Publisher's Note: “Investigating rare events with nonequilibrium work measurements. I. Nonequilibrium transition path probabilities” [J. Chem. Phys. 140, 034114 (2014)]

2014 ◽  
Vol 140 (6) ◽  
pp. 069901
Author(s):  
Mahmoud Moradi ◽  
Celeste Sagui ◽  
Christopher Roland
Keyword(s):  
Rare Events ◽  
Chem Phys ◽  
Transition Path

Investigating rare events with nonequilibrium work measurements. I. Nonequilibrium transition path probabilities

2014 ◽  
Vol 140 (3) ◽  
pp. 034114 ◽  
Author(s):  
Mahmoud Moradi ◽  
Celeste Sagui ◽  
Christopher Roland
Keyword(s):  
Rare Events ◽  
Transition Path

scholarly journals Optimizing reaction coordinate by flux maximization in the transition path ensemble

2021 ◽  
Author(s):  
Wenjin Li
Keyword(s):  
Linear Combination ◽  
Chem Phys ◽  
Reaction Coordinate ◽  
Transition Path ◽  
Heavy Atoms ◽  
Reaction Coordinates ◽  
Reactive Trajectories ◽  
Transition Channel ◽  
Highly Correlated ◽  
Practical Factors

Transition path ensemble is a collection of reactive trajectories, all of which largely keep going forward along the transition channel from the reactant state to the product one, and is believed to possess the information necessary for the identification of reaction coordinate. Previously, the full coordinates (both position and momentum) of the snapshots in the transition path ensemble were utilized to obtain the reaction coordinate (J. Chem. Phys. 2016, 144, 114103; J. Chem. Phys. 2018, 148, 084105). Here, with the conformational (or position) coordinates alone, it is demonstrated that the reaction coordinate can be optimized by maximizing the flux of a given coordinate in the transition path ensemble. In the application to alanine dipeptide in vacuum, dihderal angles ϕ and θ were identified to be the two best reaction coordinates, which was consistent with the results in existing studies. A linear combination of these two coordinates gave a better reaction coordinate, which is highly correlated with committor. Most importantly, the method obtained a linear combination of pairwise distances between heavy atoms, which was highly correlated with committor as well. The standard deviation of committor at the transition region defined by the optimized reaction coordinate is as small as 0.08. In addition, the effects of practical factors, such as the choice of transition path sub-ensembles and saving interval between frames in transition paths, on reaction coordinate optimization were also considered.

scholarly journals Correction: Transition path time distribution and the transition path free energy barrier

2018 ◽  
Vol 20 (38) ◽  
pp. 25105-25105
Author(s):  
Eli Pollak
Keyword(s):  
Free Energy ◽  
Energy Barrier ◽  
Time Distribution ◽  
Chem Phys ◽  
Free Energy Barrier ◽  
Transition Path ◽  
Phys Chem Chem Phys

Correction for ‘Transition path time distribution and the transition path free energy barrier’ by Eli Pollak et al., Phys. Chem. Chem. Phys., 2016, 18, 28872–28882.

scholarly journals Achieving Reversible Ligand-Protein Unbinding with Deep Learning and Molecular Dynamics through RAVE

2018 ◽  
Author(s):  
João Marcelo Lamim Ribeiro ◽  
Pratyush Tiwary
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Deep Learning ◽  
Rare Events ◽  
Binding Free Energy ◽  
Piecewise Linear ◽  
Chem Phys ◽  
Enhanced Sampling ◽  
Order Of Magnitude ◽  
Low Dimensional

AbstractIn this work we demonstrate how to leverage our recent iterative deep learning–all atom molecular dynamics (MD) technique “Reweighted autoencoded variational Bayes for enhanced sampling (RAVE)” (Ribeiro, Bravo, Wang, Tiwary, J. Chem. Phys. 149, 072301 (2018)) for sampling protein-ligand unbinding mechanisms and calculating absolute binding affinities when plagued with difficult to sample rare events. RAVE iterates between rounds of MD and deep learning, and unlike other enhanced sampling methods, it stands out in simultaneously learning both a low-dimensional physically interpretable reaction coordinate (RC) and associated free energy. Here, we introduce a simple but powerful extension to RAVE which allows learning a position-dependent RC expressed as a superposition of piecewise linear RCs valid in different metastable states. With this approach, we retain the original physical interpretability of a RAVE-derived RC while making it applicable to a wider range of complex systems. We demonstrate how in its multi-dimensional form introduced here, RAVE can efficiently simulate the unbinding of the tightly bound benzene-lysozyme (L99A variant) complex, in all atom-precision and with minimal use of human intuition except for the choice of a larger dictionary of order parameters. These simulations had a 100 % success rate, and took between 3–50 nanoseconds for a process that takes on an average close to few hundred milliseconds, thereby reflecting a seven order of magnitude acceleration relative to straightforward MD. Furthermore, without any time-dependent biasing, the trajectories display clear back–and– forth movement between various metastable intermediates, demonstrating the reliability of the RC and its probability distribution learnt in RAVE. Our binding free energy is in good agreement with other reported simulation results. We thus believe that RAVE, especially in its multi-dimensional variant introduced here, will be a useful tool for simulating the dissociation process of practical biophysical systems with rare events in an automated manner with minimal use of human intuition.

scholarly journals Transition path sampling of rare events by shooting from the top

2017 ◽  
Vol 147 (15) ◽  
pp. 152716 ◽  
Author(s):  
Hendrik Jung ◽  
Kei-ichi Okazaki ◽  
Gerhard Hummer
Keyword(s):  
Rare Events ◽  
Path Sampling ◽  
Transition Path ◽  
Transition Path Sampling

scholarly journals Using Bayes formula to estimate rates of rare events in transition path sampling simulations

2015 ◽  
Vol 143 (13) ◽  
pp. 134121 ◽  
Author(s):  
Pierre Terrier ◽  
Mihai-Cosmin Marinica ◽  
Manuel Athènes
Keyword(s):  
Rare Events ◽  
Path Sampling ◽  
Transition Path ◽  
Transition Path Sampling ◽  
Bayes Formula

Improved transition path sampling methods for simulation of rare events

2008 ◽  
Vol 128 (14) ◽  
pp. 144104 ◽  
Author(s):  
Manan Chopra ◽  
Rohit Malshe ◽  
Allam S. Reddy ◽  
J. J. de Pablo
Keyword(s):  
Sampling Methods ◽  
Rare Events ◽  
Path Sampling ◽  
Transition Path ◽  
Transition Path Sampling
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