scholarly journals In Silico Mutagenesis-Based Remodelling of SARS-CoV-1 Peptide (ATLQAIAS) to Inhibit SARS-CoV-2: Structural-Dynamics and Free Energy Calculations

2021 ◽  
Author(s):  
Abbas Khan ◽  
Shaheena Umbreen ◽  
Asma Hameed ◽  
Rida Fatima ◽  
Ujala Zahoor ◽  
...  
Keyword(s):  
Free Energy ◽  
Structural Dynamics ◽  
In Silico ◽  
Free Energy Calculations ◽  
Energy Calculations

In-silico Methods of Drug Design: Molecular Simulations and Free Energy Calculations

2019 ◽  
pp. 521-533 ◽  
Author(s):  
Fortunatus Chidolue Ezebuo ◽  
Prem P. Kushwaha ◽  
Atul K. Singh ◽  
Shashank Kumar ◽  
Pushpendra Singh
Keyword(s):  
Free Energy ◽  
Drug Design ◽  
In Silico ◽  
Molecular Simulations ◽  
Free Energy Calculations ◽  
Energy Calculations ◽  
In Silico Methods

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 Natural compounds as potential inhibitors of novel coronavirus (COVID-19) main protease: An in silico study

2020 ◽  
Author(s):  
Amaresh Mishra ◽  
Yamini Pathak ◽  
Gourav Choudhir ◽  
Anuj Kumar ◽  
Surabhi Kirti Mishra ◽  
...  
Keyword(s):  
Free Energy ◽  
In Silico ◽  
Molecular Mechanic ◽  
Natural Compounds ◽  
Free Energy Calculations ◽  
Docking Analysis ◽  
Energy Calculations ◽  
Lipinski’S Rule ◽  
Main Protease ◽  
Potential Inhibitors

Abstract COVID-19 pandemic has now expanded over 213 nations across the world. To date, there is no specific medication available for SARS CoV-2 infection. The main protease (Mpro) of SARS CoV-2 plays a crucial role in viral replication and transcription and thereby considered as an attractive drug target for the inhibition of COVID-19,. Natural compounds are widely recognised as valuabe source of antiviral drugs due to their structural diversity and safety. In the current study, we have screened twenty natural compounds having antiviral properties to discover the potential inhibitor molecules against Mpro of COVID-19. Systematic molecular docking analysis was conducted using AuroDock 4.2 to determine the binding affinities and interactions between natural compounds and the Mpro. Out of twenty molecules, four natural metabolites namely, amentoflavone, guggulsterone, puerarin, and piperine were found to have strong interaction with Mpro of COVID-19 based on the docking analysis. These selected natural compounds were further validated using combination of molecular dynamic simulations and molecular mechanic/generalized/Born/Poisson-Boltzmann surface area (MM/G/P/BSA) free energy calculations. During MD simulations, all four natural compounds bound to Mpro on 50ns and MM/G/P/BSA free energy calculations showed that all four shortlisted ligands have stable and favourable energies causing strong binding with binding site of Mpro protein. These four natural compounds have passed the Absorption, Distribution, Metabolism, and Excretion (ADME) property as well as Lipinski’s rule of five. Our promising findings based on in-silico studies warrant further clinical trials in order to use these natural compounds as potential inhibitors of Mpro protein of COVID.

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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