Quantum Mechanics of Collision Processes I. scattering of particles in a definite force field

1931 ◽  
Vol 3 (1) ◽  
pp. 43-88 ◽  
Author(s):  
E. U. Condon ◽  
P. M. Morse
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Collision Processes

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>

Modelling Flexible Protein-Ligand Binding in p38α MAP Kinase using the QUBE Force Field

2019 ◽  
Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Map Kinase ◽  
Binding Free Energy ◽  
Quantum Mechanical ◽  
Enhanced Sampling ◽  
Relative Binding ◽  
Stringent Test ◽  
Flexible Protein ◽  
P38α Map Kinase

<div><div><div><p>The quantum mechanical bespoke (QUBE) force field is used to retrospectively calculate the relative binding free energy of a series of 17 flexible inhibitors of p38α MAP kinase. The size and flexibility of the chosen molecules represent a stringent test of the derivation of force field parameters from quantum mechanics, and enhanced sampling is required to reduce the dependence of the results on the starting structure. Competitive accuracy with a widely-used biological force field is achieved, indicating that quantum mechanics derived force fields are approaching the accuracy required to provide guidance in prospective drug discovery campaigns.</p></div></div></div>

scholarly journals The quantum mechanics-based polarizable force field for water simulations

2018 ◽  
Vol 149 (17) ◽  
pp. 174502 ◽  
Author(s):  
Saber Naserifar ◽  
William A. Goddard
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Polarizable Force Field

scholarly journals Protein structure refinement using a quantum mechanics-based chemical shielding predictor

2016 ◽  
Author(s):  
Lars A. Bratholm ◽  
Jan H. Jensen
Keyword(s):  
Quantum Mechanics ◽  
Simulated Annealing ◽  
Chemical Shift ◽  
Force Field ◽  
Chemical Shifts ◽  
Protein Structures ◽  
Empirical Methods ◽  
Structural Refinement ◽  
Chemical Shielding ◽  
Chemical Shift Prediction

The accurate prediction of protein chemical shifts using quantum mechanics (QM)-based method has been the subject of intense research for more than 20 years but so far empirical methods for chemical shift prediction have proven more accurate. In this paper we show that a QM-based predictor of protein backbone and CB chemical shifts (ProCS15, PeerJ 2016, 3:e1344) is of comparable accuracy to empirical chemical shift predictors after chemical shift-based structural refinement that removes small structural errors. We present a method by which quantum chemistry based predictions of isotropic chemical shielding values (ProCS15) can be used to refine protein structures using Markov Chain Monte Carlo (MCMC) simulations, relating the chemical shielding values to the experimental chemical shifts probabilistically. Two kinds of MCMC structural refinement simulations were performed using force field geometry optimized X-ray structures as starting points: Simulated annealing of the starting structure and constant temperature MCMC simulation followed by simulated annealing of a representative ensemble structure. Annealing of the CHARMM structure changes the CA-RMSD by an average of 0.4 Å but lowers the chemical shift RMSD by 1.0 and 0.7 ppm for CA and N. Conformational averaging has a relatively small effect (0.1 - 0.2 ppm) on the overall agreement with carbon chemical shifts but lowers the error for nitrogen chemical shifts by 0.4 ppm. If a residue-specific offset is included the ProCS15 predicted chemical shifts have RMSD values relative to experiment that are comparable to popular empirical chemical shift predictors. The annealed representative ensemble structures differs in CA-RMSD relative to the initial structures by an average of 2.0 Å, with >2.0 Å difference for six proteins. In four of the cases, the largest structural differences arise in structurally flexible regions of the protein as determined by NMR, and in the remaining two cases, the large structural change may be due to force field deficiencies. The overall accuracy of the empirical methods are slightly improved by annealing the CHARMM structure with ProCS15, which may suggest that the minor structural changes introduced by ProCS15-based annealing improves the accuracy of the protein structures. Having established that QM-based chemical shift prediction can deliver the same accuracy as empirical shift predictors we hope this can help increase the accuracy of related approaches such as QM/MM or linear scaling approaches or interpreting protein structural dynamics from QM-derived chemical shift.

scholarly journals Protein structure refinement using a quantum mechanics-based chemical shielding predictor

2016 ◽  
Author(s):  
Lars A. Bratholm ◽  
Jan H. Jensen
Keyword(s):  
Quantum Mechanics ◽  
Simulated Annealing ◽  
Chemical Shift ◽  
Force Field ◽  
Chemical Shifts ◽  
Protein Structures ◽  
Empirical Methods ◽  
Structural Refinement ◽  
Chemical Shielding ◽  
Chemical Shift Prediction

The accurate prediction of protein chemical shifts using quantum mechanics (QM)-based method has been the subject of intense research for more than 20 years but so far empirical methods for chemical shift prediction have proven more accurate. In this paper we show that a QM-based predictor of protein backbone and CB chemical shifts (ProCS15, PeerJ 2016, 3:e1344) is of comparable accuracy to empirical chemical shift predictors after chemical shift-based structural refinement that removes small structural errors. We present a method by which quantum chemistry based predictions of isotropic chemical shielding values (ProCS15) can be used to refine protein structures using Markov Chain Monte Carlo (MCMC) simulations, relating the chemical shielding values to the experimental chemical shifts probabilistically. Two kinds of MCMC structural refinement simulations were performed using force field geometry optimized X-ray structures as starting points: Simulated annealing of the starting structure and constant temperature MCMC simulation followed by simulated annealing of a representative ensemble structure. Annealing of the CHARMM structure changes the CA-RMSD by an average of 0.4 Å but lowers the chemical shift RMSD by 1.0 and 0.7 ppm for CA and N. Conformational averaging has a relatively small effect (0.1 - 0.2 ppm) on the overall agreement with carbon chemical shifts but lowers the error for nitrogen chemical shifts by 0.4 ppm. If a residue-specific offset is included the ProCS15 predicted chemical shifts have RMSD values relative to experiment that are comparable to popular empirical chemical shift predictors. The annealed representative ensemble structures differs in CA-RMSD relative to the initial structures by an average of 2.0 Å, with >2.0 Å difference for six proteins. In four of the cases, the largest structural differences arise in structurally flexible regions of the protein as determined by NMR, and in the remaining two cases, the large structural change may be due to force field deficiencies. The overall accuracy of the empirical methods are slightly improved by annealing the CHARMM structure with ProCS15, which may suggest that the minor structural changes introduced by ProCS15-based annealing improves the accuracy of the protein structures. Having established that QM-based chemical shift prediction can deliver the same accuracy as empirical shift predictors we hope this can help increase the accuracy of related approaches such as QM/MM or linear scaling approaches or interpreting protein structural dynamics from QM-derived chemical shift.

scholarly journals Polarizable Force Field for CO2in M-MOF-74 Derived from Quantum Mechanics

2018 ◽  
Vol 122 (42) ◽  
pp. 24488-24498 ◽  
Author(s):  
Tim M. Becker ◽  
Li-Chiang Lin ◽  
David Dubbeldam ◽  
Thijs J. H. Vlugt
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Polarizable Force Field

A coarse-grain force field based on quantum mechanics (CGq FF) for molecular dynamics simulation of poly(ethylene glycol)-block-poly(ε-caprolactone) (PEG-b-PCL) micelles

2020 ◽  
Vol 22 (41) ◽  
pp. 24028-24040
Author(s):  
Maryam S. Sadeghi ◽  
Mohammad Reza Moghbeli ◽  
William A. Goddard
Keyword(s):  
Molecular Dynamics ◽  
Quantum Mechanics ◽  
Force Field ◽  
Dynamics Simulation ◽  
Coarse Grained ◽  
Coarse Grain ◽  
The Novel ◽  
Poly Ethylene Glycol ◽  
P Application ◽  
Poly Ethylene

Application of the novel quantum based coarse grained force field (CGq FF) for formation of a micelle from 250 chains of 2000 Dalton CG-MePEG23-b-PCL9 block copolymer in water at 310.15 K.

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