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

2021 ◽  
Author(s):  
Lennart Gundelach ◽  
Christofer S Tautermann ◽  
Thomas Fox ◽  
Chris-Kriton Skylaris
Keyword(s):  
Drug Discovery ◽  
Ligand Binding ◽  
Dft Calculations ◽  
Quantum Mechanical ◽  
Free Energies ◽  
Ligand Interaction ◽  
Exchange Correlation ◽  
Ligand Free ◽  
Protein Ligand Interaction ◽  
Binding Free Energies

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...

scholarly journals Alchemical Absolute Protein-Ligand Binding Free Energies for Drug Design

2021 ◽  
Author(s):  
Yuriy Khalak ◽  
Gary Tresdern ◽  
Matteo Aldeghi ◽  
Hannah Magdalena Baumann ◽  
David L. Mobley ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Drug Design ◽  
Ligand Binding ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Free Energies ◽  
Energy Calculations ◽  
Binding Free Energy Calculations ◽  
Binding Free Energies

The recent advances in relative protein-ligand binding free energy calculations have shown the value of alchemical methods in drug discovery. Accurately assessing absolute binding free energies, although highly desired, remains...

scholarly journals Computation of protein–ligand binding free energies using quantum mechanical bespoke force fields

MedChemComm ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 1116-1120 ◽  
Author(s):  
Daniel J. Cole ◽  
Israel Cabeza de Vaca ◽  
William L. Jorgensen
Keyword(s):  
Ligand Binding ◽  
Force Field ◽  
Molecular Mechanics ◽  
Force Fields ◽  
Quantum Mechanical ◽  
T4 Lysozyme ◽  
Free Energies ◽  
Binding Free Energies

A quantum mechanical bespoke molecular mechanics force field is derived for the L99A mutant of T4 lysozyme and used to compute absolute binding free energies of six benzene analogs to the protein.

scholarly journals Alchemical Absolute Protein-Ligand Binding Free Energies for Drug Design

2021 ◽  
Author(s):  
Yuriy Khalak ◽  
Gary Tresadern ◽  
Matteo Aldeghi ◽  
Hannah Magdalena Baumann ◽  
David L. Mobley ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Ligand Binding ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Free Energies ◽  
Energy Calculations ◽  
Binding Free Energy Calculations ◽  
Binding Free Energies ◽  
Apo State

The recent advances in relative protein-ligand binding free energy calculations have shown the value of alchemical methods in drug discovery. Accurately assessing absolute binding free energies, although highly desired, remains a challenging endeavour, mostly limited to small model cases. Here, we demonstrate accurate first principles based absolute binding free energy estimates for 128 pharmaceutically relevant targets. We use a novel rigorous method to generate protein-ligand ensembles for the ligand in its decoupled state. Not only do the calculations deliver accurate protein-ligand binding affinity estimates, but they also provide detailed physical insight into the structural determinants of binding. We identify subtle rotamer rearrangements between apo and holo states of a protein that are crucial for binding. When compared to relative binding free energy calculations, obtaining absolute binding free energies is considerably more challenging in large part due to the need to explicitly account for the protein in its apo state. In this work we present several approaches to obtain apo state ensembles for accurate absolute ΔG calculations, thus outlining protocols for prospective application of the methods for drug discovery.

Repurposing Known Drugs as Covalent and Non-Covalent Inhibitors of the SARS-CoV-2 Papain-like Protease

2020 ◽  
Author(s):  
Pietro Delre ◽  
Fabiana Caporuscio ◽  
Michele Saviano ◽  
Giuseppe Felice Mangiatordi
Keyword(s):  
Phase Iii ◽  
Free Energies ◽  
Ligand Interaction ◽  
Health Crisis ◽  
Covalent Inhibitors ◽  
Traditional Drug ◽  
Drug Discovery Program ◽  
Protein Ligand Interaction ◽  
Binding Free Energies

<p>In the absence of an approved vaccine, developing effective SARS-CoV-2 antivirals is essential to tackle the current pandemic health crisis due to the COVID-19 spread. As any traditional drug discovery program is a time-consuming and costly process requiring more than one decade to be completed, <i>in silico</i> repurposing of existing drugs is the preferred way for rapidly selecting promising clinical candidates. Herein we present a virtual screening campaign to identify covalent and non-covalent inhibitors of the SARS-CoV-2 papain-like protease (PLpro) showing potential multi-target activities for the COVID-19 treatment. A dataset including 688 phase III and 1702 phase IV clinical trial drugs was downloaded from ChEMBL (version 27.1) and docked to the recently released crystal structure of PLpro in complex with a covalently bound peptide inhibitor. The obtained results were analyzed by combining protein-ligand interaction fingerprint similarities, conventional docking scores and MMGBSA binding free energies and allowed the identification of some interesting candidates for further <i>in-vitro</i> testing. To the best of our knowledge, this study represents the first attempt to repurpose drugs for a covalent inhibition of PLpro and could pave the way for new therapeutic strategies against COVID-19.<b></b></p>

Repurposing Known Drugs as Covalent and Non-Covalent Inhibitors of the SARS-CoV-2 Papain-like Protease

2020 ◽  
Author(s):  
Pietro Delre ◽  
Fabiana Caporuscio ◽  
Michele Saviano ◽  
Giuseppe Felice Mangiatordi
Keyword(s):  
Phase Iii ◽  
Free Energies ◽  
Ligand Interaction ◽  
Health Crisis ◽  
Covalent Inhibitors ◽  
Traditional Drug ◽  
Drug Discovery Program ◽  
Protein Ligand Interaction ◽  
Binding Free Energies

<p>In the absence of an approved vaccine, developing effective SARS-CoV-2 antivirals is essential to tackle the current pandemic health crisis due to the COVID-19 spread. As any traditional drug discovery program is a time-consuming and costly process requiring more than one decade to be completed, <i>in silico</i> repurposing of existing drugs is the preferred way for rapidly selecting promising clinical candidates. Herein we present a virtual screening campaign to identify covalent and non-covalent inhibitors of the SARS-CoV-2 papain-like protease (PLpro) showing potential multi-target activities for the COVID-19 treatment. A dataset including 688 phase III and 1702 phase IV clinical trial drugs was downloaded from ChEMBL (version 27.1) and docked to the recently released crystal structure of PLpro in complex with a covalently bound peptide inhibitor. The obtained results were analyzed by combining protein-ligand interaction fingerprint similarities, conventional docking scores and MMGBSA binding free energies and allowed the identification of some interesting candidates for further <i>in-vitro</i> testing. To the best of our knowledge, this study represents the first attempt to repurpose drugs for a covalent inhibition of PLpro and could pave the way for new therapeutic strategies against COVID-19.<b></b></p>

Receptor–Ligand Binding Free Energies from a Consecutive Histograms Monte Carlo Sampling Method

2020 ◽  
Vol 16 (10) ◽  
pp. 6645-6655
Author(s):  
Hao Liu ◽  
Jianpeng Deng ◽  
Zhou Luo ◽  
Yawei Lin ◽  
Kenneth M. Merz ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Sampling Method ◽  
Monte Carlo Sampling ◽  
Free Energies ◽  
Receptor Ligand ◽  
Binding Free Energies

scholarly journals Quantum–mechanical property prediction of solvated drug molecules: what have we learned from a decade of SAMPL blind prediction challenges?

2020 ◽  
Author(s):  
Nicolas Tielker ◽  
Lukas Eberlein ◽  
Gerhard Hessler ◽  
K. Friedemann Schmidt ◽  
Stefan Güssregen ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Small Molecule ◽  
Method Development ◽  
Biological Data ◽  
Quantum Mechanical ◽  
Model Assessment ◽  
Ligand Interaction ◽  
Blind Prediction ◽  
Mechanical Methods

Abstract Joint academic–industrial projects supporting drug discovery are frequently pursued to deploy and benchmark cutting-edge methodical developments from academia in a real-world industrial environment at different scales. The dimensionality of tasks ranges from small molecule physicochemical property assessment over protein–ligand interaction up to statistical analyses of biological data. This way, method development and usability both benefit from insights gained at both ends, when predictiveness and readiness of novel approaches are confirmed, but the pharmaceutical drug makers get early access to novel tools for the quality of drug products and benefit of patients. Quantum–mechanical and simulation methods particularly fall into this group of methods, as they require skills and expense in their development but also significant resources in their application, thus are comparatively slowly dripping into the realm of industrial use. Nevertheless, these physics-based methods are becoming more and more useful. Starting with a general overview of these and in particular quantum–mechanical methods for drug discovery we review a decade-long and ongoing collaboration between Sanofi and the Kast group focused on the application of the embedded cluster reference interaction site model (EC-RISM), a solvation model for quantum chemistry, to study small molecule chemistry in the context of joint participation in several SAMPL (Statistical Assessment of Modeling of Proteins and Ligands) blind prediction challenges. Starting with early application to tautomer equilibria in water (SAMPL2) the methodology was further developed to allow for challenge contributions related to predictions of distribution coefficients (SAMPL5) and acidity constants (SAMPL6) over the years. Particular emphasis is put on a frequently overlooked aspect of measuring the quality of models, namely the retrospective analysis of earlier datasets and predictions in light of more recent and advanced developments. We therefore demonstrate the performance of the current methodical state of the art as developed and optimized for the SAMPL6 pKa and octanol–water log P challenges when re-applied to the earlier SAMPL5 cyclohexane-water log D and SAMPL2 tautomer equilibria datasets. Systematic improvement is not consistently found throughout despite the similarity of the problem class, i.e. protonation reactions and phase distribution. Hence, it is possible to learn about hidden bias in model assessment, as results derived from more elaborate methods do not necessarily improve quantitative agreement. This indicates the role of chance or coincidence for model development on the one hand which allows for the identification of systematic error and opportunities toward improvement and reveals possible sources of experimental uncertainty on the other. These insights are particularly useful for further academia–industry collaborations, as both partners are then enabled to optimize both the computational and experimental settings for data generation.

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