Macromolecular self-assembly is a fundamental step in numerous biological processes, producing molecular machines like the ribosome or highly symmetric viral capsids. Building models of these molecular-scale interactions that bridge to a cell scale environment requires substantial coarse-graining of assembly subunits or intermediates. Balancing multi-valent structural resolution with computational efficiency is challenging. While rule-based or local interactions overcome the often prohibitive enumeration of all possible intermediates, they must ensure global structural constraints are met. We here demonstrate ioNERDSS, a user-friendly Python package that transforms 3D atomic structures into coarse-grained models for immediate simulation with the stochastic reaction-diffusion NERDSS software. NERDSS uses rule-based interactions to produce structural trajectories of assembly dynamics with the microsecond-minutes timescales comparable to experiment. With ioNERDSS, rigid subunits for each protein chain in the assembly contain discrete binding interfaces and explicit geometric constraints to prevent disordered assemblies. Repeated subunits (as in viral capsids) are regularized to preserve the target assembly topology across stochastic association and dissociation events. The ioNERDSS python package links models and outputs to open-source visualization, simulation, and analysis tools that facilitate user-friendly sanity checks, structure validation, and analysis of output for thermodynamic, kinetic, and nonequilibrium properties of macromolecular self-assembly.
Ying et al. (Wed,) studied this question.