Including crystallographic symmetry in quantum-based refinement: Q|R#2.

Area of Science:

• Structural biology
• Computational chemistry
• Biophysics

Background:

• Low-resolution crystallographic and cryo-electron microscopy (cryo-EM) data can be refined using restraints from quantum-chemical methods.
• Standard quantum-chemistry codes struggle to accurately model nearest-neighbor interactions in crystallographic symmetry-related structures.

Purpose of the Study:

• To develop and validate a fragmentation approach that incorporates nearest-neighbor interactions for refining protein structures.
• To improve the accuracy of three-dimensional structure models derived from low-resolution experimental data.

Main Methods:

• A super-cell expansion and truncation method was used to include nearest-neighbor effects in quantum-chemical calculations.
• A target protein (PDB entry 4gif) was divided into large fragments for quantum mechanical (QM) calculations.
• QM calculations were performed using HF-D3/6-31G and GFN2-xTB methods, with TeraChem utilized for large fragments.
• Refinement procedures were validated using standard crystallographic metrics on a non-P1 structure and 13 additional protein models across various space groups.

Main Results:

• The fragmentation approach effectively and efficiently includes nearest-neighbor effects.
• Large fragments (hundreds of atoms) are computationally tractable with GPU acceleration or semi-empirical methods.
• Refinement using the developed method showed robustness across multiple protein models and space groups.

Conclusions:

• The developed fragmentation approach provides high-quality restraints for refining protein structures from low-resolution data.
• This method enhances the accuracy of three-dimensional models by properly accounting for crystallographic symmetry effects.
• The approach is robust and applicable to a variety of protein structures and crystallographic conditions.