A Local Enhanced Sampling Strategy for Surrogate-Based Microwave Component Optimization

2021 ◽  
Author(s):  
Zhen Zhang ◽  
Qingsha S. Cheng
Keyword(s):  
Sampling Strategy ◽  
Enhanced Sampling ◽  
Microwave Component

scholarly journals An Efficient GaMD Multi-Level Enhanced Sampling Strategy: Application to Polarizable Force Fields Simulations of Large Biological Systems

2021 ◽  
Author(s):  
Fréderic Célerse ◽  
Theo Jaffrelot-Inizan ◽  
Louis Lagardère ◽  
Olivier Adjoua ◽  
Pierre Monmarché ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Adaptive Sampling ◽  
Sampling Strategy ◽  
Umbrella Sampling ◽  
Potential Of Mean Force ◽  
Enhanced Sampling ◽  
Polarizable Force Field ◽  
Accelerated Molecular Dynamics ◽  
Polarizable Force Fields ◽  
Multi Level

We detail a novel multi-level enhanced sampling strategy grounded on Gaussian accelerated Molecular Dynamics (GaMD). First, we propose a GaMD multi-GPUs-accelerated implementation within the Tinker-HP molecular dynamics package. We then introduce the new "dual-water" mode and its use with the flexible AMOEBA polarizable force field. By adding harmonic boosts to the water stretching and bonding terms, it accelerates the solvent-solute interactions while enabling speedups thanks to the use of fast multiple--timestep integrators. To further reduce time-to-solution, we couple GaMD to Umbrella Sampling (US). The GaMD—US/dual-water approach is tested on the 1D Potential of Mean Force (PMF) of the CD2-CD58 system (168000 atoms) allowing the AMOEBA PMF to converge within 1 kcal/mol of the experimental value. Finally, Adaptive Sampling (AS) is added enabling AS-GaMD capabilities but also the introduction of the new Adaptive Sampling--US--GaMD (ASUS--GaMD) scheme. The highly parallel ASUS--GaMD setup decreases time to convergence by respectively 10 and 20 compared to GaMD--US and US.

scholarly journals An Efficient GaMD Multi-Level Enhanced Sampling Strategy for Polarizable Force Fields Simulations of Large Biological Systems

2021 ◽  
Author(s):  
Fréderic Célerse ◽  
Theo Jaffrelot-Inizan ◽  
Louis Lagardère ◽  
Olivier Adjoua ◽  
Pierre Monmarché ◽  
...  
Keyword(s):  
Adaptive Sampling ◽  
Sampling Strategy ◽  
Umbrella Sampling ◽  
Potential Of Mean Force ◽  
Enhanced Sampling ◽  
Polarizable Force Field ◽  
Accelerated Molecular Dynamics ◽  
Polarizable Force Fields ◽  
Solute Interactions ◽  
Multi Level

We introduce a novel multi-level enhanced sampling strategy grounded on Gaussian accelerated Molecular Dynamics (GaMD). First, we propose a GaMD multi-GPUs -accelerated implementation within Tinker-HP. For the specific use with the flexible AMOEBA polarizable force field (PFF), we introduce the new "dual–water" GaMD mode. By adding harmonic boosts to the water stretching and bonding terms, it accelerates the solvent-solute interactions while enabling speedups with fast multiple–timestep integrators. To further reduce time-to-solution, we couple GaMD to Umbrella Sampling (US). The GaMD—US/dual–water approach is tested on the 1D Potential of Mean Force (PMF) of the CD2–CD58 system (168000 atoms) allowing the AMOEBA PMF to converge within 1 kcal/mol of the experimental value. Finally, Adaptive Sampling (AS) is added enabling AS–GaMD capabilities but also the introduction of the new Adaptive Sampling–US–GaMD (ASUS–GaMD) scheme. The highly parallel ASUS–GaMD setup decreases time to convergence by respectively 10 and 20 compared to GaMD–US and US.

scholarly journals An Efficient GaMD Multi-Level Enhanced Sampling Strategy: Application to Polarizable Force Fields Simulations of Large Biological Systems

2021 ◽  
Author(s):  
Fréderic Célerse ◽  
Theo Jaffrelot-Inizan ◽  
Louis Lagardère ◽  
Olivier Adjoua ◽  
Pierre Monmarché ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Adaptive Sampling ◽  
Force Fields ◽  
Biological Systems ◽  
Sampling Strategy ◽  
Umbrella Sampling ◽  
Potential Of Mean Force ◽  
Enhanced Sampling ◽  
Polarizable Force Fields ◽  
Multi Level

We introduce a novel multi-level enhanced sampling strategy grounded on Gaussian accelerated Molecular Dynamics (GaMD). First, we propose a GaMD multi-GPUs-accelerated implementation within the Tinker-HP molecular dynamics package. We introduce the new "dual-water" mode and its use with the flexible AMOEBA polarizable force field.By adding harmonic boosts to the water stretching and bonding terms, it accelerates the solvent-solute interactions while enabling speedups thanks to the use of fast multiple--timestep integrators. To further reduce time-to-solution, we couple GaMD to Umbrella Sampling (US). The GaMD—US/dual--water approach is tested on the 1D Potential of Mean Force (PMF) of the solvated CD2--CD58 system (168000 atoms) allowing the AMOEBA PMF to converge within 1 kcal/mol of the experimental value. Finally, Adaptive Sampling (AS) is added enabling AS-GaMD capabilities but also the introduction of the new Adaptive Sampling--US--GaMD (ASUS-GaMD) scheme. The highly parallel ASUS--GaMD setup decreases time to convergence by respectively 10 and 20 times compared to GaMD-US and US. Overall, beside the acceleration of PMF computations, Tinker-HP now allows for the simultaneous use of Adaptive Sampling and GaMD-"dual water" enhanced sampling approaches increasing the applicability of polarizable force fields to large scale simulations of biological systems.

An Efficient GaMD Multi-Level Enhanced Sampling Strategy: Application to Polarizable Force Fields Simulations of Large Biological Systems

2021 ◽  
Author(s):  
Fréderic Célerse ◽  
Theo Jaffrelot-Inizan ◽  
Louis Lagardère ◽  
Olivier Adjoua ◽  
Pierre Monmarché ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Adaptive Sampling ◽  
Sampling Strategy ◽  
Umbrella Sampling ◽  
Potential Of Mean Force ◽  
Enhanced Sampling ◽  
Polarizable Force Field ◽  
Accelerated Molecular Dynamics ◽  
Polarizable Force Fields ◽  
Multi Level

We detail a novel multi-level enhanced sampling strategy grounded on Gaussian accelerated Molecular Dynamics (GaMD). First, we propose a GaMD multi-GPUs-accelerated implementation within the Tinker-HP molecular dynamics package. We then introduce the new "dual-water" mode and its use with the flexible AMOEBA polarizable force field. By adding harmonic boosts to the water stretching and bonding terms, it accelerates the solvent-solute interactions while enabling speedups thanks to the use of fast multiple--timestep integrators. To further reduce time-to-solution, we couple GaMD to Umbrella Sampling (US). The GaMD—US/dual-water approach is tested on the 1D Potential of Mean Force (PMF) of the CD2-CD58 system (168000 atoms) allowing the AMOEBA PMF to converge within 1 kcal/mol of the experimental value. Finally, Adaptive Sampling (AS) is added enabling AS-GaMD capabilities but also the introduction of the new Adaptive Sampling--US--GaMD (ASUS--GaMD) scheme. The highly parallel ASUS--GaMD setup decreases time to convergence by respectively 10 and 20 compared to GaMD--US and US.

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

2017 ◽  
Author(s):  
Samuel Gill ◽  
Nathan M. Lim ◽  
Patrick Grinaway ◽  
Ariën S. Rustenburg ◽  
Josh Fass ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Binding Mode ◽  
Free Energy Calculations ◽  
Dynamics Simulation ◽  
Binding Modes ◽  
Enhanced Sampling ◽  
Recent Success ◽  
Multiple Binding ◽  
Proper Sampling

<div>Accurately predicting protein-ligand binding is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragment-like ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation timescales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes.</div><div><br></div><div>In this technique the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over two orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step towards applying this methodology to pharmaceutically relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding Modes of Ligands using Enhanced Sampling (BLUES) package which is freely available on GitHub.</div>

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

2018 ◽  
Author(s):  
Samuel Gill ◽  
Nathan M. Lim ◽  
Patrick Grinaway ◽  
Ariën S. Rustenburg ◽  
Josh Fass ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Binding Mode ◽  
Free Energy Calculations ◽  
Dynamics Simulation ◽  
Binding Modes ◽  
Enhanced Sampling ◽  
Recent Success ◽  
Multiple Binding ◽  
Proper Sampling

<div>Accurately predicting protein-ligand binding is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragment-like ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation timescales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes.</div><div><br></div><div>In this technique the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over two orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step towards applying this methodology to pharmaceutically relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding Modes of Ligands using Enhanced Sampling (BLUES) package which is freely available on GitHub.</div>

Modelling Flexible Protein-Ligand Binding in p38α MAP Kinase using the QUBE Force Field

2019 ◽  
Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Map Kinase ◽  
Binding Free Energy ◽  
Quantum Mechanical ◽  
Enhanced Sampling ◽  
Relative Binding ◽  
Stringent Test ◽  
Flexible Protein ◽  
P38α Map Kinase

<div><div><div><p>The quantum mechanical bespoke (QUBE) force field is used to retrospectively calculate the relative binding free energy of a series of 17 flexible inhibitors of p38α MAP kinase. The size and flexibility of the chosen molecules represent a stringent test of the derivation of force field parameters from quantum mechanics, and enhanced sampling is required to reduce the dependence of the results on the starting structure. Competitive accuracy with a widely-used biological force field is achieved, indicating that quantum mechanics derived force fields are approaching the accuracy required to provide guidance in prospective drug discovery campaigns.</p></div></div></div>

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
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