Cryo-Electron Microscopy Structural Ensemble Optimization Using Individual Particles

Area of Science:

• Structural biology
• Biophysics
• Computational biology

Background:

• Biomolecules exist in dynamic conformational states essential for function.
• Cryo-electron microscopy (cryo-EM) determines atomic resolution structures but characterizing dynamic ensembles remains challenging.
• Existing methods infer population weights (ensemble reweighting) but cannot simultaneously determine structures and weights.

Purpose of the Study:

• To develop a novel method for simultaneous inference of conformational structures and their population weights from cryo-EM data.
• To enable comprehensive characterization of flexible biomolecules' conformational landscapes.
• To advance the capabilities of cryo-EM for studying biomolecular dynamics.

Main Methods:

• Developed a cryo-EM ensemble optimization method using Bayesian optimization.
• Iteratively optimized structures and weights directly from cryo-EM particle images.
• Employed a projected gradient descent-inspired approach for physical prior projection.

Main Results:

• Successfully recovered accurate structures and population weights across various systems, from toy models to large proteins.
• Demonstrated robustness under diverse experimental conditions.
• Showed effective performance even when the number of inferred structures did not match the true number of states.

Conclusions:

• The cryo-EM ensemble optimization method provides a powerful new tool for structural biology.
• Enables detailed analysis of complex, multimodal conformational landscapes in flexible biomolecules.
• Paves the way for advanced cryo-EM studies of biomolecular dynamics and function.