Protein–ligand free energies of binding from full-protein DFT calculations: convergence and choice of exchange–correlation functional

Gundelach, Lennart; Fox, Thomas; Tautermann, Christofer S.; Skylaris, Chris‐Kriton

doi:10.1039/d1cp00206f

Cited by 34 publications

(41 citation statements)

References 89 publications

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“…However, these mechanisms are independent of the QM treatment, and the investigation of such contributions can be effectively performed by coupling our model to other mechanistic treatments, such as polarizable force fields or advanced docking techniques. In addition, we here employ a common, well-established DFT approximation (PBE+D3), which already provides useful information for coarse-grained quantities and trends [21,35], and aptly fits the aim of intensive, high-throughput simulations of several structures, especially in their relaxed positions [45]. Yet, we leave out arguments about the choice of other ab initio levels of theory, which may shed light on more quantitative aspects related to processes beyond the ground-state: reaction coordinates, activation barriers, etc.…”

Section: Model Limitationsmentioning

confidence: 99%

Anticipating future SARS-CoV-2 variants of concern through ab initio quantum mechanical modeling

Zaccaria

Genovese

Farzan

et al. 2021

Preprint

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Evolved SARS-CoV-2 variants are currently challenging the efficacy of first-generation vaccines, largely through the emergence of spike protein mutants. Among these variants, Delta is presently the most concerning. We employ an ab initio quantum mechanical model based on Density Functional Theory to characterize the spike protein Receptor Binding Domain (RBD) interaction with host cells and gain mechanistic insight into SARS-CoV-2 evolution. The approach is illustrated via a detailed investigation of the role of the E484K RBD mutation, a signature mutation of the Beta and Gamma variants. The simulation is employed to: predict the depleting effect of the E484K mutation on binding the RBD with select antibodies; identify residue E484 as a weak link in the original interaction with the human receptor hACE2; and describe SARS-CoV-2 Wuhan strand binding to the bat Rhinolophus macrotis ACE2 as more optimized than the human counterpart. Finally, we predict the hACE2 binding efficacy of a hypothetical E484K mutation added to the Delta variant RBD, identifying a potential future variant of concern. Results can be generalized to other mutations, and provide useful information to complement existing experimental datasets of the interaction between randomly generated libraries of hACE2 and viral spike mutants. We argue that ab initio modeling is at the point of being aptly employed to inform and predict events pertinent to viral and general evolution.

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Section: Model Limitationsmentioning

confidence: 99%

Anticipating future SARS-CoV-2 variants of concern through ab initio quantum mechanical modeling

Zaccaria

Genovese

Farzan

et al. 2021

Preprint

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“…State-of-the-art in-silico prediction of free energies of binding usually involves a Free Energy Perturbation (FEP) protocol based on long classical molecular dynamics sim-ulations [18], which would be intractable if performed on a quantum-chemical level. A common approximation employed in quantum-mechanical studies of protein-ligand binding is to compute an average over single-point free energies of binding, calculated for selected configurations where the entropy and thermal corrections are obtained from a lower-level model [22].…”

Section: Exploratory Model Of Bace1 -Ligand Complexesmentioning

confidence: 99%

“…3 mHa), thus potentially precluding meaningful ranking of molecules for the purposes of screening [20,21]. Development of low-scaling quantummechanical methods capable of simulating proteins is ongoing, in DFT [22] and CC [23] frameworks, but clearly, there is a pressing need for accurate methods for large-scale simulations of binding energies, not only with accurate ranking of molecules in mind, but also for the purpose of benchmarking approximate methods.…”

Section: Introductionmentioning

confidence: 99%

Quantum Computational Quantification of Protein-Ligand Interactions

Kirsopp,

Di Paola,

Manrique

et al. 2021

Preprint

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We have demonstrated a prototypical hybrid classical and quantum computational workflow for the quantification of protein-ligand interactions. The workflow combines the Density Matrix Embedding Theory (DMET) embedding procedure with the Variational Quantum Eigensolver (VQE) approach for finding molecular electronic ground states. A series of β-secretase (BACE1) inhibitors is rank-ordered using binding energy differences calculated on the latest superconducting transmon (IBM) and trapped-ion (Honeywell) Noisy Intermediate Scale Quantum (NISQ) devices. This is the first application of real quantum computers to the calculation of protein-ligand binding energies. The results shed light on hardware and software requirements which would enable the application of NISQ algorithms in drug design.

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“…A potential solution to circumvent these limitations is by parallel implementations of DFT programs, which makes DFT applicable to larger systems that encapsulate a larger portion of protein molecules. [60] This could be applied to QM/MM studies and effectively enlarges the QM region treatable by DFT. Linear-scaling DFT approaches (loaded in codes such as ONETEP [61] ), allow such expansion of scopes and scales.…”

Section: Introductionmentioning

confidence: 99%

Applications of density functional theory in COVID-19 drug modeling

Yang

Liu

2022

Drug Discovery Today

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The rapidly evolving Coronavirus 2019 (COVID-19) pandemic has led to millions of deaths around the world, highlighting the pressing need to develop effective antiviral pharmaceuticals. Recent efforts with computer-aided rational drug discovery have allowed detailed examination of drug–macromolecule interactions primarily by molecular mechanics (MM) techniques. Less widely applied in COVID-19 drug modeling is density functional theory (DFT), a quantum mechanics (QM) method that enables electronic structure calculations and elucidations of reaction mechanisms. Here, we review recent advances in applying DFT in molecular modeling studies of COVID-19 pharmaceuticals. We start by providing an overview of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) drugs and targets, followed by a brief introduction to DFT. We then provide a discussion of different approaches by which DFT has been applied. Finally, we discuss essential factors to consider when incorporating DFT in future drug modeling research.

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Protein–ligand free energies of binding from full-protein DFT calculations: convergence and choice of exchange–correlation functional

Abstract: The accurate prediction of protein-ligand binding free energies with tractable computational methods has the potential to revolutionize drug discovery. Modeling the protein-ligand interaction at a quantum mechanical level, instead of...

Cited by 34 publications

References 89 publications

Anticipating future SARS-CoV-2 variants of concern through ab initio quantum mechanical modeling

Anticipating future SARS-CoV-2 variants of concern through ab initio quantum mechanical modeling

Quantum Computational Quantification of Protein-Ligand Interactions

Applications of density functional theory in COVID-19 drug modeling

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