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 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.

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 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.

Snapshot blotting: The transfer of nucleic acids and nucleoprotein complexes from electrophoresis gels to EM grids

1992 ◽  
Vol 50 (1) ◽  
pp. 762-763
Author(s):  
Stephen D. Jett
Keyword(s):  
Nucleic Acid ◽  
Nucleic Acids ◽  
Nucleic Acid Binding ◽  
Mobility Shift ◽  
Carbon Coated ◽  
Nucleoprotein Complexes ◽  
Mobility Shift Assay ◽  
Protein Nucleic Acid ◽  
Gel Mobility Shift ◽  
Nucleic Acid Interactions

The electrophoresis gel mobility shift assay is a popular method for the study of protein-nucleic acid interactions. The binding of proteins to DNA is characterized by a reduction in the electrophoretic mobility of the nucleic acid. Binding affinity, stoichiometry, and kinetics can be obtained from such assays; however, it is often desirable to image the various species in the gel bands using TEM. Present methods for isolation of nucleoproteins from gel bands are inefficient and often destroy the native structure of the complexes. We have developed a technique, called “snapshot blotting,” by which nucleic acids and nucleoprotein complexes in electrophoresis gels can be electrophoretically transferred directly onto carbon-coated grids for TEM imaging.

Hydration Free Energies of Organic Molecules in the FreeSolv Database Calculated with Polarized Atom In Molecules Atomic Charges and the GAFF Force Field.

2018 ◽  
Author(s):  
Maximiliano Riquelme ◽  
Alejandro Lara ◽  
David L. Mobley ◽  
Toon Vestraelen ◽  
Adelio R Matamala ◽  
...  
Keyword(s):  
Force Field ◽  
Functional Groups ◽  
Electrostatic Interactions ◽  
Organic Molecules ◽  
Force Fields ◽  
Chemical Elements ◽  
Atomic Charges ◽  
Free Energies ◽  
Molecular Systems ◽  
Solvent Model

<div>Computer simulations of bio-molecular systems often use force fields, which are combinations of simple empirical atom-based functions to describe the molecular interactions. Even though polarizable force fields give a more detailed description of intermolecular interactions, nonpolarizable force fields, developed several decades ago, are often still preferred because of their reduced computation cost. Electrostatic interactions play a major role in bio-molecular systems and are therein described by atomic point charges.</div><div>In this work, we address the performance of different atomic charges to reproduce experimental hydration free energies in the FreeSolv database in combination with the GAFF force field. Atomic charges were calculated by two atoms-in-molecules approaches, Hirshfeld-I and Minimal Basis Iterative Stockholder (MBIS). To account for polarization effects, the charges were derived from the solute's electron density computed with an implicit solvent model and the energy required to polarize the solute was added to the free energy cycle. The calculated hydration free energies were analyzed with an error model, revealing systematic errors associated with specific functional groups or chemical elements. The best agreement with the experimental data is observed for the MBIS atomic charge method, including the solvent polarization, with a root mean square error of 2.0 kcal mol<sup>-1</sup> for the 613 organic molecules studied. The largest deviation was observed for phosphor-containing molecules and the molecules with amide, ester and amine functional groups.</div>

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

2019 ◽  
Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Organic Molecules ◽  
Force Fields ◽  
Liquid Density ◽  
Small Organic Molecules ◽  
Automated Generation ◽  
Energy Of Hydration ◽  
Novel Method ◽  
Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

2019 ◽  
Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Organic Molecules ◽  
Force Fields ◽  
Liquid Density ◽  
Small Organic Molecules ◽  
Automated Generation ◽  
Energy Of Hydration ◽  
Novel Method ◽  
Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

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