Efficient molecular dynamics and hybrid Monte Carlo algorithms for path integrals

1993 ◽  
Vol 99 (4) ◽  
pp. 2796-2808 ◽  
Author(s):  
Mark E. Tuckerman ◽  
Bruce J. Berne ◽  
Glenn J. Martyna ◽  
Michael L. Klein
Keyword(s):  
Molecular Dynamics ◽  
Monte Carlo ◽  
Path Integrals ◽  
Hybrid Monte Carlo ◽  
Monte Carlo Algorithms

scholarly journals Computational Prediction of Heteromeric Protein Complex Disassembly Order with Hybrid Monte Carlo/Molecular Dynamics Simulation

2022 ◽  
Author(s):  
Ikuo Kurisaki ◽  
Shigenori Tanaka
Keyword(s):  
Molecular Dynamics ◽  
Monte Carlo ◽  
Protein Complex ◽  
Protein Complexes ◽  
Structural Bioinformatics ◽  
Dynamics Simulation ◽  
Tryptophan Synthase ◽  
Selection Scheme ◽  
Hybrid Monte Carlo ◽  
Multimeric Protein

The physicochemical entity of biological phenomenon in the cell is a network of biochemical reactions and the activity of such a network is regulated by multimeric protein complexes. Mass spectroscopy (MS) experiments and multimeric protein docking simulations based on structural bioinformatics techniques have revealed the molecular-level stoichiometry and static configuration of subcomplexes in their bound forms, then revealing the subcomplex populations and formation orders. Meanwhile, these methodologies are not designed to straightforwardly examine temporal dynamics of multimeric protein assembly and disassembly, essential physicochemical properties to understand functional expression mechanisms of proteins in the biological environment. To address the problem, we had developed an atomistic simulation in the framework of the hybrid Monte Carlo/Molecular Dynamics (hMC/MD) method and succeeded in observing disassembly of homomeric pentamer of the serum amyloid P component protein in experimentally consistent order. In this study, we improved the hMC/MD method to examine disassembly processes of the tryptophan synthase tetramer, a paradigmatic heteromeric protein complex in MS studies. We employed the likelihood-based selection scheme to determine a dissociation-prone subunit pair at each hMC/MD simulation cycle and achieved highly reliable predictions of the disassembly orders with the success rate over 0.9 without a priori knowledge of the MS experiments and structural bioinformatics simulations. We similarly succeeded in reliable predictions for the other three tetrameric protein complexes. These achievements indicate the potential availability of our hMC/MD approach as the general purpose methodology to obtain microscopic and physicochemical insights into multimeric protein complex formation.

scholarly journals Chemo-mechanical coupling in kerogen gas adsorption/desorption

2018 ◽  
Vol 20 (18) ◽  
pp. 12390-12395 ◽  
Author(s):  
Tuan Anh Ho ◽  
Yifeng Wang ◽  
Louise J. Criscenti
Keyword(s):  
Molecular Dynamics ◽  
Monte Carlo ◽  
Molecular Dynamics Simulation ◽  
Simulation Study ◽  
Gas Adsorption ◽  
Dynamics Simulation ◽  
Hybrid Monte Carlo ◽  
Mechanical Coupling ◽  
Adsorption Desorption

Strong chemo-mechanical coupling in kerogen gas adsorption from a hybrid Monte Carlo/molecular dynamics simulation study.

scholarly journals Parallel Hybrid Monte Carlo Algorithms for Matrix Computations

2005 ◽  
pp. 752-759 ◽  
Author(s):  
V. Alexandrov ◽  
E. Atanassov ◽  
I. Dimov ◽  
S. Branford ◽  
A. Thandavan ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Matrix Computations ◽  
Hybrid Monte Carlo ◽  
Monte Carlo Algorithms ◽  
Parallel Hybrid

Higher-order hybrid Monte Carlo algorithms

1989 ◽  
Vol 63 (1) ◽  
pp. 9-12 ◽  
Author(s):  
Michael Creutz ◽  
Andreas Gocksch
Keyword(s):  
Monte Carlo ◽  
Higher Order ◽  
Hybrid Monte Carlo ◽  
Monte Carlo Algorithms

Reversible molecular dynamics for rigid bodies and hybrid Monte Carlo

1999 ◽  
Vol 110 (7) ◽  
pp. 3291-3301 ◽  
Author(s):  
Nobuyuki Matubayasi ◽  
Masaru Nakahara
Keyword(s):  
Molecular Dynamics ◽  
Monte Carlo ◽  
Rigid Bodies ◽  
Hybrid Monte Carlo

scholarly journals Combining stochastic and deterministic approaches within high efficiency molecular simulations

2013 ◽  
Vol 11 (4) ◽  
Author(s):  
Bruno Escribano ◽  
Elena Akhmatskaya ◽  
Jon Mujika
Keyword(s):  
Molecular Dynamics ◽  
Monte Carlo ◽  
High Efficiency ◽  
Rare Event ◽  
Molecular Simulations ◽  
Monte Carlo Sampling ◽  
Symplectic Integrators ◽  
Computational Time ◽  
Hybrid Monte Carlo ◽  
Event Dynamics

AbstractGeneralized Shadow Hybrid Monte Carlo (GSHMC) is a method for molecular simulations that rigorously alternates Monte Carlo sampling from a canonical ensemble with integration of trajectories using Molecular Dynamics (MD). While conventional hybrid Monte Carlo methods completely re-sample particle’s velocities between MD trajectories, our method suggests a partial velocity update procedure which keeps a part of the dynamic information throughout the simulation. We use shadow (modified) Hamiltonians, the asymptotic expansions in powers of the discretization parameter corresponding to timestep, which are conserved by symplectic integrators to higher accuracy than true Hamiltonians. We present the implementation of this method into the highly efficient MD code GROMACS and demonstrate its performance and accuracy on computationally expensive systems like proteins in comparison with the molecular dynamics techniques already available in GROMACS. We take advantage of the state-of-the-art algorithms adopted in the code, leading to an optimal implementation of the method. Our implementation introduces virtually no overhead and can accurately recreate complex biological processes, including rare event dynamics, saving much computational time compared with the conventional simulation methods.

Hybrid Monte Carlo simulations theory and initial comparison with molecular dynamics

Biopolymers ◽  
1993 ◽  
Vol 33 (8) ◽  
pp. 1307-1315 ◽  
Author(s):  
A. Brass ◽  
B. J. Pendleton ◽  
Y. Chen ◽  
B. Robson
Keyword(s):  
Molecular Dynamics ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Hybrid Monte Carlo ◽  
Initial Comparison
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