Predictive modeling and cryo-EM: A synergistic approach to modeling macromolecular structure

Area of Science:

• Structural biology
• Biophysics
• Computational biology

Background:

• Single-particle cryo-electron microscopy (cryo-EM) now achieves near-atomic resolution.
• Machine learning, exemplified by AlphaFold, accurately predicts protein structures from sequence.
• These powerful techniques offer synergistic potential for macromolecular modeling.

Purpose of the Study:

• To provide an overview of cryo-EM and predictive modeling advancements.
• To illustrate the integration of these techniques for model building.
• To discuss insights, assessment, and limitations in combined approaches.

Main Methods:

• Review of single-particle cryo-EM for high-resolution structure determination.
• Application of machine learning-based predictive modeling (e.g., AlphaFold) for protein structures.
• Integration strategies for combining cryo-EM density maps with predicted models.

Main Results:

• Cryo-EM has overcome previous resolution limitations for macromolecular complexes.
• Predictive modeling tools can reliably generate accurate protein models from sequence.
• Combined approaches facilitate the construction of large, complex macromolecular models.

Conclusions:

• The synergy between cryo-EM and predictive modeling accelerates structural biology insights.
• Integration offers powerful tools for model building, assessment, and understanding limitations.
• Coupling with experimental data further enhances the generation of macromolecular structural information.