Title: AQuaRef: machine learning accelerated quantum refinement of protein structures.
Authors: Roman Zubatyuk, Malgorzata Biczysko, Kavindri Ranasinghe, Nigel W. Moriarty, Hatice Gokcan, Holger Kruse, Billy K. Poon, Paul D. Adams, Mark P. Waller, Adrian E. Roitberg, Olexandr Isayev & Pavel V. Afonine
DOI: 10.1038/s41467-025-64313-1
Date: 2025-10-17
Working Group: WG1, WG2
Grant Period: GP3
Grant Period Goal (number): 4
Covered deliverables from the MoU (number): 1.6.3
Countries involved: Poland, USA, Australia
Number of female/young/ITC coauthors: 2/0/1
Is the publication open access?: Yes
Is the publication co-lead by a YRI?: No
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.
