scholarly journals Long dynamics simulations of proteins using atomistic force fields and a continuum representation of solvent effects: Calculation of structural and dynamic properties

2005 ◽  
Vol 60 (3) ◽  
pp. 464-484 ◽  
Author(s):  
Xianfeng Li ◽  
Sergio A. Hassan ◽  
Ernest L. Mehler
Keyword(s):  
Solvent Effects ◽  
Force Fields ◽  
Dynamic Properties ◽  
Dynamics Simulations

Systematic Comparison of the Structural and Dynamic Properties of Commonly Used Water Models for Molecular Dynamics Simulations

2021 ◽  
Author(s):  
Sachini P. Kadaoluwa Pathirannahalage ◽  
Nastaran Meftahi ◽  
Aaron Elbourne ◽  
Alessia C. G. Weiss ◽  
Chris F. McConville ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Dynamic Properties ◽  
Systematic Comparison ◽  
Water Models ◽  
Dynamics Simulations

scholarly journals A universal framework for featurization of atomistic systems

2021 ◽  
Author(s):  
Xiangyun Lei ◽  
Andrew Medford
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Force Fields ◽  
Reactive Systems ◽  
Quantum Molecular Dynamics ◽  
Learning Models ◽  
Fixed Dimension ◽  
Comparable Performance ◽  
Invaluable Tool ◽  
Dynamics Simulations

Abstract Molecular dynamics simulations are an invaluable tool in numerous scientific fields. However, the ubiquitous classical force fields cannot describe reactive systems, and quantum molecular dynamics are too computationally demanding to treat large systems or long timescales. Reactive force fields based on physics or machine learning can be used to bridge the gap in time and length scales, but these force fields require substantial effort to construct and are highly specific to a given chemical composition and application. A significant limitation of machine learning models is the use of element-specific features, leading to models that scale poorly with the number of elements. This work introduces the Gaussian multipole (GMP) featurization scheme that utilizes physically-relevant multipole expansions of the electron density around atoms to yield feature vectors that interpolate between element types and have a fixed dimension regardless of the number of elements present. We combine GMP with neural networks to directly compare it to the widely used Behler-Parinello symmetry functions for the MD17 dataset, revealing that it exhibits improved accuracy and computational efficiency. Further, we demonstrate that GMP-based models can achieve chemical accuracy for the QM9 dataset, and their accuracy remains reasonable even when extrapolating to new elements. Finally, we test GMP-based models for the Open Catalysis Project (OCP) dataset, revealing comparable performance to graph convolutional deep learning models. The results indicate that this featurization scheme fills a critical gap in the construction of efficient and transferable machine-learned force fields.

scholarly journals The Role of Electrostatics in Enzymes: Do Biomolecular Force Fields Reflect Protein Electric Fields?

2020 ◽  
Author(s):  
Richard T Bradshaw ◽  
Jacek Dziedzic ◽  
Chris-Kriton Skylaris ◽  
Jonathan W. Essex
Keyword(s):  
Electric Fields ◽  
Active Sites ◽  
Catalytic Mechanism ◽  
Force Fields ◽  
Prolyl Isomerase ◽  
Peptidyl Prolyl Isomerase ◽  
Polarizable Force Field ◽  
Polarizable Force Fields ◽  
Protein Active Sites ◽  
Dynamics Simulations

<div><div><div><p>Preorganization of large, directionally oriented, electric fields inside protein active sites has been proposed as a crucial contributor to catalytic mechanism in many enzymes, and may be efficiently investigated at the atomistic level with molecular dynamics simulations. Here we evaluate the ability of the AMOEBA polarizable force field, as well as the additive Amber ff14SB and Charmm C36m models, to describe the electric fields present inside the active site of the peptidyl-prolyl isomerase cyclophilin A. We compare the molecular mechanical electric fields to those calculated with a fully first principles quantum mechanical (QM) representation of the protein, solvent, and ions, and find that AMOEBA consistently shows far greater correlation with the QM electric fields than either of the additive force fields tested. Catalytically-relevant fields calculated with AMOEBA were typically smaller than those observed with additive potentials, but were generally consistent with an electrostatically-driven mechanism for catalysis. Our results highlight the accuracy and the potential advantages of using polarizable force fields in systems where accurate electrostatics may be crucial for providing mechanistic insights.</p></div></div></div>

scholarly journals Self-Diffusion Coefficients of Methane/n-Hexane Mixtures at High Pressures: An Evaluation of the Finite-Size Effect and a Comparison of Force Fields

2019 ◽  
Author(s):  
Thiago José Pinheiro dos Santos ◽  
Charlles Abreu ◽  
Bruno Horta ◽  
Frederico W. Tavares
Keyword(s):  
Molecular Dynamics ◽  
Diffusion Coefficients ◽  
High Pressures ◽  
Force Fields ◽  
Transport Coefficients ◽  
Finite Size ◽  
Finite Size Effect ◽  
Self Diffusion ◽  
Analytical Correction ◽  
Dynamics Simulations

Mass transport coefficients play an important role in process design and in compositional grading of oil reservoirs. As experimental measurements of these properties can be costly and hazardous, Molecular Dynamics simulations emerge as an alternative approach. In this work, we used Molecular Dynamics to calculate the self-diffusion coefficients of methane/n-hexane mixtures at different conditions, in both liquid and supercritical phases. We evaluated how the finite box size and the choice of the force field affect the calculated properties at high pressures. Results show a strong dependency between self-diffusion and the simulation box size. The Yeh-Hummer analytical correction [J. Phys. Chem. B, 108, 15873 (2004)] can attenuate this effect, but sometimes makes the results depart from experimental data due to issues concerning the force fields. We have also found that different all-atom and united-atom models can produce biased results due to caging effects and to different dihedral configurations of the n-alkane.

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