scholarly journals Relative Binding Free Energy Predictions for Inhibitors of Tetrameric Influenza Virus Neuraminidase

2021 ◽  
Author(s):  
Billy J Williams-Noonan ◽  
Elizabeth Yuriev ◽  
David K Chalmers
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Influenza Virus ◽  
Binding Free Energy ◽  
Test System ◽  
Rational Drug Design ◽  
Energy Methods ◽  
Ligand Complex ◽  
Relative Binding ◽  
Influenza Virus Neuraminidase

Accurate methods to predict the free energies of protein-ligand interactions have great potential to assist rational drug design. In this work, we used molecular dynamics simulations with alchemical perturbation to predict the binding of carbohydrate-based ligands to influenza virus neuraminidase (N2). This specific drug target is a challenging test system for alchemical free energy methods because it has flexible binding site motifs. We use a molecular dynamics protocol that works for longer time scales than are often reported in previous molecular dynamics studies of N2. We demonstrated that N2-ligand complex stability and that accurate N2 150-loop dynamics, on a 350 ns time scale, are both force field-dependent (AMBER99SB-ILDN, GAFF and TIP4P water are required). Further, we showed that crystallographic waters must be retained to maintain stability of N2-ligand complexes over 350 ns. Using our modelling protocol, we were able to determine relative binding free energy values between neuraminidase ligands which correlated strongly with experimental differences in pIC50 values (R = -0.96, ρ = 0.86, N = 13, sig < 0.0001). It is anticipated that the molecular dynamics protocol and the relative binding free energy methods reported here, will both be useful in expediting the discovery of novel therapeutics for N2 and other homologous glycohydrolases.

scholarly journals How quickly can we predict trimethoprim resistance using alchemical free energy methods?

2020 ◽  
Vol 10 (6) ◽  
pp. 20190141
Author(s):  
Philip W. Fowler
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Molecular Dynamics Simulations ◽  
Clinical Microbiology ◽  
Binding Free Energy ◽  
Single Mutation ◽  
Energy Methods ◽  
Synonymous Mutations ◽  
Dynamics Simulations ◽  
Alchemical Free Energy

The emergence of antimicrobial resistance threatens modern medicine and necessitates more personalized treatment of bacterial infections. Sequencing the whole genome of the pathogen(s) in a clinical sample offers one way to improve clinical microbiology diagnostic services, and has already been adopted for tuberculosis in some countries. A key weakness of a genetics clinical microbiology is it cannot return a result for rare or novel genetic variants and therefore predictive methods are required. Non-synonymous mutations in the S. aureus dfrB gene can be successfully classified as either conferring resistance (or not) by calculating their effect on the binding free energy of the antibiotic, trimethoprim. The underlying approach, alchemical free energy methods, requires large numbers of molecular dynamics simulations to be run. We show that a large number ( N = 15) of binding free energies calculated from a series of very short (50 ps) molecular dynamics simulations are able to satisfactorily classify all seven mutations in our clinically derived testset. A result for a single mutation could therefore be returned in less than an hour, thereby demonstrating that this or similar methods are now sufficiently fast and reproducible for clinical use.

scholarly journals Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision and Reproducibility

2021 ◽  
Author(s):  
Alexander Wade ◽  
Agastya Bhati ◽  
Shunzhou Wan ◽  
Peter Coveney
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Binding Free Energy ◽  
Thermodynamic Integration ◽  
Free Energy Perturbation ◽  
Free Energies ◽  
Ensemble Averaging ◽  
Relative Binding ◽  
Energy Perturbation ◽  
Binding Free Energies

The binding free energy between a ligand and its target protein is an essential quantity to know at all stages of the drug discovery pipeline. Assessing this value computationally can offer insight into where efforts should be focused in the pursuit of effective therapeutics to treat myriad diseases. In this work we examine the computation of alchemical relative binding free energies with an eye to assessing reproducibility across popular molecular dynamics packages and free energy estimators. The focus of this work is on 54 ligand transformations from a diverse set of protein targets: MCL1, PTP1B, TYK2, CDK2 and thrombin. These targets are studied with three popular molecular dynamics packages: OpenMM, NAMD2 and NAMD3. Trajectories collected with these packages are used to compare relative binding free energies calculated with thermodynamic integration and free energy perturbation methods. The resulting binding free energies show good agreement between molecular dynamics packages with an average mean unsigned error between packages of 0.5 $kcal/mol$ The correlation between packages is very good with the lowest Spearman's, Pearson's and Kendall's tau correlation coefficient between two packages being 0.91, 0.89 and 0.74 respectively. Agreement between thermodynamic integration and free energy perturbation is shown to be very good when using ensemble averaging.

Identification of potential CCR5 inhibitors through pharmacophore-based virtual screening, molecular dynamics simulation and binding free energy analysis

2016 ◽  
Vol 12 (11) ◽  
pp. 3396-3406 ◽  
Author(s):  
Juan Wang ◽  
Mao Shu ◽  
Yuanqiang Wang ◽  
Yong Hu ◽  
Yuanliang Wang ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Molecular Dynamics Simulation ◽  
Energy Analysis ◽  
Binding Free Energy ◽  
Rational Drug Design ◽  
Dynamics Simulation ◽  
Rational Drug ◽  
Combined Strategy ◽  
Free Energy Analysis

Employing the combined strategy to identify novel CCR5 inhibitors and provide a basis for rational drug design.

Virtual Compound Screening and Molecular Dynamics to Identify New Inhibitors for Human Glutathione Reductase

2020 ◽  
Vol 17 (12) ◽  
pp. 1465-1474
Author(s):  
Mohsen Sargolzaei
Keyword(s):  
Oxidative Stress ◽  
Molecular Dynamics ◽  
Free Energy ◽  
Glutathione Reductase ◽  
Binding Free Energy ◽  
Rational Drug Design ◽  
Dynamics Simulation ◽  
Reductase Inhibitors ◽  
Free Energy Decomposition

Background: Oxidative stress is a defense mechanism against malarial intracellular parasite infection. On the other hand, the Human glutathione reductase enzyme reduces oxidative stress in the cells, making the inhibitors of this enzyme a promising candidate for malaria treatment. Objective: Rational drug design was used in this work to plan new human glutathione reductase inhibitors. Methods: Virtual screening was performed using the ZINC database and molecular docking was used to detect appropriate human glutathione reductase inhibitors. Based on the docking scores obtained, the top three highest-ranked ligands were selected for the molecular dynamics simulation study. The MD simulation was performed for each complex in a length of 100 ns. Results: RMSD, RMSF and hydrogen bond analyzes were performed on the derived trajectories. Molecular mechanics generalized born surface area (MM-GBSA) and pairwise per-residue free energy decomposition analyzes were performed for the determination of binding free energy and the determination of dominant residues involved in the binding process, respectively. The binding free energy analysis showed that the molecule of 3-((7-(furan-2-ylmethyl)-5,6-diphenyl-7H-pyrrolo[2,3- d] pyrimidin-4-yl) amino) propan-1-ol is the most potent inhibitor among the molecules considered against human glutathione reductase enzyme. Conclusion: This molecule can be considered a novel candidate for antimalarial treatments.

scholarly journals How quickly can we predict trimethoprim resistance using alchemical free energy methods?

2020 ◽  
Author(s):  
Philip W Fowler
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Molecular Dynamics Simulations ◽  
Clinical Microbiology ◽  
Binding Free Energy ◽  
Single Mutation ◽  
Energy Methods ◽  
Synonymous Mutations ◽  
Dynamics Simulations ◽  
Alchemical Free Energy

AbstractThe emergence of antimicrobial resistance (AMR) threatens modern medicine and necessitates more personalised treatment of bacterial infections. Sequencing the whole genome of the pathogen(s) in a clinical sample offers one way to improve clinical microbiology diagnostic services, and has already been adopted for tuberculosis in some countries. A key weakness of a genetics clinical microbiology is it cannot return a result for rare or novel genetic variants and therefore predictive methods are required. Non-synonymous mutations in the S. aureus dfrB gene can be successfully classified as either conferring resistance (or not) by calculating their effect on the binding free energy of the antibiotic, trimethoprim. The underlying approach, alchemical free energy methods, requires large numbers of molecular dynamics simulations to be run.We show that a large number (N=15) of binding free energies calculated from a series of very short (50 ps) molecular dynamics simulations are able to satisfactorily classify all seven mutations in our clinically-derived testset. A result for a single mutation could therefore be returned in less than an hour, thereby demonstrating that this or similar methods are now sufficiently fast and reproducible for clinical use.

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