of their heterogeneous and dynamical nature. To address this problem, we introduce the AlphaFold-Metainference method to use AlphaFold-derived distances as structural restraints in molecular dynamics simulations to construct structural ensembles of ordered and disordered proteins. The results obtained using AlphaFold-Metainference illustrate the possibility of making predictions of the conformational properties of disordered proteins using deep learning methods trained on the large structural databases available for folded proteins.
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