Free Energy Calculations on Disulfide Bridges Reduction in Proteins by Combining ab Initio and Molecular Mechanics Methods

2010 ◽  
Vol 114 (8) ◽  
pp. 3020-3027 ◽  
Author(s):  
Catalina David ◽  
Mironel Enescu
Keyword(s):  
Free Energy ◽  
Ab Initio ◽  
Molecular Mechanics ◽  
Free Energy Calculations ◽  
Disulfide Bridges ◽  
Energy Calculations

Ab Initio Free Energy Calculations at Multiple Electronic Structure Levels Made Affordable: An Effective Combination of Perturbation Theory and Machine Learning

2020 ◽  
Author(s):  
Tomas Bucko ◽  
Monika Gešvandtnerová ◽  
Dario Rocca
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Perturbation Theory ◽  
Ab Initio ◽  
Single Point ◽  
Free Energy Calculations ◽  
Thermodynamic Quantities ◽  
Free Energies ◽  
Energy Calculations ◽  
Ab Initio Theory

<div>While free energies are fundamental thermodynamic quantities to characterize chemical reactions, their calculation based on ab initio theory is usually limited by the high computational cost. This is particularly true if multiple levels of theory have to be tested to establish their relative accuracy, if highly expensive quantum mechanical approximations are of interest, and also if several different temperatures have to be considered. We present an ab initio approach that effectively couples perturbation theory and machine learning to make ab initio free energy calculations more affordable. Starting from results based on a certain production ab initio theory, perturbation theory is applied to obtain free energies. The large number of single point calculations required by a brute force application of this approach are here significantly decreased by applying machine learning techniques. Importantly, the </div><div>training of the machine learning model requires only a small amount of data and does not need to be </div><div>performed again when the temperature is decreased.</div><div>The accuracy and efficiency of this method is demonstrated by computing the free energy of activation of the </div><div>proton exchange reaction in the zeolite chabazite. Starting from an ab initio calculation based on a semilocal</div><div>approximation of density functional theory, free energies based on significantly </div><div>more expensive non-local van der Waals and hybrid functionals are obtained with only a few tens</div><div>of additional single point calculations. In this way this work paves the route to</div><div>quick free energy calculations using different levels of theory or approximations that would be</div><div>too computationally expensive to be directly employed in molecular dynamics or Monte Carlo simulations.</div>

Insights to the Binding of a Selective Adenosine A3 Receptor Antagonist using Molecular Dynamics Simulations, Binding Free Energy Calculations and Mutagenesis

2019 ◽  
Author(s):  
Panagiotis Lagarias ◽  
Kerry Barkan ◽  
Eva Tzortzini ◽  
Eleni Vrontaki ◽  
Margarita Stampelou ◽  
...  
Keyword(s):  
Free Energy ◽  
Computational Model ◽  
Molecular Mechanics ◽  
Surface Area ◽  
Binding Free Energy ◽  
Md Simulations ◽  
Free Energy Calculations ◽  
Binding Energies ◽  
Energy Calculations ◽  
Binding Free Energy Calculations

<p>Adenosine A<sub>3 </sub>receptor (A<sub>3</sub>R), is a promising drug target against cancer cell proliferation. Currently there is no experimentally determined structure of A<sub>3</sub>R. Here, we have investigate a computational model, previously applied successfully for agonists binding to A<sub>3</sub>R, using molecular dynamic (MD) simulations, Molecular Mechanics-Poisson Boltzmann Surface Area (MM-PBSA) and Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) binding free energy calculations. Extensive computations were performed to explore the binding profile of O4-{[3-(2,6-dichlorophenyl)-5-methylisoxazol-4-yl]carbonyl}-2-methyl-1,3-thiazole-4-carbohydroximamide (K18) to A<sub>3</sub>R. K18 is a new specific and competitive antagonist at the orthosteric binding site of A<sub>3</sub>R, discovered using virtual screening and characterized pharmacologically in our previous studies. The most plausible binding conformation for the dichlorophenyl group of K18 inside the A<sub>3</sub>R is oriented towards trans-membrane helices (TM) 5 and 6, according to the MM-PBSA and MM-GBSA binding free energy calculations, and by the previous results obtained by mutating residues of TM5, TM6 to alanine which reduce antagonist potency. The results from 14 site-directed mutagenesis experiments were interpreted using MD simulations and MM-GBSA calculations which show that the relative binding free energies of the mutant A<sub>3</sub>R - K18 complexes compare to the WT A<sub>3</sub>R are in agreement with the effect of the mutations, i.e. the reduction, maintenance or increase of antagonist potency. We show that when the residues V169<sup>5.30</sup>, M177<sup>5.38</sup>, I249<sup>6.54</sup> involved in direct interactions with K18 are mutated to alanine, the mutant A<sub>3</sub>R - K18 complexes reduce potency, increase the RMSD value of K18 inside the binding area and the MM-GBSA binding free energy compared to the WT A<sub>3</sub>R complex. Our computational model shows that other mutant A<sub>3</sub>R complexes with K18, including directly interacting residues, i.e. F168<sup>5.29</sup>A, L246<sup>6.51</sup>A, N250<sup>6.55</sup>A complexes with K18 are not stable. In these complexes of A<sub>3</sub>R mutated in directly interacting residues one or more of the interactions between K18 and these residues are lost. In agreement with the experiments, the computations show that, M174<sup>5.35</sup> a residue which does not make direct interactions with K18 is critical for K18 binding. A striking results is that the mutation of residue V169<sup>5.30</sup> to glutamic acid maintained antagonistic potency. This effect is in agreement with the binding free energy calculations and it is suggested that is due to K18 re-orientation but also to the plasticity of A<sub>3</sub>R binding area. The mutation of direct interacting L90<sup>3.32</sup> in the low region and the non-directly interacting L264<sup>7.35</sup> to alanine in the middle region increases the antagonistic potency, suggesting that chemical modifications of K18 can be applied to augment antagonistic potency. The calculated binding energies Δ<i>G</i><sub>eff</sub> values of K18 against mutant A<sub>3</sub>Rs displayed very good correlation with experimental potencies (pA<sub>2</sub> values). These results further approve the computational model for the description of K18 binding with critical residues of the orthosteric binding area which can have implications for the design of more effective antagonists based on the structure of K18.</p>

Ab initio free energy calculations on the polymorphs of iron at core conditions

2000 ◽  
Vol 117 (1-4) ◽  
pp. 123-137 ◽  
Author(s):  
Lidunka Vočadlo ◽  
John Brodholt ◽  
Dario Alfè ◽  
Michael J. Gillan ◽  
Geoffrey D. Price
Keyword(s):  
Free Energy ◽  
Ab Initio ◽  
Free Energy Calculations ◽  
Energy Calculations ◽  
Core Conditions

scholarly journals Full atomistic reaction mechanism with kinetics for CO reduction on Cu(100) from ab initio molecular dynamics free-energy calculations at 298 K

2017 ◽  
Vol 114 (8) ◽  
pp. 1795-1800 ◽  
Author(s):  
Tao Cheng ◽  
Hai Xiao ◽  
William A. Goddard
Keyword(s):  
Free Energy ◽  
Reaction Mechanism ◽  
Ab Initio ◽  
Rational Design ◽  
Major Product ◽  
Free Energy Calculations ◽  
Ab Initio Molecular Dynamics ◽  
Applied Potential ◽  
Hydrogen Electrode ◽  
Energy Calculations

A critical step toward the rational design of new catalysts that achieve selective and efficient reduction of CO2to specific hydrocarbons and oxygenates is to determine the detailed reaction mechanism including kinetics and product selectivity as a function of pH and applied potential for known systems. To accomplish this, we apply ab initio molecular metadynamics simulations (AIMμD) for the water/Cu(100) system with five layers of the explicit solvent under a potential of −0.59 V [reversible hydrogen electrode (RHE)] at pH 7 and compare with experiment. From these free-energy calculations, we determined the kinetics and pathways for major products (ethylene and methane) and minor products (ethanol, glyoxal, glycolaldehyde, ethylene glycol, acetaldehyde, ethane, and methanol). For an applied potential (U) greater than −0.6 V (RHE) ethylene, the major product, is produced via the Eley–Rideal (ER) mechanism using H2O +e–. The rate-determining step (RDS) is C–C coupling of two CO, with ΔG‡= 0.69 eV. For an applied potential less than −0.60 V (RHE), the rate of ethylene formation decreases, mainly due to the loss of CO surface sites, which are replaced by H*. The reappearance of C2H4along with CH4atUless than −0.85 V arises from *CHO formation produced via an ER process of H* with nonadsorbed CO (a unique result). This *CHO is the common intermediate for the formation of both CH4and C2H4. These results suggest that, to obtain hydrocarbon products selectively and efficiency at pH 7, we need to increase the CO concentration by changing the solvent or alloying the surface.

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