Protein interactions are the key component of cellular processes. Approaches based on deep learning currently solve protein structures at accuracy similar to experiment. This breakthrough inspires a major transition in the future direction of computational methodology development - from pattern recognition that has been the major driving force in computational biology, to stochastic modeling of live processes. Our study bridged protein docking and kinetic simulations, putting forward an approach that can reach unprecedented simulation times at all-atom resolution. The energy landscape of a cell-size system was mapped by the pairwise FFT docking (Fig 1) and sampled by a Monte Carlo protocol. The procedure was parametrized on existing data and validated on observations from experiment and molecular dynamics simulations. The speed of calculation allows reaching extra-long trajectories of cell-size macromolecular systems at atomic resolution (Fig 2), providing the tool to explore the dynamics of cellular processes in structural detail and address important biological questions involving macromolecular association.

Simulation of cell environment by sampling macromolecular landscape

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