Langevin-Modellierung langsamer biomolekularer Prozesse
Theoretische Chemie: Elektronenstruktur, Dynamik, Simulation
Zusammenfassung der Projektergebnisse
The in silico prediction of rare biomolecular events represents a long-standing problem, because slow (say, millisecond timescale) processes are typically out of reach of current all-atom molecular dynamics (MD) simulations. To circumvent this time scale problem, one often invokes massive parallel computing strategies or various enhanced sampling techniques such as metadynamics, steered or targeted MD, and strategies based Markov state models (MSM). While these approaches may provide an efficient sampling of the system’s free energy landscape, a dynamical description requires some coarse-grained “post-simulation” model that is capable of rebuilding the kinetics from the sampled data. To this end, we have recently proposed a data-driven Langevin equation (dLE) approach that constructs a low-dimensional dynamical model from a given MD trajectory. To extend the application of these approaches to rare biomolecular processes, various enhanced sampling strategies were developed during the funding period of this project. By employing Metadynamics to generate well-distributed initial structures for short MD trajectories, a global MSM of the long-time dynamics was constructed. Similarly, we built global MSMs based on MELD which efficiently samples relevant conformational states to seed multiple short MD trajectories. Most importantly, dissipation corrected targeted molecular dynamics combined with temperature-boosted Langevin simulations was introduced, which allows to study multisecond ligand dissociation dynamics of medically relevant macromolecular system such as heat-shock protein Hsp90. To facilitate and augment these developments, various theoretical aspects of the problem were considered, including the dynamical coring of MSMs, the dimensionality reduction of driven systems, and the evaluation of nonequilibrium memory effects.
Projektbezogene Publikationen (Auswahl)
- MELD-Path Efficiently Computes Conformational Transitions, Including Multiple and Diverse Paths, J. Chem. Theory Comput. 14, 2109 (2018)
A. Perez, F. Sittel, G. Stock and K. Dill
(Siehe online unter https://doi.org/10.1021/acs.jctc.7b01294) - Metadynamics Enhanced Markov Modeling: Protein Dynamics from Short Trajectories, J. Phys. Chem. B 122, 5508 (2018)
M. Biswas, B. Lickert and G. Stock
(Siehe online unter https://doi.org/10.1021/acs.jpcb.7b11800) - Targeted molecular dynamics calculations of free energy profiles using a nonequilibrium friction correction, J. Chem. Theory Comput. 14, 6175 (2018)
S. Wolf and G. Stock
(Siehe online unter https://doi.org/10.1021/acs.jctc.8b00835) - Dynamical coring of Markov state models, J. Chem. Phys. 150, 094111 (2019)
D. Nagel, A. Weber, B. Lickert and G. Stock
(Siehe online unter https://doi.org/10.1063/1.5081767) - Principal component analysis of nonequilibrium molecular dynamics simulations, J. Chem. Phys. 150, 204110 (2019)
M. Post, S. Wolf and G. Stock
(Siehe online unter https://doi.org/10.1063/1.5089636) - A numerical procedure to evaluate memory effects in non-equilibrium coarse-grained models, Adv. Theory Simul. 111, 2000197 (2020)
H. Meyer, S. Wolf, G. Stock and T. Schilling
(Siehe online unter https://doi.org/10.1002/adts.202000197) - Multisecond ligand dissociation dynamics from atomistic simulations, Nature Commun. 11, 2918 (2020)
S. Wolf, B. Lickert, S. Bray and G. Stock
(Siehe online unter https://doi.org/10.1038/s41467-020-16655-1)
