Graph Measures Reveal Fine Structure of Complexes Forming in Multiparticle Simulations

Area of Science:

• Computational biology
• Biophysics
• Biochemistry

Background:

• Modern simulation techniques are increasingly applied to study dynamic multiprotein systems.
• Monitoring the assembly and disassembly of specific protein structures in many-particle simulations is challenging.
• Analyzing complex formation requires efficient methods to distinguish complete structures from intermediates and monomers.

Purpose of the Study:

• To introduce and validate a novel strategy for analyzing complex formation in many-particle simulations.
• To demonstrate the convenience and efficiency of mapping spatial configurations onto dynamically updated interaction graphs.
• To facilitate the detailed analysis of dynamic assembly and disassembly processes in multiprotein systems.

Main Methods:

• Utilizing many-particle simulations, specifically Monte Carlo simulations.
• Employing spherical particles with isotropic or directed mutual attractions.
• Mapping spatial configurations onto a dynamically updated interaction graph for analysis.

Main Results:

• The combined strategy of spatial mapping onto interaction graphs significantly simplifies the monitoring of complex formation.
• This approach allows for efficient and detailed analysis of protein complex and virus capsid assembly.
• The method effectively distinguishes fully assembled structures from monomers and partial complexes.

Conclusions:

• Mapping spatial configurations to dynamic interaction graphs is an efficient strategy for analyzing complex formation in many-particle systems.
• This technique enhances the study of dynamic assembly and disassembly processes in multiprotein systems.
• The method provides a convenient and detailed approach for biophysical and computational biology research.