New force field parameters for metalloproteins I: Divalent copper ion centers including three histidine residues and an oxygen-ligated amino acid residue

2014 ◽  
Vol 35 (17) ◽  
pp. 1278-1289 ◽  
Author(s):  
Olivia Wise ◽  
Orkid Coskuner
Keyword(s):  
Amino Acid ◽  
Force Field ◽  
Amino Acid Residue ◽  
Copper Ion ◽  
Divalent Copper ◽  
Histidine Residues ◽  
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

Development of the ABEEMσπ Polarization Force Field for Base Pairs with Amino Acid Residue Complexes

2017 ◽  
Vol 13 (5) ◽  
pp. 2098-2111 ◽  
Author(s):  
Cui Liu ◽  
Yue Li ◽  
Bing-Yu Han ◽  
Li-Dong Gong ◽  
Li-Nan Lu ◽  
...  
Keyword(s):  
Amino Acid ◽  
Force Field ◽  
Amino Acid Residue ◽  
Base Pairs ◽  
Polarization Force

scholarly journals Development and Testing of Force Field Parameters for Phenylalanine and Tyrosine Derivatives

2020 ◽  
Vol 7 ◽  
Author(s):  
Xiaowen Wang ◽  
Wenjin Li
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Force Field ◽  
Unnatural Amino Acids ◽  
Unnatural Amino Acid ◽  
Tyrosine Derivatives ◽  
Force Field Parameters ◽  
Enzyme Functions ◽  
Theoretical Analyses ◽  
Good Agreement

Theoretical analyses are valuable for the exploration of the effects of unnatural amino acids on enzyme functions; however, many necessary parameters for unnatural amino acids remain lacking. In this study, we developed and tested force field parameters compatible with Amber ff14SB for 18 phenylalanine and tyrosine derivatives. The charge parameters were derived from ab initio calculations using the RESP fitting approach and then adjusted to reproduce the benchmark relative energies (at the MP2/TZ level) of the α- and β-backbones for each unnatural amino acid dipeptide. The structures optimized under the proposed force field parameters for the 18 unnatural amino acid dipeptides in both the α- and β-backbone forms were in good agreement with their QM structures, as the average RMSD was as small as 0.1 Å. The force field parameters were then tested in their application to seven proteins containing unnatural amino acids. The RMSDs of the simulated configurations of these unnatural amino acids were approximately 1.0 Å compared with those of the crystal structures. The vital interactions between proteins and unnatural amino acids in five protein–ligand complexes were also predicted using MM/PBSA analysis, and they were largely consistent with experimental observations. This work will provide theoretical aid for drug design involving unnatural amino acids.

Statistical Force-Field for Structural Modeling Using Chemical Cross-Linking/mass Spectrometry Distance Constraints

2018 ◽  
Author(s):  
Allan J. R. Ferrari ◽  
Fabio C. Gozzo ◽  
Leandro Martinez
Keyword(s):  
Mass Spectrometry ◽  
Amino Acid ◽  
Force Field ◽  
Protein Structures ◽  
Protein Structure Determination ◽  
Structural Modeling ◽  
Cross Linking ◽  
Amino Acid Residues ◽  
Distance Constraints ◽  
Chemical Cross Linking

<div><p>Chemical cross-linking/Mass Spectrometry (XLMS) is an experimental method to obtain distance constraints between amino acid residues, which can be applied to structural modeling of tertiary and quaternary biomolecular structures. These constraints provide, in principle, only upper limits to the distance between amino acid residues along the surface of the biomolecule. In practice, attempts to use of XLMS constraints for tertiary protein structure determination have not been widely successful. This indicates the need of specifically designed strategies for the representation of these constraints within modeling algorithms. Here, a force-field designed to represent XLMS-derived constraints is proposed. The potential energy functions are obtained by computing, in the database of known protein structures, the probability of satisfaction of a topological cross-linking distance as a function of the Euclidean distance between amino acid residues. The force-field can be easily incorporated into current modeling methods and software. In this work, the force-field was implemented within the Rosetta ab initio relax protocol. We show a significant improvement in the quality of the models obtained relative to current strategies for constraint representation. This force-field contributes to the long-desired goal of obtaining the tertiary structures of proteins using XLMS data. Force-field parameters and usage instructions are freely available at http://m3g.iqm.unicamp.br/topolink/xlff <br></p></div><p></p><p></p>

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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