Classical force field parameters for the heme prosthetic group of cytochromec

2004 ◽  
Vol 25 (13) ◽  
pp. 1613-1622 ◽  
Author(s):  
Felix Autenrieth ◽  
Emad Tajkhorshid ◽  
Jerome Baudry ◽  
Zaida Luthey-Schulten
Keyword(s):  
Force Field ◽  
Prosthetic Group ◽  
Heme Prosthetic Group ◽  
Force Field Parameters

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>

Integrating cell-free biosyntheses of heme prosthetic group and apoenzyme for the synthesis of functional P450 monooxygenase

2012 ◽  
Vol 110 (4) ◽  
pp. 1193-1200 ◽  
Author(s):  
Yong-Chan Kwon ◽  
In-Seok Oh ◽  
Nahum Lee ◽  
Kyung-Ho Lee ◽  
Yeo Joon Yoon ◽  
...  
Keyword(s):  
Prosthetic Group ◽  
P450 Monooxygenase ◽  
Heme Prosthetic Group

Specific force field parameters determination for the hybridab initioQM/MM LSCF method

2002 ◽  
Vol 23 (6) ◽  
pp. 610-624 ◽  
Author(s):  
Nicolas Ferré ◽  
Xavier Assfeld ◽  
Jean-Louis Rivail
Keyword(s):  
Force Field ◽  
Specific Force ◽  
Force Field Parameters

scholarly journals New CHARMM force field parameters for dehydrated amino acid residues, the key to lantibiotic molecular dynamics simulations

RSC Advances ◽  
2014 ◽  
Vol 4 (89) ◽  
pp. 48621-48631 ◽  
Author(s):  
Eleanor R. Turpin ◽  
Sam Mulholland ◽  
Andrew M. Teale ◽  
Boyan B. Bonev ◽  
Jonathan D. Hirst
Keyword(s):  
Molecular Dynamics ◽  
Amino Acid ◽  
Force Field ◽  
Molecular Dynamics Simulations ◽  
Amino Acid Residues ◽  
Charmm Force Field ◽  
Force Field Parameters ◽  
Dynamics Simulations

Hydration of platinum(II) complexes: a molecular mechanics study using atom-based force-field parameters

2000 ◽  
Vol 104 (3-4) ◽  
pp. 247-251 ◽  
Author(s):  
Jacqueline Langlet ◽  
Jacqueline Berg�s ◽  
Jacqueline Caillet ◽  
Jiri Kozelka
Keyword(s):  
Force Field ◽  
Molecular Mechanics ◽  
Force Field Parameters

Tuning force field parameters of ionic liquids using machine learning techniques

2021 ◽  
Vol 200 ◽  
pp. 110759
Author(s):  
Rafikul Islam ◽  
Md Fauzul Kabir ◽  
Saugato Rahman Dhruba ◽  
Khurshida Afroz
Keyword(s):  
Machine Learning ◽  
Ionic Liquids ◽  
Force Field ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Force Field Parameters

VFFDT: A New Software for Preparing AMBER Force Field Parameters for Metal-Containing Molecular Systems

2016 ◽  
Vol 56 (4) ◽  
pp. 811-818 ◽  
Author(s):  
Suqing Zheng ◽  
Qing Tang ◽  
Jian He ◽  
Shiyu Du ◽  
Shaofang Xu ◽  
...  
Keyword(s):  
Force Field ◽  
Amber Force Field ◽  
Molecular Systems ◽  
Force Field Parameters
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