scholarly journals Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0256990
Author(s):  
Joe G. Greener ◽  
David T. Jones
Keyword(s):  
Force Field ◽  
Molecular Simulation ◽  
Protein Design ◽  
Automatic Differentiation ◽  
Force Fields ◽  
Coarse Grained ◽  
Multiple Parameters ◽  
Small Proteins ◽  
Chemical Knowledge ◽  
And Training

Finding optimal parameters for force fields used in molecular simulation is a challenging and time-consuming task, partly due to the difficulty of tuning multiple parameters at once. Automatic differentiation presents a general solution: run a simulation, obtain gradients of a loss function with respect to all the parameters, and use these to improve the force field. This approach takes advantage of the deep learning revolution whilst retaining the interpretability and efficiency of existing force fields. We demonstrate that this is possible by parameterising a simple coarse-grained force field for proteins, based on training simulations of up to 2,000 steps learning to keep the native structure stable. The learned potential matches chemical knowledge and PDB data, can fold and reproduce the dynamics of small proteins, and shows ability in protein design and model scoring applications. Problems in applying differentiable molecular simulation to all-atom models of proteins are discussed along with possible solutions and the variety of available loss functions. The learned potential, simulation scripts and training code are made available at https://github.com/psipred/cgdms.

scholarly journals Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins

2021 ◽  
Author(s):  
Joe G Greener ◽  
David T Jones
Keyword(s):  
Force Field ◽  
Molecular Simulation ◽  
Protein Design ◽  
Automatic Differentiation ◽  
Force Fields ◽  
Coarse Grained ◽  
Multiple Parameters ◽  
Small Proteins ◽  
Chemical Knowledge ◽  
And Training

AbstractFinding optimal parameters for force fields used in molecular simulation is a challenging and time-consuming task, partly due to the difficulty of tuning multiple parameters at once. Automatic differentiation presents a general solution: run a simulation, obtain gradients of a loss function with respect to all the parameters, and use these to improve the force field. This approach takes advantage of the deep learning revolution whilst retaining the interpretability and efficiency of existing force fields. We demonstrate that this is possible by parameterising a simple coarse-grained force field for proteins, based on training simulations of up to 2,000 steps learning to keep the native structure stable. The learned potential matches chemical knowledge and PDB data, can fold and reproduce the dynamics of small proteins, and shows ability in protein design and model scoring applications. Problems in applying differentiable molecular simulation to all-atom models of proteins are discussed along with possible solutions. The learned potential, simulation scripts and training code are made available at https://github.com/psipred/cgdms.

scholarly journals A new protein nucleic-acid coarse-grained force field based on the UNRES and NARES-2P force fields

2018 ◽  
Vol 39 (28) ◽  
pp. 2360-2370 ◽  
Author(s):  
Adam K. Sieradzan ◽  
Artur Giełdoń ◽  
Yanping Yin ◽  
Yi He ◽  
Harold A. Scheraga ◽  
...  
Keyword(s):  
Nucleic Acid ◽  
Force Field ◽  
Force Fields ◽  
Coarse Grained ◽  
Protein Nucleic Acid ◽  
New Protein

scholarly journals Structural-Kinetic-Thermodynamic Relationships Identified from Physics-based Molecular Simulation Models

2017 ◽  
Author(s):  
Joseph F. Rudzinski ◽  
Tristan Bereau
Keyword(s):  
Thermodynamic Properties ◽  
Force Field ◽  
Molecular Simulation ◽  
Simulation Models ◽  
Coarse Grained ◽  
Kinetic Properties ◽  
Coarse Grained Model ◽  
Forbidden Configurations ◽  
Force Field Parameters ◽  
Representative System

Coarse-grained molecular simulation models have provided immense, often general, insight into the complex behavior of condensed-phase systems, but suffer from a lost connection to the true dynamical properties of the underlying system. In general, the physics that is built into a model shapes the free-energy landscape, restricting the attainable static and kinetic properties. In this work, we perform a detailed investigation into the property interrelationships resulting from these restrictions, for a representative system of the helix-coil transition. Inspired by high-throughput studies, we systematically vary force-field parameters and monitor their structural, kinetic, and thermodynamic properties. The focus of our investigation is a simple coarse-grained model, which accurately represents the underlying structural ensemble, i.e., effectively avoids sterically-forbidden configurations. As a result of this built-in physics, we observe a rather large restriction in the topology of the networks characterizing the simulation kinetics. When screening across force-field parameters, we find that structurally-accurate models also best reproduce the kinetics, suggesting structural-kinetic relationships for these models. Additionally, an investigation into thermodynamic properties reveals a link between the cooperativity of the transition and the network topology at a single reference temperature.

Molecular simulation of osmometry in aqueous solutions of the BMIMCl ionic liquid: a potential route to force field parameterization of liquid mixtures

2020 ◽  
Vol 22 (48) ◽  
pp. 28325-28338
Author(s):  
Debdas Dhabal ◽  
Tanmoy Patra
Keyword(s):  
Experimental Data ◽  
Aqueous Solution ◽  
Ionic Liquid ◽  
Aqueous Solutions ◽  
Force Field ◽  
Molecular Simulation ◽  
Osmotic Coefficient ◽  
Force Fields ◽  
Liquid Mixtures ◽  
Force Field Parameterization

By means of molecular simulation, the osmotic coefficient of aqueous solution of BMIMCl ionic liquid is calculated to compare with the experimental data and use that to optimize two popular force fields available in the literature for bulk ILs.

scholarly journals Coarse grained force field for the molecular simulation of natural gases and condensates

2015 ◽  
Vol 406 ◽  
pp. 91-100 ◽  
Author(s):  
Carmelo Herdes ◽  
Tim S. Totton ◽  
Erich A. Müller
Keyword(s):  
Force Field ◽  
Molecular Simulation ◽  
Coarse Grained ◽  
Natural Gases

Development of accurate coarse-grained force fields for weakly polar groups by an indirect parameterization strategy

2021 ◽  
Vol 23 (11) ◽  
pp. 6763-6774
Author(s):  
Junjie Song ◽  
Mingwei Wan ◽  
Ying Yang ◽  
Lianghui Gao ◽  
Weihai Fang
Keyword(s):  
Force Field ◽  
Force Fields ◽  
Coarse Grained ◽  
Polar Groups ◽  
Force Field Parameterization

An indirect coarse-grained force field parameterization strategy for weakly polar groups.

Development of Coarse-grained Force Field for Alcohols: an Efficient Meta-Multilinear Interpolation Parameterization Algorithm

2021 ◽  
Author(s):  
Mingwei Wan ◽  
Junjie Song ◽  
Ying Yang ◽  
Lianghui Gao ◽  
Wei-hai Fang
Keyword(s):  
Molecular Dynamics ◽  
Force Field ◽  
Force Fields ◽  
Coarse Grained

Coarse-grained (CG) molecular dynamics are powerful tools to access mesoscopic phenomenon and simultaneously record microscopic details, but currently the CG force fields (FFs) are still limited by low parameterization efficiency...

scholarly journals Adsorption of CO2, N2, CH4, and their mixtures on silicalite: A critical evaluation of force fields

2020 ◽  
Vol 26 (3) ◽  
pp. 295-308
Author(s):  
Sarah Arvelos ◽  
Thalles Diógenes ◽  
Eponina Hori ◽  
Romanielo Lobato
Keyword(s):  
Force Field ◽  
Molecular Simulation ◽  
Force Fields ◽  
Critical Evaluation ◽  
Computational Power ◽  
Potential Accuracy ◽  
Simulation Results ◽  
Adsorption Of Co2 ◽  
Reliable Software ◽  
Potential Use

The use of molecular simulation has been growing in the field of engineering, fueled not just by the advances in computational power but also on the availability of reliable software. One potential use of molecular simulation is related to the screening of materials for a specific application. The reliability of molecular simulation results depends on the trustworthiness of the force field used, which for engineering purposes should be as simple as possible. This work provides an evaluation of the potential accuracy cost of using simple generic force fields to predict the adsorption of CO2, CH4, N2 and their mixtures on MFI. We employed the GCMC technique for this investigation. Different models and force fields to describe adsorbates and adsorbent were tested. The force fields performances were estimated through comparison with available adsorption experimental data. Transferability was evaluated on the prediction of pure and mixtures adsorption on CHA, LTA and FER. The results showed that a simple force field presented similar performance when compared to a more sophisticated one.

scholarly journals Molecular simulation of the thermophysical properties and phase behaviour of impure CO2 relevant to CCS

2016 ◽  
Vol 192 ◽  
pp. 415-436 ◽  
Author(s):  
Alexander J. Cresswell ◽  
Richard J. Wheatley ◽  
Richard D. Wilkinson ◽  
Richard S. Graham
Keyword(s):  
Physical Properties ◽  
Force Field ◽  
Ab Initio ◽  
Molecular Simulation ◽  
Quantum Chemical Calculations ◽  
Quantum Chemical ◽  
Force Fields ◽  
Phase Behaviour ◽  
Suitable Model ◽  
Wide Range

Impurities from the CCS chain can greatly influence the physical properties of CO2. This has important design, safety and cost implications for the compression, transport and storage of CO2. There is an urgent need to understand and predict the properties of impure CO2 to assist with CCS implementation. However, CCS presents demanding modelling requirements. A suitable model must both accurately and robustly predict CO2 phase behaviour over a wide range of temperatures and pressures, and maintain that predictive power for CO2 mixtures with numerous, mutually interacting chemical species. A promising technique to address this task is molecular simulation. It offers a molecular approach, with foundations in firmly established physical principles, along with the potential to predict the wide range of physical properties required for CCS. The quality of predictions from molecular simulation depends on accurate force-fields to describe the interactions between CO2 and other molecules. Unfortunately, there is currently no universally applicable method to obtain force-fields suitable for molecular simulation. In this paper we present two methods of obtaining force-fields: the first being semi-empirical and the second using ab initio quantum-chemical calculations. In the first approach we optimise the impurity force-field against measurements of the phase and pressure–volume behaviour of CO2 binary mixtures with N2, O2, Ar and H2. A gradient-free optimiser allows us to use the simulation itself as the underlying model. This leads to accurate and robust predictions under conditions relevant to CCS. In the second approach we use quantum-chemical calculations to produce ab initio evaluations of the interactions between CO2 and relevant impurities, taking N2 as an exemplar. We use a modest number of these calculations to train a machine-learning algorithm, known as a Gaussian process, to describe these data. The resulting model is then able to accurately predict a much broader set of ab initio force-field calculations at comparatively low numerical cost. Although our method is not yet ready to be implemented in a molecular simulation, we outline the necessary steps here. Such simulations have the potential to deliver first-principles simulation of the thermodynamic properties of impure CO2, without fitting to experimental data.

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