Generate, Repurpose, Validate: A Receptor-Mediated Atom-by-Atom Drug Generation for SARS-Cov-2 Spike Protein and Similarity-Mapped Drug Repurposing for COVID-19 with Rigorous Free Energy Validation Using Well-Tempered Metadynamics

2020 ◽  
Author(s):  
Rituparno Chowdhury ◽  
Venkata Sai Sreyas Adury ◽  
Amal Vijay ◽  
Reman K. Singh ◽  
Arnab Mukherjee
Keyword(s):  
Free Energy ◽  
Drug Repurposing ◽  
Structural Similarity ◽  
Preventive Therapy ◽  
Free Energy Calculations ◽  
Human Cells ◽  
Spike Protein ◽  
Energy Calculations ◽  
Drug Molecules ◽  
Investigational Drugs

Finding a cure for Covid-19 is of immediate and paramount importance. In this study, we propose new and repurpose drugs to prevent SARS-Cov-2 (Covid-19) viral attack on human cells. Our study comprises three steps: generation of new molecules, structural similarity mapping to existing approved and investigational drugs, and validation of their binding strengths to the viral spike proteins based on rigorous all-atom well-tempered metadynamics free energy calculations. We show that some of our new molecules and some of the existing drugs bind more strongly than human ACE2 protein to the viral spike protein. Therefore, these drug molecules may have the potential to be repurposed as a preventive therapy for Covid-19, subject to further experimental verifications.

scholarly journals Generate, Repurpose, Validate: A Receptor-Mediated Atom-by-Atom Drug Generation for SARS-Cov-2 Spike Protein and Similarity-Mapped Drug Repurposing for COVID-19 with Rigorous Free Energy Validation Using Well-Tempered Metadynamics

2020 ◽  
Author(s):  
Rituparno Chowdhury ◽  
Venkata Sai Sreyas Adury ◽  
Amal Vijay ◽  
Reman K. Singh ◽  
Arnab Mukherjee
Keyword(s):  
Free Energy ◽  
Drug Repurposing ◽  
Structural Similarity ◽  
Preventive Therapy ◽  
Free Energy Calculations ◽  
Human Cells ◽  
Spike Protein ◽  
Energy Calculations ◽  
Drug Molecules ◽  
Investigational Drugs

Finding a cure for Covid-19 is of immediate and paramount importance. In this study, we propose new and repurpose drugs to prevent SARS-Cov-2 (Covid-19) viral attack on human cells. Our study comprises three steps: generation of new molecules, structural similarity mapping to existing approved and investigational drugs, and validation of their binding strengths to the viral spike proteins based on rigorous all-atom well-tempered metadynamics free energy calculations. We show that some of our new molecules and some of the existing drugs bind more strongly than human ACE2 protein to the viral spike protein. Therefore, these drug molecules may have the potential to be repurposed as a preventive therapy for Covid-19, subject to further experimental verifications.

Compared with SARS-CoV2 wild type's spike protein, the SARS-CoV2 omicron's receptor binding motif has adopted a more SARS-CoV1 and/or bat/civet-like structure.

2021 ◽  
Author(s):  
Michael O. Glocker ◽  
Kwabena F. M. Opuni ◽  
Hans-Juergen Thiesen
Keyword(s):  
Free Energy ◽  
Receptor Binding ◽  
Free Energy Calculations ◽  
Viral Fitness ◽  
Spike Protein ◽  
Receptor Protein ◽  
Binding Motif ◽  
Energy Calculations ◽  
Selection Processes ◽  
Protein Receptor

Our study focuses on free energy calculations of SARS-Cov2 spike protein receptor binding motives (RBMs) from wild type and variants-of-concern with particular emphasis on currently emerging SARS- CoV2 omicron variants of concern (VOC). Our computational free energy analysis underlines the occurrence of positive selection processes that specify omicron host adaption and bring changes on the molecular level into context with clinically relevant observations. Our free energy calculations studies regarding the interaction of omicron's RBM with human ACE2 shows weaker binding to ACE2 than alpha's, delta's, or wild type's RBM. Thus, less virus is predicted to be generated in time per infected cell. Our mutant analyses predict with focus on omicron variants a reduced spike-protein binding to ACE2--receptor protein possibly enhancing viral fitness / transmissibility and resulting in a delayed induction of danger signals as trade-off. Finally, more virus is produced but less per cell accompanied with delayed Covid-19 immunogenicity and pathogenicity. Regarding the latter, more virus is assumed to be required to initiate inflammatory immune responses.

scholarly journals Accurate calculation of the absolute free energy of binding for drug molecules

2016 ◽  
Vol 7 (1) ◽  
pp. 207-218 ◽  
Author(s):  
Matteo Aldeghi ◽  
Alexander Heifetz ◽  
Michael J. Bodkin ◽  
Stefan Knapp ◽  
Philip C. Biggin
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Free Energy Calculations ◽  
Accurate Calculation ◽  
Energy Calculations ◽  
Thermodynamic Cycles ◽  
Drug Molecules ◽  
The Absolute ◽  
Free Energy Of Binding

Free energy calculations based on molecular dynamics and thermodynamic cycles accurately reproduce experimental affinities of diverse bromodomain inhibitors.

scholarly journals SARS-CoV-2 host cell entry: an in silico investigation of potential inhibitory roles of terpenoids

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Gideon A. Gyebi ◽  
Oludare M. Ogunyemi ◽  
Ibrahim M. Ibrahim ◽  
Olalekan B. Ogunro ◽  
Adegbenro P. Adegunloye ◽  
...  
Keyword(s):  
Free Energy ◽  
In Silico ◽  
Md Simulation ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Cell Entry ◽  
Spike Protein ◽  
Energy Calculations ◽  
Wide Range ◽  
Binding Free Energy Calculations

Abstract Background Targeting viral cell entry proteins is an emerging therapeutic strategy for inhibiting the first stage of SARS-CoV-2 infection. In this study, 106 bioactive terpenoids from African medicinal plants were screened through molecular docking analysis against human angiotensin-converting enzyme 2 (hACE2), human transmembrane protease serine 2 (TMPRSS2), and the spike (S) proteins of SARS-CoV-2, SARS-CoV, and MERS-CoV. In silico absorption-distribution-metabolism-excretion-toxicity (ADMET) and drug-likeness prediction, molecular dynamics (MD) simulation, binding free energy calculations, and clustering analysis of MD simulation trajectories were performed on the top docked terpenoids to respective protein targets. Results The results revealed eight terpenoids with high binding tendencies to the catalytic residues of different targets. Two pentacyclic terpenoids (24-methylene cycloartenol and isoiguesteri) interacted with the hACE2 binding hotspots for the SARS-CoV-2 spike protein, while the abietane diterpenes were found accommodated within the S1-specificity pocket, interacting strongly with the active site residues TMPRSS2. 3-benzoylhosloppone and cucurbitacin interacted with the RBD and S2 subunit of SARS-CoV-2 spike protein respectively. These interactions were preserved in a simulated dynamic environment, thereby, demonstrating high structural stability. The MM-GBSA binding free energy calculations corroborated the docking interactions. The top docked terpenoids showed favorable drug-likeness and ADMET properties over a wide range of molecular descriptors. Conclusion The identified terpenoids from this study provides core structure that can be exploited for further lead optimization to design drugs against SARS-CoV-2 cell-mediated entry proteins. They are therefore recommended for further in vitro and in vivo studies towards developing entry inhibitors against the ongoing COVID-19 pandemic.

scholarly journals Identification of Potential Binders of the SARS-Cov-2 Spike Protein via Molecular Docking, Dynamics Simulation and Binding Free Energy Calculation

2020 ◽  
Author(s):  
Dr. Chirag N. Patel ◽  
Dr. Prasanth Kumar S. ◽  
Dr. Himanshu A. Pandya ◽  
Dr. Rakesh M. Rawal
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Molecular Docking ◽  
Molecular Dynamics Simulations ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Dynamics Simulation ◽  
Spike Protein ◽  
Energy Calculations ◽  
Dynamics Simulations

<p>The pandemic outbreak of COVID-19 virus (SARS-CoV-2) has become critical global health issue. The biophysical and structural evidence shows that SARS-CoV-2 spike protein possesses higher binding affinity towards angiotensin-converting enzyme 2 (ACE2) and hemagglutinin-acetylesterase (HE) glycoprotein receptor. Hence, it was selected as a target to generate the potential candidates for the inhibition of HE glycoprotein. The present study focuses on extensive computational approaches which contains molecular docking, ADMET prediction followed by molecular dynamics simulations and free energy calculations. Furthermore, virtual screening of NPACT compounds identified 3,4,5-Trihydroxy-1,8-bis[(2R,3R)-3,5,7-trihydroxy-3,4-dihydro-2H-chromen-2-yl]benzo[7]annulen-6-one, Silymarin, Withanolide D, Spirosolane and Oridonin were interact with high affinity. The ADMET prediction revealed pharmacokinetics and drug-likeness properties of top-ranked compounds. Molecular dynamics simulations and binding free energy calculations affirmed that these five NPACT compounds were robust HE inhibitor.</p>

scholarly journals Identification of Potential Binders of the SARS-Cov-2 Spike Protein via Molecular Docking, Dynamics Simulation and Binding Free Energy Calculation

2020 ◽  
Author(s):  
Dr. Chirag N. Patel ◽  
Dr. Prasanth Kumar S. ◽  
Dr. Himanshu A. Pandya ◽  
Dr. Rakesh M. Rawal
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Molecular Docking ◽  
Molecular Dynamics Simulations ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Dynamics Simulation ◽  
Spike Protein ◽  
Energy Calculations ◽  
Dynamics Simulations

<p>The pandemic outbreak of COVID-19 virus (SARS-CoV-2) has become critical global health issue. The biophysical and structural evidence shows that SARS-CoV-2 spike protein possesses higher binding affinity towards angiotensin-converting enzyme 2 (ACE2) and hemagglutinin-acetylesterase (HE) glycoprotein receptor. Hence, it was selected as a target to generate the potential candidates for the inhibition of HE glycoprotein. The present study focuses on extensive computational approaches which contains molecular docking, ADMET prediction followed by molecular dynamics simulations and free energy calculations. Furthermore, virtual screening of NPACT compounds identified 3,4,5-Trihydroxy-1,8-bis[(2R,3R)-3,5,7-trihydroxy-3,4-dihydro-2H-chromen-2-yl]benzo[7]annulen-6-one, Silymarin, Withanolide D, Spirosolane and Oridonin were interact with high affinity. The ADMET prediction revealed pharmacokinetics and drug-likeness properties of top-ranked compounds. Molecular dynamics simulations and binding free energy calculations affirmed that these five NPACT compounds were robust HE inhibitor.</p>

Automated Assessment of Binding Affinity via Alchemical Free Energy Calculations

2020 ◽  
Author(s):  
Maximilian Kuhn ◽  
Stuart Firth-Clark ◽  
Paolo Tosco ◽  
Antonia S. J. S. Mey ◽  
Mark Mackey ◽  
...  
Keyword(s):  
Free Energy ◽  
Protein Structures ◽  
Free Energy Calculations ◽  
Distinct Advantage ◽  
Binding Affinities ◽  
Structure Based Drug Design ◽  
Energy Calculations ◽  
Kendall’S Τ ◽  
Experimental Values ◽  
Better Than

Free energy calculations have seen increased usage in structure-based drug design. Despite the rising interest, automation of the complex calculations and subsequent analysis of their results are still hampered by the restricted choice of available tools. In this work, an application for automated setup and processing of free energy calculations is presented. Several sanity checks for assessing the reliability of the calculations were implemented, constituting a distinct advantage over existing open-source tools. The underlying workflow is built on top of the software Sire, SOMD, BioSimSpace and OpenMM and uses the AMBER14SB and GAFF2.1 force fields. It was validated on two datasets originally composed by Schrödinger, consisting of 14 protein structures and 220 ligands. Predicted binding affinities were in good agreement with experimental values. For the larger dataset the average correlation coefficient Rp was 0.70 ± 0.05 and average Kendall’s τ was 0.53 ± 0.05 which is broadly comparable to or better than previously reported results using other methods. <br>

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

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