scholarly journals Rapid non-empirical approaches for estimating relative binding free energies.

1995 ◽  
Vol 42 (4) ◽  
pp. 525-535 ◽  
Author(s):  
A E Mark ◽  
Y Xu ◽  
H Liu ◽  
W F Van Gunsteren
Keyword(s):  
Free Energy Perturbation ◽  
Free Energies ◽  
Empirical Methods ◽  
Structure Based Drug Design ◽  
Perturbation Formula ◽  
Relative Binding ◽  
Novel Approach ◽  
Energy Perturbation ◽  
Empirical Approaches ◽  
Binding Free Energies

Rapid non-empirical methods for estimating binding free energies are reviewed. A novel approach based on the application of the free energy perturbation formula to a biased ensemble is presented. Preliminary results demonstrating the applicability of this approach in protein systems are shown and the potential of this method in structure-based drug design is discussed.

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.

scholarly journals Accurate absolute free energies for ligand–protein binding based on non-equilibrium approaches

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Vytautas Gapsys ◽  
Ahmet Yildirim ◽  
Matteo Aldeghi ◽  
Yuriy Khalak ◽  
David van der Spoel ◽  
...  
Keyword(s):  
Free Energy ◽  
First Principles ◽  
Binding Free Energy ◽  
Free Energy Perturbation ◽  
Free Energies ◽  
Hamiltonian Replica Exchange ◽  
Current State ◽  
Energy Perturbation ◽  
Binding Free Energies ◽  
Non Equilibrium

AbstractThe accurate calculation of the binding free energy for arbitrary ligand–protein pairs is a considerable challenge in computer-aided drug discovery. Recently, it has been demonstrated that current state-of-the-art molecular dynamics (MD) based methods are capable of making highly accurate predictions. Conventional MD-based approaches rely on the first principles of statistical mechanics and assume equilibrium sampling of the phase space. In the current work we demonstrate that accurate absolute binding free energies (ABFE) can also be obtained via theoretically rigorous non-equilibrium approaches. Our investigation of ligands binding to bromodomains and T4 lysozyme reveals that both equilibrium and non-equilibrium approaches converge to the same results. The non-equilibrium approach achieves the same level of accuracy and convergence as an equilibrium free energy perturbation (FEP) method enhanced by Hamiltonian replica exchange. We also compare uni- and bi-directional non-equilibrium approaches and demonstrate that considering the work distributions from both forward and reverse directions provides substantial accuracy gains. In summary, non-equilibrium ABFE calculations are shown to yield reliable and well-converged estimates of protein–ligand binding affinity.

Advances in Binding Free Energies Calculations: QM/MM-Based Free Energy Perturbation Method for Drug Design

2013 ◽  
Vol 19 (26) ◽  
pp. 4674-4686 ◽  
Author(s):  
R.S. Rathore ◽  
M. Sumakanth ◽  
M. Reddy ◽  
P. Reddanna ◽  
Allam Rao ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Design ◽  
Perturbation Method ◽  
Free Energy Perturbation ◽  
Free Energies ◽  
Energy Perturbation ◽  
Binding Free Energies

scholarly journals Critical interactions for SARS-CoV-2 spike protein binding to ACE2 identified by machine learning

2021 ◽  
Author(s):  
Anna Pavlova ◽  
Zijian Zhang ◽  
Atanu Acharya ◽  
Diane L. Lynch ◽  
Yui Tik Pang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Experimental Studies ◽  
Md Simulations ◽  
Spike Protein ◽  
Free Energy Perturbation ◽  
Receptor Binding Domain ◽  
Free Energies ◽  
Energy Perturbation ◽  
The Individual ◽  
Binding Free Energies

ABSTRACTBoth SARS-CoV and SARS-CoV-2 bind to the human ACE2 receptor. Based on high-resolution structures, the two viruses bind in practically identical conformations, although several residues of the receptor-binding domain (RBD) differ between them. Here we have used molecular dynamics (MD) simulations, machine learning (ML), and free energy perturbation (FEP) calculations to elucidate the differences in RBD binding by the two viruses. Although only subtle differences were observed from the initial MD simulations of the two RBD-ACE2 complexes, ML identified the individual residues with the most distinctive ACE2 interactions, many of which have been highlighted in previous experimental studies. FEP calculations quantified the corresponding differences in binding free energies to ACE2, and examination of MD trajectories provided structural explanations for these differences. Lastly, the energetics of emerging SARS-CoV-2 mutations were studied, showing that the affinity of the RBD for ACE2 is increased by N501Y and E484K mutations but is slightly decreased by K417N.

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