Bulletin of the American Physical Society
APS March Meeting 2021
Volume 66, Number 1
Monday–Friday, March 15–19, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session P25: Predicting Rare Event Kinetics in Complex Systems with Theory, Simulations and Machine Learning IIFocus Live
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Sponsoring Units: DCP Chair: Matteo Salvalaglio, University College London; Andrew Ferguson, University of Chicago |
Wednesday, March 17, 2021 3:00PM - 3:36PM Live |
P25.00001: Incorporating rate constants as kinetic constraints in molecular dynamics simulations Invited Speaker: Peter Bolhuis From the point of view of statistical mechanics, a full characterization of a molecular system requires an accurate determination of its possible states, their populations and the respective interconversion rates. Towards this goal, well-established methods increase the accuracy of molecular dynamics simulations by incorporating experimental information about states using structural restraints, and about populations using thermodynamics restraints. However, until so far it remains unclear how to include experimental knowledge about interconversion rates. Here we introduce a novel method of imposing known rate constants as constraints in molecular dynamics simulations, which is based on a combination of the maximum entropy and maximum caliber principles. Starting from an existing ensemble of trajectories, obtained from either molecular dynamics or enhanced trajectory sampling, such as transition path sampling, this method provides a minimally perturbed path distribution by reweighting trajectories, consistent with the kinetic constraints. In addition, the approach yields a modified free energy and committor landscape. We illustrate the application of the method to model systems, including all atom molecular simulations of protein folding. Our results show that by combining experimental rate constants and molecular dynamics simulations this approach enables improved determination of transition states, reaction mechanisms and free energies. We anticipate that this method will extend the applicability of molecular simulations to kinetic studies in structural biology, and that it will assist the development of force fields to reproduce kinetic and thermodynamic observables. Furthermore, this approach is generally applicable to a wide range of systems in biology, physics, chemistry, and material science. |
Wednesday, March 17, 2021 3:36PM - 3:48PM Live |
P25.00002: Kinetics and Free Energy of Protein-Ligand Interaction Using Weighted Ensemble Milestoning (WEM) Dhiman Ray, Trevor Gokey, David L. Mobley, Ioan Andricioaei Protein-ligand interactions are pivotal to the functioning of biological processes. However, their timescales often reach beyond μs-ms, making them difficult to probe using computional methods like all-atom molecular dynamics (MD) simulation. Weighted ensemble (WE) and milestoning are two powerful path sampling techniques to study such rare events, although both require significant computational effort. We developed the weighted ensemble milestoning (WEM) scheme, which combines the strength of these two methods to calculate the kinetics and the free energy profile from short and low cost MD trajectories. We study the unbinding and binding of 4-hydroxy-2-butanone (BUT) ligand to FKBP protein using the WEM protocol. We also propose an analytical diffusion model to calculate the binding rate constant and free energy, utilizing the WEM trajectories within the milestoning framework. Ligand binding and unbinding kinetics, and the binding free energy, obtained from 30 μs conventional MD simulation, are accurately reproduced using less than 100 ns of WEM calculation. Thus, WEM provides an inexpensive computational approach, to predict multiple important properties of protein-ligand interactions, for potential application in in-silico drug design. |
Wednesday, March 17, 2021 3:48PM - 4:00PM Live |
P25.00003: Direct calculation of kinetics from free energy surfaces Kristof Bal Many physical and chemical transformations involve metastable states with lifetimes that exceed the time scale of standard molecular dynamics (MD) simulations. State of the art enhanced sampling methods, on the other hand, make the reconstruction of a free energy surface (FES) nowadays fairly routine in many cases. Sampling of reaction rates, however, remains considerably more challenging and also tends to require different sampling strategies. |
Wednesday, March 17, 2021 4:00PM - 4:12PM Live |
P25.00004: Metadynamics of Paths Davide Mandelli, Barak Hirshberg, Michele Parrinello We present a method to sample reactive pathways via biased molecular dynamics simulations in trajectory space. We show that the use of enhanced sampling techniques enables unconstrained exploration of multiple reaction routes. Time correlation functions are conveniently computed via reweighted averages along a single trajectory and kinetic rates are accessed at no additional cost. These abilities are illustrated analyzing a model potential and the umbrella inversion of NH3 in water. The algorithm allows a parallel implementation and promises to be a powerful tool for the study of rare events. |
Wednesday, March 17, 2021 4:12PM - 4:24PM Live |
P25.00005: Accurate prediction of absolute molecular process rates on multisecond time scales Steffen Wolf, Benjamin Lickert, Simon Bray, Gerhard Stock Fully atomistic simulations of processes with rates of seconds to hours is still far beyond the scope of current molecular dynamics (MD). To access such time scales, we have developed dissipation-corrected targeted MD simulations to coarse-grain fully atomistic dynamics based on a Markovian Langevin equation framework and the Jarzynski equality. We enforce a molecular process along a reaction coordinate x and use the resulting bias force to calculate free energies ΔF(x) and friction profiles Γ(x). With ΔF(x) and Γ(x) as input for the temperature-boosted integration of the Langevin equation, we readily simulate dynamics far beyond the limits of fully atomistic MD methods. Using the dissociation-association of a sodium chloride ion pair in water as simple two-body problem, and two protein-ligand complexes as challenging test systems, we reproduce rates from unbiased simulations and experiments up to a time scale of 0.5 minutes within a factor of 2–20, and dissociation constants within a factor of 1–4 in reasonable computational time. Analysis of Γ(x) allows insight into system dynamics orthogonal to ΔF(x), revealing changes of hydration shells to mediate dynamics in all investigated systems. |
Wednesday, March 17, 2021 4:24PM - 4:36PM Live |
P25.00006: Square Root Approximation of the Infinitesimal Generator for Molecular Systems Luca Donati, Marcus Weber, Bettina Keller Molecular dynamics can be modelled as a stochastic process governed by a mathematical operator called infinitesimal generator which describes the kinetic properties of the system in terms of rates. |
Wednesday, March 17, 2021 4:36PM - 5:12PM Live |
P25.00007: Adaptive sampling for rare-event kinetics Cecilia Clementi Adaptive sampling methods are becoming increasingly popular for speeding up rare events in simulation such as molecular dynamics (MD) without biasing the system dynamics. Several adaptive sampling strategies have been proposed, but it is not clear which methods perform better for different physical systems. |
Wednesday, March 17, 2021 5:12PM - 5:24PM Live |
P25.00008: Switching Channels in TPS: Rare events within rare events David Swenson Just as a spontaneous transition from one metastable state to another is a rare event in a molecular dynamics simulation, the transition from sampling one reaction channel to another can be a rare event in a transition path sampling simulation. Here we discuss how channel switching can be analyzed and enhanced in path sampling simulations. Several approaches will be presented, as well as applications to two biological systems: comparing two proposed mechanisms of a conformational transition in DNA, and comparing the dynamics of wild-type KRas with a cancer-causing mutant. |
Wednesday, March 17, 2021 5:24PM - 5:36PM Live |
P25.00009: Unbiased trajectory-based estimation of stationary distributions and splitting probabilities John Russo, David Aristoff, Gideon Simpson, Jeremy Copperman, Daniel Zuckerman Despite many years of progress, procedures to harvest both kinetic and mechanistic observables from a set of unbiased molecular dynamics trajectories are still being optimized. Here we present an approach that exploits stationarity properties to yield both equilibrium and non-equilibrium observables. The method makes no Markov assumption, produces unbiased estimates of observables, and appears to fully harvest information residing in continuous trajectories. The approach can be realized through an iterative procedure, which we demonstrate to be equivalent to an efficient matrix formulation. Most notably, the method is able to resolve committor (splitting probability) values even for rarely visited states in the transition region between designated macrostates. We demonstrate the approach in toy and atomistic protein systems. |
Wednesday, March 17, 2021 5:36PM - 5:48PM Live |
P25.00010: Accelerated estimation of long-timescale kinetics from weighted ensemble simulation via non-Markovian "microbin" analysis. Jeremy Copperman, Daniel Zuckerman The weighted ensemble (WE) simulation strategy provides unbiased sampling of non-equilibrium processes, such as molecular folding or binding, but the extraction of rate constants relies on characterizing steady state behavior. Unfortunately, WE simulations of sufficiently complex systems will not relax to steady state on observed simulation times. Here we show that a post-simulation clustering of molecular configurations into ``microbins'' using methods developed in the Markov State Model (MSM) community, can yield unbiased kinetics from WE data before steady-state convergence of the WE simulation itself. Because WE trajectories are directional and not equilibrium-distributed, the history-augmented MSM (haMSM) formulation can be used, which yields the mean first-passage time (MFPT) without bias for arbitrarily small lag times. We report significant progress towards the unbiased estimation of protein folding times and pathways, though key challenges remain.[1] |
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