Pavel V. Afonine

The paper implements a method in CCTBX and Phenix to calculate variable-resolution maps from atomic models, improving accuracy by incorporating local resolution and atomic parameters.

The paper identifies density misfit barrier traps that limit macromolecular model accuracy and introduces a synthetic dataset to challenge and improve ensemble refinement algorithms.

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

The paper introduces a mosaic bulk-solvent model that improves the fit between crystallographic models and data, enhancing density map quality and solving previously inaccessible problems.

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

The paper describes real-space refinement in Phenix for improving cryo-EM models using additional restraints and shows significant model improvement.