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Updated: Jan 14, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

AQuaRef: machine learning accelerated quantum refinement of protein structures.

Roman Zubatyuk¹, Malgorzata Biczysko², Kavindri Ranasinghe³

¹Department of Chemistry, Carnegie Mellon University, Pittsburgh, PA, USA.

Nature Communications

|October 17, 2025

Summary

This summary is machine-generated.

A new AI method, AQuaRef, refines biomacromolecular models using quantum mechanics at lower costs. This approach improves model quality and accurately determines proton positions, aiding in understanding protein structures.

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Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures

Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures

Published on: December 1, 2020

Area of Science:

Structural Biology
Computational Chemistry
Artificial Intelligence

Background:

Macromolecular models from cryo-electron microscopy (cryo-EM) and X-ray crystallography require refinement.
Current refinement methods use limited stereochemical data and miss noncovalent interactions.
Quantum mechanical (QM) calculations offer better accuracy but are computationally expensive for large biomolecules.

Purpose of the Study:

To introduce a novel AI-enabled Quantum Refinement (AQuaRef) method.
To mimic QM calculations at reduced computational cost for biomolecular structure refinement.
To improve the geometric quality and experimental fit of atomic models.

Main Methods:

Developed AQuaRef utilizing the AIMNet2 machine learned interatomic potential (MLIP).
Applied AQuaRef to refine 41 cryo-EM and 30 X-ray structures.
Compared AQuaRef results against standard refinement techniques.

Main Results:

AQuaRef produced atomic models with superior geometric quality.
The method maintained or improved the fit to experimental data compared to standard techniques.
AQuaRef successfully determined proton positions, including challenging short hydrogen bonds in DJ-1 and YajL proteins.

Conclusions:

AI-enabled QM refinement offers a computationally efficient alternative for improving biomolecular models.
AQuaRef enhances structural accuracy and provides insights into hydrogen bonding and protonation states.
This method holds promise for advancing structural biology and understanding disease-associated proteins.