Highly Optimized Simulation of Atomic Resolution Cell-Like Protein Environment.
Andrii M Tytarenko, Amar Singh, Vineeth Kumar Ambati, Matthew M Copeland, Petras J Kundrotas, Randal Halfmann, Pavlo O Kasyanov, Eugene A Feinberg, Ilya A Vakser
Author Information
Andrii M Tytarenko: Institute for Applied System Analysis at the Igor Sikorsky Kyiv Polytechnic Institute, Kyiv 03056, Ukraine.
Amar Singh: Computational Biology Program, The University of Kansas, Lawrence, Kansas 66045, United States. ORCID
Vineeth Kumar Ambati: Computational Biology Program, The University of Kansas, Lawrence, Kansas 66045, United States.
Matthew M Copeland: Computational Biology Program, The University of Kansas, Lawrence, Kansas 66045, United States. ORCID
Petras J Kundrotas: Computational Biology Program, The University of Kansas, Lawrence, Kansas 66045, United States. ORCID
Randal Halfmann: Stowers Institute for Medical Research, Kansas City, Missouri 64110, United States.
Pavlo O Kasyanov: Institute for Applied System Analysis at the Igor Sikorsky Kyiv Polytechnic Institute, Kyiv 03056, Ukraine.
Eugene A Feinberg: Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, United States.
Ilya A Vakser: Computational Biology Program, The University of Kansas, Lawrence, Kansas 66045, United States. ORCID
Computational approaches can provide details of molecular mechanisms in a crowded environment inside cells. Protein docking predicts stable configurations of molecular complexes, which correspond to deep energy minima. Systematic docking approaches, such as those based on fast Fourier transform (FFT), also map the entire intermolecular energy landscape by determining the position and depth of the full spectrum of the energy minima. Such mapping allows speeding up simulations by precalculating the intermolecular energy values. Our earlier study combined FFT docking with the Monte Carlo protocol, enabling simulation of cell-size, crowded protein systems with seconds, and longer trajectories at atomic resolution, several orders of magnitude longer than those achievable by alternative approaches. In this study, we present a further drastic extension of the modeling capabilities by parallelized implementation of the simulation protocol. The procedure was applied to a panel of Death Fold Domains that form nucleated polymers in human innate immune signaling, recapitulating their homooligomerization tendencies and providing insights into the molecular mechanisms of polymer nucleation. The parallelized protocol allows extension of the simulation trajectories by orders of magnitude beyond the previously reported implementation, reaching into the uncharted territory of atomic resolution simulation of cell-sized systems.
References
Bioinformatics. 2015 Mar 15;31(6):926-32
[PMID: 25398609]
