ArticleNature communications2025
AQuaRef: machine learning accelerated quantum refinement of protein structures.
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 9 papers.
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Who cites it
9 citing papers in PubMed.
- Article
- MAPLE: a machine-learning force-field-native platform for automated reaction modeling and enzyme design.Chemical science · 2026Article
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Environmental Contributions to Proton Sharing in Protein Low-Barrier Hydrogen Bonds.Biochemistry · 2026Article
- The Untangle Challenge for accurate ensemble models.bioRxiv : the preprint server for biology · 2026Article
- AIMNet2-NSE: A Transferable Reactive Neural Network Potential for Open-Shell Chemistry.Angewandte Chemie (International ed. in English) · 2026Article
- Environmental Contributions to Proton Sharing in Protein Low-Barrier Hydrogen Bonds.bioRxiv : the preprint server for biology · 2025Article
- Artificial intelligence in structural biology: Preface.Structural dynamics (Melville, N.Y.) · 2025Article
- Ten rules for a structural bioinformatic analysis.PLoS computational biology · 2025Article
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12 authors.
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Abstract
Cryo-EM and X-ray crystallography provide crucial experimental data for obtaining atomic-detail models of biomacromolecules. Refining these models relies on library-based stereochemical data, which, in addition to being limited to known chemical entities, do not include meaningful noncovalent interactions. Quantum mechanical (QM) calculations could alleviate these issues but are too expensive for large molecules. Here we present a novel AI-enabled Quantum Refinement (AQuaRef) based on AIMNet2 machine learned interatomic potential (MLIP) mimicking QM at substantially lower computational costs. By refining 41 cryo-EM and 30 X-ray structures, we show that this approach yields atomic models with superior geometric quality compared to standard techniques, while maintaining an equal or better fit to experimental data. Notably, AQuaRef aids in determining proton positions, as illustrated in the challenging case of short hydrogen bonds in the parkinsonism-associated human protein DJ-1 and its bacterial homolog YajL.
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Registered trials
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