scholarly journals Automated Optimization of Water–Water Interaction Parameters for a Coarse-Grained Model

2014 ◽  
Vol 118 (6) ◽  
pp. 1603-1611 ◽  
Author(s):  
Joseph C. Fogarty ◽  
See-Wing Chiu ◽  
Peter Kirby ◽  
Eric Jakobsson ◽  
Sagar A. Pandit
Keyword(s):  
Coarse Grained ◽  
Interaction Parameters ◽  
Coarse Grained Model ◽  
Water Interaction ◽  
Automated Optimization

scholarly journals Beyond the Scales: A physics-informed machine learning approach for more efficient modeling of SARS-CoV-2 spike glycoprotein

2021 ◽  
Author(s):  
David Liang ◽  
Ziji Zhang ◽  
Miriam Rafailovich ◽  
Marcia Simon ◽  
Yuefan Deng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coarse Grained ◽  
Interaction Parameters ◽  
Learning Approach ◽  
Spike Glycoprotein ◽  
Force Matching ◽  
Coarse Grained Model ◽  
Lennard Jones ◽  
Learning Procedure ◽  
Machine Learning Approach

Abstract This paper presents a physics-informed machine learning approach to the derivation of a bottom-up coarse-grained model of the SARS-CoV-2 spike glycoprotein from all-atomic molecular dynamics simulations. The machine learning procedure employs a force-matching scheme in the optimization of interaction parameters, where the force-matching scheme is combined in methodology with the initialization of the interaction parameters by the traditional iterative Boltzmann inversion method. The force-matched machine learning procedure is constructed based on two physics-informed layers: one is the Harmonic layer consisting of bond, angle, and dihedral terms as bonded potentials; the other is the Lennard-Jones layer consisting of the non-bonded Lennard-Jones potential. Coarse-grained validation simulations are performed with the learned parameters to test the derived bottom-up coarse-grained model. The simulations are able to reach the microsecond time scale with stability. The physics-informed learning approach yields simulation speeds nearly 40,000 times faster than conventional all-atomic simulations while maintaining comparable simulation accuracy. Additionally, through examination of the non-bonded Lennard-Jones parameters and the radial distribution function analysis, the learning approach matches pairwise distances of the ground-truth data with greater accuracy than the conventional iterative approach method.

Parameterization of Divalent Cations for Coarse-Grained Simulations

2020 ◽  
Author(s):  
Florencia Klein ◽  
Daniela Cáceres-Rojas ◽  
Monica Carrasco ◽  
Juan Carlos Tapia ◽  
Julio Caballero ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Metal Ions ◽  
Molecular Dynamics Simulations ◽  
Divalent Cations ◽  
Computational Cost ◽  
Data Bank ◽  
Coarse Grained ◽  
Interaction Parameters ◽  
Dynamics Simulations ◽  
Dynamical Description

<p>Although molecular dynamics simulations allow for the study of interactions among virtually all biomolecular entities, metal ions still pose significant challenges to achieve an accurate structural and dynamical description of many biological assemblies. This is particularly the case for coarse-grained (CG) models. Although the reduced computational cost of CG methods often makes them the technique of choice for the study of large biomolecular systems, the parameterization of metal ions is still very crude or simply not available for the vast majority of CG- force fields. Here, we show that incorporating statistical data retrieved from the Protein Data Bank (PDB) to set specific Lennard-Jones interactions can produce structurally accurate CG molecular dynamics simulations. Using this simple approach, we provide a set of interaction parameters for Calcium, Magnesium, and Zinc ions, which cover more than 80% of the metal-bound structures reported on the PDB. Simulations performed using the SIRAH force field on several proteins and DNA systems show that using the present approach it is possible to obtain non-bonded interaction parameters that obviate the use of topological constraints. </p>

Insights Into Crowding Effects on Protein Stability From a Coarse-Grained Model

2009 ◽  
Vol 131 (7) ◽  
Author(s):  
Vincent K. Shen ◽  
Jason K. Cheung ◽  
Jeffrey R. Errington ◽  
Thomas M. Truskett
Keyword(s):  
Protein Stability ◽  
Coarse Grained ◽  
Protein Solutions ◽  
Native State ◽  
High Concentration ◽  
Coarse Grained Model ◽  
Excluded Volume Effects ◽  
Thermodynamic Phase ◽  
Broad Interest ◽  
Liquid Liquid Phase Separation

Proteins aggregate and precipitate from high concentration solutions in a wide variety of problems of natural and technological interest. Consequently, there is a broad interest in developing new ways to model the thermodynamic and kinetic aspects of protein stability in these crowded cellular or solution environments. We use a coarse-grained modeling approach to study the effects of different crowding agents on the conformational equilibria of proteins and the thermodynamic phase behavior of their solutions. At low to moderate protein concentrations, we find that crowding species can either stabilize or destabilize the native state, depending on the strength of their attractive interaction with the proteins. At high protein concentrations, crowders tend to stabilize the native state due to excluded volume effects, irrespective of the strength of the crowder-protein attraction. Crowding agents reduce the tendency of protein solutions to undergo a liquid-liquid phase separation driven by strong protein-protein attractions. The aforementioned equilibrium trends represent, to our knowledge, the first simulation predictions for how the properties of crowding species impact the global thermodynamic stability of proteins and their solutions.

scholarly journals Molecular Dynamics Study of Ganglioside GM3/DPPC Membrane by Using Coarse-Grained Model

2016 ◽  
Vol 110 (3) ◽  
pp. 323a
Author(s):  
Kento Inoue ◽  
Eiji Ymamoto ◽  
Daisuke Takaiwa ◽  
Kenji Yasuoka ◽  
Masuhiro Mikami
Keyword(s):  
Molecular Dynamics ◽  
Coarse Grained ◽  
Ganglioside Gm3 ◽  
Coarse Grained Model
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