scholarly journals Prediction of Binding Free Energy of Protein–Ligand Complexes with a Hybrid Molecular Mechanics/Generalized Born Surface Area and Machine Learning Method

ACS Omega ◽  
2021 ◽  
Author(s):  
Lina Dong ◽  
Xiaoyang Qu ◽  
Yuan Zhao ◽  
Binju Wang
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Molecular Mechanics ◽  
Surface Area ◽  
Binding Free Energy ◽  
Machine Learning Method ◽  
Learning Method ◽  
Generalized Born

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>

scholarly journals 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>

Application of the ESMACS Binding Free Energy Protocol to a Highly Varied Ligand Dataset: Lactate Dehydogenase A

2019 ◽  
Author(s):  
David Wright ◽  
Fouad Husseini ◽  
Shunzhou Wan ◽  
Christophe Meyer ◽  
Herman Van Vlijmen ◽  
...  
Keyword(s):  
Free Energy ◽  
Surface Area ◽  
Binding Free Energy ◽  
Normal Mode Analysis ◽  
Binding Mode ◽  
Accessible Surface Area ◽  
Solvent Accessible Surface Area ◽  
Mode Analysis ◽  
Energy Calculation ◽  
Accessible Surface

<div>Here, we evaluate the performance of our range of ensemble simulation based binding free energy calculation protocols, called ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) for use in fragment based drug design scenarios. ESMACS is designed to generate reproducible binding affinity predictions from the widely used molecular mechanics Poisson-Boltzmann surface area (MMPBSA) approach. We study ligands designed to target two binding pockets in the lactate dehydogenase A target protein, which vary in size, charge and binding mode. When comparing to experimental results, we obtain excellent statistical rankings across this highly diverse set of ligands. In addition, we investigate three approaches to account for entropic contributions not captured by standard MMPBSA calculations: (1) normal mode analysis, (2) weighted solvent accessible surface area (WSAS) and (3) variational entropy. </div>

Speech Organ Contour Extraction Using Real-Time MRI and Machine Learning Method

2019 ◽  
Author(s):  
Hironori Takemoto ◽  
Tsubasa Goto ◽  
Yuya Hagihara ◽  
Sayaka Hamanaka ◽  
Tatsuya Kitamura ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Machine Learning Method ◽  
Learning Method ◽  
Contour Extraction
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