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• W2893554442 abstract "The reaction-diffusion equations provide a powerful framework for modeling nonequilibrium, cell-scale dynamics over the long time scales that are inaccessible by traditional molecular modeling approaches. Single-particle reaction-diffusion offers the highest resolution technique for tracking such dynamics, but it has not been applied to the study of protein self-assembly due to its treatment of reactive species as single-point particles. Here, we develop a relatively simple but accurate approach for building rigid structure and rotation into single-particle reaction-diffusion methods, providing a rate-based method for studying protein self-assembly. Our simplifying assumption is that reactive collisions can be evaluated purely on the basis of the separations between the sites, and not their orientations. The challenge of evaluating reaction probabilities can then be performed using well-known equations based on translational diffusion in both 3D and 2D, by employing an effective diffusion constant we derive here. We show how our approach reproduces both the kinetics of association, which is altered by rotational diffusion, and the equilibrium of reversible association, which is not. Importantly, the macroscopic kinetics of association can be predicted on the basis of the microscopic parameters of our structurally resolved model, allowing for critical comparisons with theory and other rate-based simulations. We demonstrate this method for efficient, rate-based simulations of self-assembly of clathrin trimers, highlighting how formation of regular lattices impacts the kinetics of association." @default.
• W2893554442 created "2018-10-05" @default.
• W2893554442 creator A5029667852 @default.
• W2893554442 date "2018-09-26" @default.
• W2893554442 modified "2023-09-24" @default.
• W2893554442 title "Modeling the Self-Assembly of Protein Complexes through a Rigid-Body Rotational Reaction–Diffusion Algorithm" @default.
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• W2893554442 doi "https://doi.org/10.1021/acs.jpcb.8b08339" @default.
• W2893554442 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30256109" @default.
• W2893554442 hasPublicationYear "2018" @default.
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