Automated Parametrization of Biomolecular Force Fields from Quantum Mechanics/Molecular Mechanics (QM/MM) Simulations through Force Matching

2007 ◽  
Vol 3 (2) ◽  
pp. 628-639 ◽  
Author(s):  
Patrick Maurer ◽  
Alessandro Laio ◽  
Håkan W. Hugosson ◽  
Maria Carola Colombo ◽  
Ursula Rothlisberger
Keyword(s):  
Quantum Mechanics ◽  
Molecular Mechanics ◽  
Force Fields ◽  
Force Matching

scholarly journals Exploration and Validation of Force Field Design Protocols through QM-to-MM Mapping

2021 ◽  
Author(s):  
Chris Ringrose ◽  
Joshua Horton ◽  
Lee-Ping Wang ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Molecular Mechanics ◽  
Organic Molecule ◽  
Force Fields ◽  
Small Organic Molecule ◽  
Field Development ◽  
Force Field Development ◽  
Fitting Parameters ◽  
Made In

The scale of the parameter optimisation problem in traditional molecular mechanics force field construction means that design of a new force field is a long process, and sub-optimal choices made in the early stages can persist for many generations of the force field. We hypothesise that careful use of quantum mechanics to inform molecular mechanics parameter derivation (QM-to-MM mapping) should be used to significantly reduce the number of parameters that require fitting to experiment and increase the pace of force field development. Here, we design a collection of 15 new protocols for small, organic molecule force field design, and test their accuracy against experimental liquid properties. Our best performing model has only seven fitting parameters, yet achieves mean unsigned errors of just 0.031 g/cm3 and 0.69 kcal/mol in liquid densities and heats of vaporisation, compared to experiment. The software required to derive the designed force fields is freely available at https://github.com/qubekit/QUBEKit.

On-the-Fly Determination of Active Region Centers in Adaptive-Partitioning QM/MM

2020 ◽  
Author(s):  
Zenghui Yang
Keyword(s):  
Quantum Mechanics ◽  
Active Region ◽  
Molecular Mechanics ◽  
Active Regions ◽  
Available Energy ◽  
Adaptive Partitioning ◽  
Different Levels

Quantum mechanics/molecular mechanics (QM/MM) methods partition the system into active and environmental regions and treat them with different levels of theory, achieving accuracy and efficiency at the same time. Adaptive-partitioning (AP) QM/MM methods allow on-the-fly changes to the QM/MM partitioning of the system. Many of the available energy-based AP-QM/MM methods partition the system according to distances to pre-chosen centers of active regions. For such AP-QM/MM methods, I develop an adaptive-center (AC) method that allows on-the-fly determination of the centers of active regions according to general geometrical or potential-related criteria, extending the range of application of energy-based AP-QM/MM methods to systems where active regions may occur or vanish during the simulation.

A Conserved Asparagine in a Ubiquitin Conjugating Enzyme Positions the Substrate for Nucleophilic Attack

2018 ◽  
Author(s):  
Walker M. Jones ◽  
Aaron G. Davis ◽  
R. Hunter Wilson ◽  
Katherine L. Elliott ◽  
Isaiah Sumner
Keyword(s):  
Molecular Dynamics ◽  
Quantum Mechanics ◽  
Molecular Mechanics ◽  
Reaction Rates ◽  
Nucleophilic Attack ◽  
Classical Molecular Dynamics ◽  
Ubiquitin Conjugating Enzyme ◽  
Conjugating Enzyme

We present classical molecular dynamics (MD), Born-Oppenheimer molecular dynamics (BOMD), and hybrid quantum mechanics/molecular mechanics (QM/MM) data. MD was performed using the GPU accelerated pmemd module of the AMBER14MD package. BOMD was performed using CP2K version 2.6. The reaction rates in BOMD were accelerated using the Metadynamics method. QM/MM was performed using ONIOM in the Gaussian09 suite of programs. Relevant input files for BOMD and QM/MM are available.

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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