Nigel W. Moriarty

The paper provides a library of 37,000 minimized small molecule geometries for use in macromolecular refinement and molecular dynamics simulations.

AQuaRef uses machine learning to refine protein structures with quantum accuracy at lower computational cost, improving geometric quality and experimental data fit.

The paper determines the structure of active Ni(I)-Methyl-CoM reductase, revealing how it catalyzes methane formation and providing insights for methane emission control and biofuel production.

This paper improves macromolecular structure refinement using Phenix and OPLS3e for better structure and ligand quality in X-ray and cryo-EM data.

This paper introduces a quantum-based refinement method for cryo-EM maps to improve atomic model accuracy at low resolutions using quantum-chemical restraints.

The paper refines a protein structure using quantum chemistry restraints, incorporating crystallographic symmetry effects through a fragmentation approach.

The paper integrates the Amber force field into Phenix for crystallographic refinement, improving model quality through better stereochemistry, fewer clashes, and better hydrogen bonds.

The paper introduces new computational tools for validating cryo-EM maps and atomic models, including methods for resolution estimation, map sharpening, and map analysis.