A Comparative Linear Interaction Energy and MM/PBSA Study on SIRT1–Ligand Binding Free Energy Calculation

Rifai, Eko Aditya; Dijk, Marc van; Vermeulen, Nico; Yanuar, Arry; Geerke, Daan P.

doi:10.1021/acs.jcim.9b00609

Cited by 70 publications

(53 citation statements)

References 100 publications

(232 reference statements)

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“…With 2fs steps, each simulation was performed at 10ns, and each model was simulated three times to generate three independent trajectory replications. Binding free energy (ΔG) was calculated using MM-PBSA method (GitHub; https://github.com/Jerkwin/gmxtool), with the trajectories after structural equilibrium assessed using RMSD (Root Mean Square Deviation) [24,35] . To calculate the equilibrium dissociation constant (K D ) and Δ G, the formula…”

Section: Molecular Dynamics (Md) Simulationmentioning

confidence: 99%

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V367F mutation in SARS-CoV-2 spike RBD emerging during the early transmission phase enhances viral infectivity through increased human ACE2 receptor binding affinity

Zhou

Dai

et al. 2020

Preprint

128

121

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250 words 25 Importance: 150 words 26 Abstract 28 A novel coronavirus SARS-CoV-2 is associated with the current global pandemic of Coronavirus 29Disease 2019 . Spike protein receptor-binding domain (RBD) of SARS-CoV-2 is the 30 critical determinant of viral tropism and infectivity. To investigate whether the mutations in the RBD 31 have altered the receptor binding affinity and caused these strains more infectious, we performed 32 molecular dynamics simulations of the binding affinity between the mutant SARS-CoV-2 RBDs to 33 date and the human ACE2 receptor. Among 1609 genomes of global SARS-CoV-2 strains, 32 34 non-synonymous RBD mutants were identified and clustered into 9 mutant types under high positive 35 selection pressure. Three mutant types (V367F, W436R, and D364Y) emerging in Wuhan, Shenzhen, 36 Hong Kong, and France, displayed higher human ACE2 affinity, and probably higher infectivity. This 37 is due to the enhanced structural stabilization of the RBD beta-sheet scaffold. High frequencies of 38 RBD mutations were identified: V367F from five France and one Hong Kong mutants, 13 V483A and 39 7 G476S mutants from the U.S.A. This suggested they originated as novel sub-lineages. The 40 enhancement of the binding affinity of the mutant type (V367F) was further validated by the 41 receptor-ligand binding ELISA assay. The molecular dynamics simulations also indicated that it 42 would be difficult for bat SARS-like CoV to infect humans. However, the pangolin CoV is potentially 43 infectious to humans. The analysis of critical RBD mutations provides further insights into the 44 evolutionary history of SARS-CoV-2 under high selection pressure. An enhancement of the 45 SARS-CoV-2 binding affinity to human ACE2 receptor reveals higher infectivity of the mutant 46 strains. 47 48 Importance 49 A novel coronavirus SARS-CoV-2 has caused the pandemic of COVID-19. The origin of 50 SARS-CoV-2 was associated with zoonotic infections. The spike protein receptor-binding domain 51 (RBD) is identified as the critical determinant of viral tropism and infectivity. Thus, whether the 52 mutations in the RBD of the circulating SARS-CoV-2 strains have altered the receptor binding 53affinity and caused these strains more infectious, should be paid more attentions to. Here, 32 54 non-synonymous RBD mutants were identified and clustered into 9 mutant types under high positive 55 . CC-BY-NC-ND 4.0 International license (which was not certified by peer review) is the author/funder. It is made available under a selection pressure, suggesting they originated as novel sub-lineages. Three mutant types displayed 56 higher human ACE2 affinity, and probably higher infectivity, one of which (V367F) was validated 57 by wet bench. The RBD mutation analysis provides insights into SARS-CoV-2 evolution. The 58 emergence of RBD mutations with increased human ACE2 affinity reveals higher risk of severe 59 morbidity and mortality during a sustained COVID-19 pandemic, particularly if no effective 60 precautions are implemented. 61 62

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Section: Molecular Dynamics (Md) Simulationmentioning

confidence: 99%

“…S477N, V367F, G485R, A520S, P384L, A522V, and P330S mutations were first detected in D614 sequences, and then with the G614 mutation. Receptor binding efficiencies of dominant mutation are evaluated by amino acid property change and by binding free energy using the MM-PBSA method [35] .…”

Section: Surface Plasmon Resonance (Spr) Experimentsmentioning

confidence: 99%

V367F mutation in SARS-CoV-2 spike RBD emerging during the early transmission phase enhances viral infectivity through increased human ACE2 receptor binding affinity

Zhou

Dai

et al. 2020

Preprint

128

121

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“…They are similar in that we are computing a free energy between two Hamiltonians, one in which an interaction is turned off. We could thus use similar techniques for computing these free energy differences, such as thermodynamic integration [54,55], BAR [56], MBAR [36,55], or MM/PBSA [57], although here we effectively use a simple free energy perturbation (FEP) expression [58,59]. The approaches are different in that we are only considering ensembles of structures where the interactions being turned off are relatively weak.…”

Section: Discussionmentioning

confidence: 99%

Correction Terms for Calculating Binding Free Energy Using Rates from Nonequilibrium Simulations

Hall¹,

Dixon²,

Dickson³

2020

Preprint

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<div>The free energy of a process is the fundamental quantity that determines its spontaneity or propensity at a given temperature. Binding free energy of a drug candidate to its biomolecular target is used as an objective quantity in drug design. Binding kinetics -- rates of association (k<sub>on</sub>) and dissociation (k<sub>off</sub>) -- have also demonstrated utility for their ability to predict efficacy and in some cases have been shown to be more predictive than the binding free energy alone. Although challenging, some methods exist to calculate binding kinetics from molecular simulations. While the kinetics of the binding process are related to the free energy by the log of their ratio, it is not straightforward to account for common, practical details pertaining to the calculation of rates in molecular simulations, such as the finite simulation volume or the particular definition of the ``bound" and ``unbound" states. Here we derive a set of correction terms that can be applied to calculations of binding free energies using rates observed in simulations. One term accounts for the particular definitions of the bound and unbound states. The second term accounts for residual electrostatic interactions that might still be present between the molecules, which is especially useful if one or both of the molecules carry an explicit charge. The third term accounts for the volume of the unbound state in the simulation box, which is useful to keep the simulated volume as small as possible during rate calculations. We apply these correction terms to revisit the calculation of binding free energies from rate constants for a host-guest system that was part of a blind prediction challenge, where significant deviations were observed between free energies calculated with rate ratios and those calculated from alchemical perturbation. The correction terms combine to significantly decrease the error with respect to computational benchmarks, from 3.4 to 0.76 kcal/mol.</div>

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“…[76] After MD, protein-ligand interaction profiles during simulation were analyzed in terms of protein residue-ligand interaction frequencies using the dedicated Python-based biomolecular analysis library MDInteract, which is freely available at https://github.com/MD-Studio/MDInteract. [77]…”

Section: Methodsmentioning

confidence: 99%

A Modified Arrhenius Approach to Thermodynamically Study Regioselectivity in Cytochrome P450‐Catalyzed Substrate Conversion

et al. 2020

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Scheme1.Kinetic scheme for the catalytic conversion of substrate St otwo possible products, P 1 and P 2 .The associatedrate constants k cat,1 and k cat,2 dependonrate constants for the individual steps of the catalytic conversion, which comprise bindingt o( k b )a nd unbinding of (k u )t he enzyme-substrate complex ES, interconversion between ES 1 and ES 2 (k i , k -i ), formation of the enzyme-product complex EP out of ES (k p ), and unbindingoft he EP complex (k r ).

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A Comparative Linear Interaction Energy and MM/PBSA Study on SIRT1–Ligand Binding Free Energy Calculation

Cited by 70 publications

References 100 publications

V367F mutation in SARS-CoV-2 spike RBD emerging during the early transmission phase enhances viral infectivity through increased human ACE2 receptor binding affinity

V367F mutation in SARS-CoV-2 spike RBD emerging during the early transmission phase enhances viral infectivity through increased human ACE2 receptor binding affinity

Correction Terms for Calculating Binding Free Energy Using Rates from Nonequilibrium Simulations

A Modified Arrhenius Approach to Thermodynamically Study Regioselectivity in Cytochrome P450‐Catalyzed Substrate Conversion

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