Inferential optimization for simultaneous fitting of multiple components into a CryoEM map of their assembly

Area of Science:

• Structural Biology
• Computational Biology
• Biophysics

Background:

• Macromolecular assemblies are crucial for cellular functions.
• Current methods struggle to accurately fit atomic structures into low-resolution density maps.
• Unambiguous determination of component positions and orientations is challenging.

Purpose of the Study:

• To introduce MultiFit, a novel computational method for fitting atomic structures into macromolecular assembly density maps.
• To enable accurate modeling of multicomponent assemblies even at low resolutions (down to 25 Å).
• To provide reliable initial configurations for further refinement of complex biological structures.

Main Methods:

• MultiFit simultaneously fits multiple atomic structures into a given density map.
• It optimizes component positions and orientations using a scoring function.
• The scoring function considers fit quality, protrusion from the map, and shape complementarity.
• DOMINO (Discrete Optimization of Multiple INteracting Objects) is used for global optimization.

Main Results:

• MultiFit was successfully benchmarked on seven diverse macromolecular assemblies.
• The method accurately determined near-native configurations for assemblies with up to seven protein components.
• Input structures were sourced from the Protein Data Bank and comparative modeling.
• The method performs well even with comparative models showing 16-99% sequence identity.

Conclusions:

• MultiFit offers a robust solution for modeling macromolecular assemblies from low-resolution electron microscopy data.
• It significantly improves the accuracy of determining component positions and orientations.
• The method is valuable for generating starting models for further refinement in structural biology.