The enhanced sampling in parallel finite-time dynamics method with replica exchange

2021 ◽  
Vol 263 ◽  
pp. 107911
Author(s):  
Chudong Xu ◽  
Shengdong Lu ◽  
Yongfeng Kong ◽  
Wanjie Xiong
Keyword(s):  
Finite Time ◽  
Replica Exchange ◽  
Enhanced Sampling ◽  
Time Dynamics ◽  
Dynamics Method

Crossover effects in dilute magnetic materials by finite-time dynamics method

2014 ◽  
Vol 405 ◽  
pp. 352-359 ◽  
Author(s):  
Wanjie Xiong ◽  
Chudong Xu ◽  
Zizheng Guo ◽  
Xiaoxian Liu
Keyword(s):  
Magnetic Materials ◽  
Finite Time ◽  
Time Dynamics ◽  
Crossover Effects ◽  
Dynamics Method

Toward an Enhanced Sampling Molecular Dynamics Method for Studying Ligand-Induced Conformational Changes in Proteins

2015 ◽  
Vol 119 (46) ◽  
pp. 14594-14603 ◽  
Author(s):  
Ole Juul Andersen ◽  
Julie Grouleff ◽  
Perri Needham ◽  
Ross C. Walker ◽  
Frank Jensen
Keyword(s):  
Molecular Dynamics ◽  
Conformational Changes ◽  
Molecular Dynamics Method ◽  
Enhanced Sampling ◽  
Dynamics Method

scholarly journals Extending Transition Path Theory: Periodically Driven and Finite-Time Dynamics

2020 ◽  
Vol 30 (6) ◽  
pp. 3321-3366
Author(s):  
Luzie Helfmann ◽  
Enric Ribera Borrell ◽  
Christof Schütte ◽  
Péter Koltai
Keyword(s):  
Finite Time ◽  
Social Dynamics ◽  
Transition Path ◽  
Time Dynamics ◽  
Transition Path Theory ◽  
Periodically Driven ◽  
Statistical Behavior ◽  
Ergodic Limit ◽  
Dynamical Regimes ◽  
Time Systems

Abstract Given two distinct subsets A, B in the state space of some dynamical system, transition path theory (TPT) was successfully used to describe the statistical behavior of transitions from A to B in the ergodic limit of the stationary system. We derive generalizations of TPT that remove the requirements of stationarity and of the ergodic limit and provide this powerful tool for the analysis of other dynamical scenarios: periodically forced dynamics and time-dependent finite-time systems. This is partially motivated by studying applications such as climate, ocean, and social dynamics. On simple model examples, we show how the new tools are able to deliver quantitative understanding about the statistical behavior of such systems. We also point out explicit cases where the more general dynamical regimes show different behaviors to their stationary counterparts, linking these tools directly to bifurcations in non-deterministic systems.

scholarly journals Enhanced Sampling in Molecular Dynamics Using Metadynamics, Replica-Exchange, and Temperature-Acceleration

Entropy ◽  
2013 ◽  
Vol 16 (1) ◽  
pp. 163-199 ◽  
Author(s):  
Cameron Abrams ◽  
Giovanni Bussi
Keyword(s):  
Molecular Dynamics ◽  
Replica Exchange ◽  
Enhanced Sampling

scholarly journals Exploring Protocols to Build Reservoirs to Accelerate Replica Exchange MD Simulations

2020 ◽  
Author(s):  
koushik kasavajhala ◽  
kenneth lam ◽  
Carlos Simmerling
Keyword(s):  
Amino Acids ◽  
Sampling Method ◽  
Convergence Rates ◽  
Md Simulations ◽  
Replica Exchange ◽  
Conformational Sampling ◽  
Enhanced Sampling ◽  
Replica Exchange Molecular Dynamics ◽  
The Past ◽  
Biomolecular Simulations

Replica Exchange Molecular Dynamics (REMD) is a widely used enhanced sampling method for accelerating biomolecular simulations. During the past two decades, several variants of REMD have been developed to further improve the rate of conformational sampling of REMD. One such variant, Reservoir REMD (RREMD), was shown to improve the rate of conformational sampling by around 5-20x. Despite the significant increase in sampling speed, RREMD methods have not been widely used due to the difficulties in building the reservoir and also due to the code not being available on the GPUs.<br><br>In this work, we ported the AMBER RREMD code onto GPUs making it 20x faster than the CPU code. Then, we explored protocols for building Boltzmann-weighted reservoirs as well as non-Boltzmann reservoirs, and tested how each choice affects the accuracy of the resulting RREMD simulations. We show that, using the recommended protocols outlined here, RREMD simulations can accurately reproduce Boltzmann-weighted ensembles obtained by much more expensive conventional REMD simulations, with at least 15x faster convergence rates even for larger proteins (>50 amino acids) compared to conventional REMD.

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