Accurate determination of protein:ligand standard binding free energies from molecular dynamics simulations

Fu, Haohao; Chen, Haochuan; Blazhynska, Margaret M.; Lacam, Emma Goulard Coderc de; Szczepaniak, Florence; Pavlova, Ànna; Shao, Xueguang; Gumbart, James C.; Dehez, François; Roux, Benoı̂t; Cai, Wensheng; Chipot, Christophe

doi:10.1038/s41596-021-00676-1

Cited by 82 publications

(102 citation statements)

References 80 publications

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“…SR3134 also has a sulphate group and displays a similar sketch to SR3133. Although some computational techniques, such as attach-pull-release ( Velez-Vega and Gilson, 2013 ), confine-and-release ( Cacelli and Prampolini, 2007 ), and BFEE2 ( Fu et al, 2022 ) may estimate ligand binding affinities in silico , we measured the ABHD5-SPZ ligand binding by experiments. The results show that SR4559, SR3133, and SR3134 dissociate the ABHD5-PLIN5 complexes in Cos7 cell lysates with IC 50 values of 3.44 ± 0.50 × 10 −6 M, 1.59 ± 0.66 × 10 −5 , and 8.90 ± 1.84 × 10 −6 M, respectively ( Figure 6 ), indicating that all three ligands bind ABHD5.…”

Section: Resultsmentioning

confidence: 99%

Molecular Modeling of ABHD5 Structure and Ligand Recognition

Shahoei

Pangeni

Sanders

et al. 2022

Front. Mol. Biosci.

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Alpha/beta hydrolase domain-containing 5 (ABHD5), also termed CGI-58, is the key upstream activator of adipose triglyceride lipase (ATGL), which plays an essential role in lipid metabolism and energy storage. Mutations in ABHD5 disrupt lipolysis and are known to cause the Chanarin-Dorfman syndrome. Despite its importance, the structure of ABHD5 remains unknown. In this work, we combine computational and experimental methods to build a 3D structure of ABHD5. Multiple comparative and machine learning-based homology modeling methods are used to obtain possible models of ABHD5. The results from Gaussian accelerated molecular dynamics and experimental data of the apo models and their mutants are used to select the most likely model. Moreover, ensemble docking is performed on representative conformations of ABHD5 to reveal the binding mechanism of ABHD5 and a series of synthetic ligands. Our study suggests that the ABHD5 models created by deep learning-based methods are the best candidate structures for the ABHD5 protein. The mutations of E41, R116, and G328 disturb the hydrogen bonding network with nearby residues and suppress membrane targeting or ATGL activation. The simulations also reveal that the hydrophobic interactions are responsible for binding sulfonyl piperazine ligands to ABHD5. Our work provides fundamental insight into the structure of ABHD5 and its ligand-binding mode, which can be further applied to develop ABHD5 as a therapeutic target for metabolic disease and cancer.

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Section: Resultsmentioning

confidence: 99%

Molecular Modeling of ABHD5 Structure and Ligand Recognition

Shahoei

Pangeni

Sanders

et al. 2022

Front. Mol. Biosci.

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“…In the case of the protein–ligand complexes, the substrates were parametrized using the CHARMM general force field (CGenFF) through the CGenFF web server. The topology files and Cartesian coordinates from the equilibrium simulations were then piped into the Binding Free-Energy Estimator 2 (BFEE2) software, , which automatically generates the input files for binding free energy calculations with the geometrical route associated with the well-tempered meta-eABF (WTM-eABF) algorithm. , For a fair comparison, all of the separation PMFs of the shortcut route were obtained from 1 μs simulations split into a set of sequential 100 ns subruns. To make our point cogent, the length of these simulations was chosen purposely to exceed that of the complete geometrical route, amounting to about 600 ns for protein–ligand complexes and about 900 ns for protein–protein complexes.…”

Section: Theoretical Background and Methodologymentioning

confidence: 99%

“…Since convergence of the PMF calculations following the geometrical route is commonly achieved in finite-length simulations by virtue of the geometrical restraints acting on the CVs (Table ), our protocol ordinarily supplies very similar estimates in replicated simulations of protein–ligand and protein–protein complexes. , Under these premises, only a single value and its total simulation time for the restrained Δ G b ° are reported in Table . As can be observed in Table , the different values of the unrestrained Δ G b ° do not fall within k B T from the experimental measurements.…”

Section: Theoretical Background and Methodologymentioning

confidence: 99%

Hazardous Shortcuts in Standard Binding Free Energy Calculations

Blazhynska

Lacam

Chen

et al. 2022

J. Phys. Chem. Lett.

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Calculating the standard binding free energies of protein–protein and protein–ligand complexes from atomistic molecular dynamics simulations in explicit solvent is a problem of central importance in computational biophysics. A rigorous strategy for carrying out such calculations is the so-called “geometrical route”. In this method, two molecular objects are progressively separated from one another in the presence of orientational and conformational restraints serving to control the change in configurational entropy that accompanies the dissociation process, thereby allowing the computations to converge within simulations of affordable length. Although the geometrical route provides a rigorous theoretical framework, a tantalizing computational shortcut consists of simply leaving out such orientational and conformational degrees of freedom during the separation process. Here the accuracy and convergence of the two approaches are critically compared in the case of two protein–ligand complexes (Abl kinase-SH3:p41 and MDM2-p53:NVP-CGM097) and three protein–protein complexes (pig insulin dimer, SARS-CoV-2 spike RBD:ACE2, and CheA kinase-P2:CheY). The results of the simulations that strictly follow the geometrical route match the experimental standard binding free energies within chemical accuracy. In contrast, simulations bereft of geometrical restraints converge more poorly, yielding inconsistent results that are at variance with the experimental measurements. Furthermore, the orientational and positional time correlation functions of the protein in the unrestrained simulations decay over several microseconds, a time scale that is far longer than the typical simulation times of the geometrical route, which explains why those simulations fail to sample the relevant degrees of freedom during the separation process of the complexes.

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“…The binding free energy acts as a useful index to evaluate the binding affinity between mutants and drugs, and can be used as an important indicator of drug resistance ( Zhou et al, 2013 ; Ma et al, 2015 ; Khan et al, 2020 ). In this article, the standard binding free-energy calculations of all systems were performed employing BFEE2 and following a geometrical route ( Gumbart et al, 2013 ; Fu et al, 2021 ; Fu et al, 2022 ). BFEE2, which is a graphical user interface-based software, can automatically set up and analyze absolute binding free-energy calculations carried out with the popular MD engine NAMD ( Fu et al, 2021 ; Fu et al, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

“…In this article, the standard binding free-energy calculations of all systems were performed employing BFEE2 and following a geometrical route ( Gumbart et al, 2013 ; Fu et al, 2021 ; Fu et al, 2022 ). BFEE2, which is a graphical user interface-based software, can automatically set up and analyze absolute binding free-energy calculations carried out with the popular MD engine NAMD ( Fu et al, 2021 ; Fu et al, 2022 ). The calculation process of each protein-ligand complex was divided into eight independent subprocesses.…”

Section: Methodsmentioning

confidence: 99%

Uncovering the Mechanism of Drug Resistance Caused by the T790M Mutation in EGFR Kinase From Absolute Binding Free Energy Calculations

Zhou

Liu

et al. 2022

Front. Mol. Biosci.

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The emergence of drug resistance may increase the death rates in advanced non-small cell lung cancer (NSCLC) patients. The resistance of erlotinib, the effective first-line antitumor drug for NSCLC with the L858R mutation of epidermal growth factor receptor (EGFR), happens after the T790M mutation of EGFR, because this mutation causes the binding of adenosine triphosphate (ATP) to EGFR more favorable than erlotinib. However, the mechanism of the enhancement of the binding affinity of ATP to EGFR, which is of paramount importance for the development of new inhibitors, is still unclear. In this work, to explore the detailed mechanism of the drug resistance due to the T790M mutation, molecular dynamics simulations and absolute binding free energy calculations have been performed. The results show that the binding affinity of ATP with respect to the L858R/T790M mutant is higher compared with the L858R mutant, in good agreement with experiments. Further analysis demonstrates that the T790M mutation significantly changes the van der Waals interaction of ATP and the binding site. We also find that the favorable binding of ATP to the L858R/T790M mutant, compared with the L858R mutant, is due to a conformational change of the αC-helix, the A-loop and the P-loop of the latter induced by the T790M mutation. This change makes the interaction of ATP and P-loop, αC-helix in the L858R/T790M mutant higher than that in the L858R mutant, therefore increasing the binding affinity of ATP to EGFR. We believe the drug-resistance mechanism proposed in this study will provide valuable guidance for the design of drugs for NSCLC.

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Accurate determination of protein:ligand standard binding free energies from molecular dynamics simulations

Cited by 82 publications

References 80 publications

Molecular Modeling of ABHD5 Structure and Ligand Recognition

Molecular Modeling of ABHD5 Structure and Ligand Recognition

Hazardous Shortcuts in Standard Binding Free Energy Calculations

Uncovering the Mechanism of Drug Resistance Caused by the T790M Mutation in EGFR Kinase From Absolute Binding Free Energy Calculations

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