Accurate model and ensemble refinement using cryo-electron microscopy maps and Bayesian inference.

Area of Science:

• Structural biology
• Biophysics
• Computational biology

Background:

• Cryo-electron microscopy (cryo-EM) is crucial for determining molecular structures.
• Current refinement methods often compromise stereochemical quality for data fit.
• Representing conformational heterogeneity in cryo-EM maps remains a challenge.

Purpose of the Study:

• To develop a novel Bayesian inference approach for cryo-EM data analysis.
• To generate high-quality single-structure models and structural ensembles.
• To improve the balance between experimental data fit and physico-chemical accuracy.

Main Methods:

• EMMIVox: A Bayesian inference approach for cryo-EM data.
• Automatic balancing of experimental data with physico-chemical models.
• Inclusion of environmental factors (waters, lipids, ions) and data correlation.
• Inference of accurate B-factors for model quality.

Main Results:

• EMMIVox produces structural models with excellent fit to cryo-EM data.
• Achieves high stereochemical quality, outperforming current methods.
• Successfully determines structural ensembles, capturing conformational heterogeneity.
• Provides accurate B-factors for improved model interpretation.

Conclusions:

• EMMIVox offers a flexible and robust approach for cryo-EM model building.
• Enhances the accuracy and reliability of structural models derived from cryo-EM.
• Facilitates a deeper understanding of molecular mechanisms in biological systems.