A conservation and biophysics guided stochastic approach to refining docked multimeric proteins

Area of Science:

• Computational Biology
• Structural Biology
• Biochemistry

Background:

• Protein complexes are crucial for biological processes, necessitating accurate structural determination.
• Existing computational protein docking methods often struggle with accuracy and refinement, especially for complexes with more than two monomers.
• The computational complexity of modeling larger protein assemblies limits current approaches.

Purpose of the Study:

• To develop and validate a protein docking refinement method capable of handling multi-monomer complexes.
• To improve the accuracy and energetic stability of computationally docked protein structures.
• To address the limitations of existing docking tools for non-dimeric protein assemblies.

Main Methods:

• A novel scoring function integrating evolutionary conservation, geometric, and physico-chemical properties.
• A refinement scheme designed to handle complexes with any number of monomeric units.
• Probabilistic selection to avoid local energy minima during refinement.

Main Results:

• The refinement method successfully processed complexes with multiple monomers, prioritizing native interactions.
• Refined complexes demonstrated improved accuracy (lower IRMSDs) compared to initial docked structures.
• The method effectively filtered false positive docking results and reduced complex energies.

Conclusions:

• Leveraging evolutionary conservation guides the identification of functional protein interfaces.
• The developed method enhances the modeling of multi-protein complexes, offering a significant advancement over existing tools.
• Future work aims to integrate this refinement into a comprehensive docking framework for de novo multimeric complex prediction.