Modeling the Self-Assembly of Protein Complexes through a Rigid-Body Rotational Reaction-Diffusion Algorithm.

Area of Science:

• Biophysics
• Computational Biology
• Chemical Physics

Background:

• Reaction-diffusion equations model cell-scale dynamics over long timescales.
• Single-particle reaction-diffusion offers high resolution but struggles with reactive species treated as point particles.
• Protein self-assembly dynamics are crucial but challenging to model at molecular scales.

Purpose of the Study:

• To develop a method integrating rigid structure and rotation into single-particle reaction-diffusion for protein self-assembly.
• To provide a rate-based simulation approach for studying protein self-assembly.
• To enable prediction of macroscopic association kinetics from microscopic parameters.

Main Methods:

• Developed a rate-based method for single-particle reaction-diffusion incorporating structure and rotation.
• Assumed reactive collisions depend on site separations, not orientations.
• Utilized translational diffusion equations with a derived effective diffusion constant for 3D and 2D simulations.

Main Results:

• Successfully reproduced kinetics of association, influenced by rotational diffusion.
• Accurately predicted equilibrium of reversible association, unaffected by rotation.
• Demonstrated efficient, rate-based simulations of clathrin trimer self-assembly.
• Showcased how lattice formation impacts association kinetics.

Conclusions:

• The new method accurately models protein self-assembly by incorporating structural and rotational effects.
• This approach bridges the gap between molecular dynamics and macroscopic observations.
• It facilitates critical comparisons between simulations, theory, and experimental data.