Atomic resolution ensembles of intrinsically disordered proteins with Alphafold
Open accessPublished version of a preprint
Abstract
Intrinsically disordered proteins are ubiquitous in biological systems and play essential roles in a wide range of biological processes and diseases. Despite recent advances in high-resolution structural biology techniques and breakthroughs in deep learning-based protein structure prediction, accurately determining structural ensembles of IDPs at atomic resolution remains a major challenge. Here, we introduce bAIes, a Bayesian framework that integrates AlphaFold2 predictions with physico-chemical molecular mechanics force fields to generate accurate atomic-resolution ensembles of IDPs. We show that bAIes produces structural ensembles that match a wide range of high- and low-resolution experimental data across diverse systems, achieving accuracy comparable to atomistic molecular dynamics simulations but at a fraction of their computational cost. Furthermore, bAIes outperforms state-of-the-art IDP models based on coarse-grained potentials as well as deep-learning approaches. Our findings pave the way for integrating structural information from modern deep-learning approaches with molecular simulations, advancing ensemble-based understanding of disordered proteins.
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Cites
Highly accurate protein structure prediction with AlphaFold
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“Lookingahead,the growing number of structure prediction tools like Alphafold3 30 and Boltz2 67 could further expand the range of applications of our approach.”
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- Authors
- Schnapka, V., Morozova, T. I., Sen, S., & Bonomi, M.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- École Normale Supérieure de Lyon (FR)
- Corresponding author
- Bonomi, Massimiliano
- Venue
- Nature Communications
- Published
- 2026-02-05
- Volume
- 17
- Issue
- 1
- Pages
- 2399
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC12982490
- OpenAlex
- W7127941162
- Cited by
- 23
- References
- 10
- Citations by year
- 2026: 18 · 2025: 5
- FWCI
- 37.20
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nature Communications (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- EC | Horizon 2020 Framework Programme (101086685 – bAIes)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- intrinsically disordered proteins, structural ensembles, AlphaFold2, molecular mechanics force field, bays, protein structure prediction, coarse-grained potentials
- MeSH terms
- Deep Learning, Bayes Theorem, Protein Conformation, Protein Folding, Molecular Dynamics Simulation, Intrinsically Disordered Proteins
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