Cross-crystal averaging with search models to improve molecular replacement phases

Area of Science:

• Macromolecular crystallography
• Structural biology
• Biophysics

Background:

• Molecular replacement (MR) is crucial for solving macromolecular structures.
• The "model bias" problem can hinder MR accuracy when dealing with incomplete structural information.
• Accurate electron density maps are essential for reliable structure determination.

Purpose of the Study:

• To develop and validate a strategy for reducing model bias in macromolecular crystallography.
• To improve electron density interpretability for unknown regions of macromolecular structures.
• To facilitate structure determination when only partial models are available.

Main Methods:

• Proposed a novel strategy involving cross-crystal averaging of known structural components with the search model after MR.
• Applied the strategy to determine structures of coronavirus receptor-binding domains complexed with their receptors.
• Tested the method on an antigen-antibody complex with significant missing regions.

Main Results:

• The cross-crystal averaging strategy significantly improved electron density in the unknown regions of the structures.
• Successfully determined the structures of two coronavirus receptor-binding domain-receptor complexes at moderate resolutions.
• Enabled automated model building for an antigen-antibody complex where over 50% of the model was initially absent.

Conclusions:

• The proposed averaging strategy effectively reduces model bias in macromolecular crystallography.
• This method enhances the interpretability of electron density for missing or unknown structural elements.
• The strategy shows potential for routine application after MR to improve structure determination workflows.