Predicting Ligand Selectivity Across Bromodomain Families

Aldeghi, Matteo; Heifetz, Alexander; Bodkin, Michael J.; Knapp, S.; Biggin, Philip C.

doi:10.1016/j.bpj.2015.11.2915

Cited by 13 publications

(18 citation statements)

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“…Recently, Aldeghi et al reported accurate absolute free energy calculations for a set of diverse inhibitors binding to bromodomain-containing protein 4 that is based on a thermodynamic cycle. 3 They achieved a mean absolute error of 0.6 kcal/mol −1 using X-ray structures and 1.0 kcal/mol using docked structures. However, the approach was also too computationally demanding for use in screening large numbers of candidate drugs.…”

Section: ■ Accurate Prediction Of Binding Energies Is Hard and Resour...mentioning

confidence: 95%

Ligand Entropy Is Hard but Should Not Be Ignored

Winkler

2020

J. Chem. Inf. Model.

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Section: ■ Accurate Prediction Of Binding Energies Is Hard and Resour...mentioning

confidence: 95%

Ligand Entropy Is Hard but Should Not Be Ignored

Winkler

2020

J. Chem. Inf. Model.

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“…Molecular Mechanic/Poisson-Boltzmann Surface Area (MMPBSA) method was used to obtain the binding free energy of the interaction between ligandprotein complexes (Aldeghi et al, 2017). This employs ensembles derived from MD simulation.…”

Section: Mm/g/p/bsa Binding Free Energy Calculationsmentioning

confidence: 99%

Identification of phytochemical inhibitors against main protease of COVID-19 using molecular modeling approaches

Kumar

Choudhir

Shukla

et al. 2020

Preprint

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Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a novel corona virus that causes corona virus disease 2019 (COVID-19). The COVID-19 rapidly spread across the nations with high mortality rate even as very little is known to contain the virus at present. In the current study, we report novel natural metabolites namely, ursolic acid, carvacrol and oleanolic acid as the potential inhibitors against main protease (Mpro) of COVID-19 by using integrated molecular modeling approaches. From a combination of molecular docking and molecular dynamic (MD) simulations, we found three ligands bound to protease during 50 ns of MD simulations. Furthermore, the molecular mechanic/generalized/Born/Poisson-Boltzmann surface area (MM/G/P/BSA) free energy calculations showed that these chemical molecules have stable and favourable energies causing strong binding with binding site of Mpro protein. All these three molecules, namely, ursolic acid, carvacrol and oleanolic acid, have passed the ADME (Absorption, Distribution, Metabolism, and Excretion) property as well as Lipinski’s rule of five. The study provides a basic foundation and suggests that the three phytochemicals, viz. ursolic acid, carvacrol and oleanolic acid could serve as potential inhibitors in regulating the Mpro protein's function and controlling viral replication.

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“…For example, Aldeghi et al performed ABFE calculations on a set of 11 diverse bromodomain-containing protein 4 (BRD4) inhibitors that yielded an absolute error of 0.6 kcal/mol for a set of validation molecules and 1.0 kcal/mol for blinded predictions. 98 More recent work by Araujo et al presented a structure-based virtual screening protocol that culminates with ABFE calculations. 99 The authors found promising hits for ALK-5 inhibitors that were initially identified from docking, but further characterized with ABFE calculations to add confidence in the predictions, although these molecules have yet to be confirmed as ALK-5 inhibitors.…”

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confidence: 99%

“…Another advantage of ABFE calculations is the ability to compare predicted binding energies across different targets, because the binding energies computed are on an absolute scale. 98 This allows for in silico selectivity screening, where one could optimize narrow selectivity (binding to a single target and not undesirable off-targets) or broad promiscuity (multiple targets for polypharmacology or multiple protein variants to evade drug resistant mutations). While there are limited examples where this approach has been applied in the context of virtual screening, we foresee this as a high-value application area for ABFE in future vHTS campaigns.…”

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confidence: 99%

Rigorous Free Energy Simulations in Virtual Screening

Cournia

Allen

Beuming³

et al. 2020

J. Chem. Inf. Model.

181

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Virtual high throughput screening (vHTS) in drug discovery is a powerful approach to identify hits: when applied successfully, it can be much faster and cheaper than experimental high-throughput screening approaches. However, mainstream vHTS tools have significant limitations: ligand-based methods depend on knowledge of existing chemical matter, while structure-based tools such as docking involve significant approximations that limit their accuracy. Recent advances in scientific methods coupled with dramatic speedups in computational processing with GPUs make this an opportune time to consider the role of more rigorous methods that could improve the predictive power of vHTS workflows. In this Perspective, we assert that alchemical binding free energy methods using all-atom molecular dynamics simulations have matured to the point where they can be applied in virtual screening campaigns as a final scoring stage to prioritize the top molecules for experimental testing. Specifically, we propose that alchemical absolute binding free energy (ABFE) calculations offer the most direct and computationally efficient approach within a rigorous statistical thermodynamic framework for computing binding energies of diverse molecules, as is required for virtual screening. ABFE calculations are particularly attractive for drug discovery at this point in time, where the confluence of large-scale genomics data and insights from chemical biology have unveiled a large number of promising disease targets for which no small molecule binders are known, precluding ligand-based approaches, and where traditional docking approaches have foundered to find progressible chemical matter.

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Predicting Ligand Selectivity Across Bromodomain Families

Cited by 13 publications

References 0 publications

Ligand Entropy Is Hard but Should Not Be Ignored

Ligand Entropy Is Hard but Should Not Be Ignored

Identification of phytochemical inhibitors against main protease of COVID-19 using molecular modeling approaches

Rigorous Free Energy Simulations in Virtual Screening

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