scholarly journals Computational Evaluation of the COVID-19 3c-like Protease Inhibition Mechanism, and Drug Repurposing Screening

2020 ◽  
Author(s):  
Hao Liu ◽  
Tao Jiang ◽  
Wenlang Liu ◽  
Zheng Zheng
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Energy Method ◽  
Drug Repurposing ◽  
Inhibition Mechanism ◽  
Binding Affinities ◽  
Protease Inhibition ◽  
Drug Candidates ◽  
Experimental Drugs ◽  
Drugbank Database

<p>The rapid spread of the COVID-19 outbreak is now a global threat with over a million diagnosed cases and more than 70 thousand deaths. Specific treatments and effective drugs regarding such disease are in urgent need. To contribute to the drug discovery against COVID-19, we performed computational study to understand the inhibition mechanism of the COVID-19 3c-like protease, and search for possible drug candidates from approved or experimental drugs through drug repurposing screening against the DrugBank database. Two novel computational methods were applied in this study. We applied the “Consecutive Histogram Monte Carlo” (CHMC) sampling method for understanding the inhibition mechanism from studying the 2-D binding free energy landscape. We also applied the “Movable Type” (MT) free energy method for the lead compound screening by evaluating the binding free energies of the COVID-19 3c-like protease – inhibitor complexes. Lead compounds from the DrugBank database were first filtered using ligand similarity comparison to 19 published SARS 3c-like protease inhibitors. 70 selected compounds were then evaluated for protein-ligand binding affinities using the MT free energy method. 4 drug candidates with strong binding affinities and reasonable protein-ligand binding modes were selected from this study, <i>i.e.</i> Enalkiren (DB03395), Rupintrivir (DB05102), Saralasin (DB06763) and TRV-120027 (DB12199). </p>

scholarly journals Computational Evaluation of the COVID-19 3c-like Protease Inhibition Mechanism, and Drug Repurposing Screening

2020 ◽  
Author(s):  
Hao Liu ◽  
Tao Jiang ◽  
Wenlang Liu ◽  
Zheng Zheng
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Energy Method ◽  
Drug Repurposing ◽  
Inhibition Mechanism ◽  
Binding Affinities ◽  
Protease Inhibition ◽  
Drug Candidates ◽  
Experimental Drugs ◽  
Drugbank Database

<p>The rapid spread of the COVID-19 outbreak is now a global threat with over a million diagnosed cases and more than 70 thousand deaths. Specific treatments and effective drugs regarding such disease are in urgent need. To contribute to the drug discovery against COVID-19, we performed computational study to understand the inhibition mechanism of the COVID-19 3c-like protease, and search for possible drug candidates from approved or experimental drugs through drug repurposing screening against the DrugBank database. Two novel computational methods were applied in this study. We applied the “Consecutive Histogram Monte Carlo” (CHMC) sampling method for understanding the inhibition mechanism from studying the 2-D binding free energy landscape. We also applied the “Movable Type” (MT) free energy method for the lead compound screening by evaluating the binding free energies of the COVID-19 3c-like protease – inhibitor complexes. Lead compounds from the DrugBank database were first filtered using ligand similarity comparison to 19 published SARS 3c-like protease inhibitors. 70 selected compounds were then evaluated for protein-ligand binding affinities using the MT free energy method. 4 drug candidates with strong binding affinities and reasonable protein-ligand binding modes were selected from this study, <i>i.e.</i> Enalkiren (DB03395), Rupintrivir (DB05102), Saralasin (DB06763) and TRV-120027 (DB12199). </p>

scholarly journals Identification of promising drug candidates against Non-Structural Protein 15 (NSP15) from SARS-CoV-2: an in silico assisted drug-repurposing studies

2020 ◽  
Author(s):  
Rameez Jabeer Khan ◽  
Rajat Kumar Jha ◽  
Ekampreet Singh ◽  
Monika Jain ◽  
Gizachew Muluneh Amera ◽  
...  
Keyword(s):  
Free Energy ◽  
In Silico ◽  
Binding Free Energy ◽  
Drug Repurposing ◽  
Md Simulations ◽  
Therapeutic Agents ◽  
Global Dynamics ◽  
Host Immune System ◽  
Structural Protein ◽  
Drug Candidates

<div>The recent COVID-19 pandemic caused by SARS-CoV-2 has recorded a high number of infected people across the globe. The notorious nature of the virus makes it necessary for us to identify promising therapeutic agents in a time-sensitive manner. The current study utilises an <i>in silico</i> based drug repurposing approach to identify potential drug candidates targeting non-structural protein 15 (NSP15), i.e. a uridylate specific endoribonuclease of SARS-CoV-2</div><div>which plays an indispensable role in RNA processing and viral immune evasion from the host immune system. NSP15 was screened against an in-house library of 123 antiviral drugs obtained from the DrugBank database from which three promising drug candidates were identified based on their estimated free energy of binding (<i>ΔG</i>), estimated inhibition constant (<i>Ki</i>), the orientation of drug molecules in the active site and the key interacting residues of</div><div>NSP15. The MD simulations were performed for the selected NSP15-drug complexes along with free protein to mimic on their physiological state. The binding free energies of the selected NSP15-drug complexes were also calculated using the trajectories of MD simulations of NSP15-drug complexes through MM/PBSA (Molecular Mechanics with Poisson-Boltzmann and surface area solvation) approach where NSP15-Simeprevir (-242.559 kJ/mol) and NSP15-Paritaprevir (-149.557 kJ/mol) exhibited the strongest binding affinities. Together with the results of molecular docking, global dynamics, essential dynamics and binding free energy analysis, we propose that Simeprevir and Paritaprevir are promising drug candidates for the inhibition of NSP15 and could act as potential therapeutic agents against SARS-CoV-2.</div>

Free Energy Calculations to Estimate Ligand-Binding Affinities in Structure-Based Drug Design

2014 ◽  
Vol 20 (20) ◽  
pp. 3323-3337 ◽  
Author(s):  
M. Reddy ◽  
C. Reddy ◽  
R. Rathore ◽  
Mark Erion ◽  
P. Aparoy ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Design ◽  
Ligand Binding ◽  
Free Energy Calculations ◽  
Binding Affinities ◽  
Structure Based Drug Design ◽  
Energy Calculations

scholarly journals Mechanism of non-blocking inhibition of sodium channels revealed by conformation-selective photolabeling

2020 ◽  
Author(s):  
Mátyás C. Földi ◽  
Krisztina Pesti ◽  
Katalin Zboray ◽  
Tamás Hegedűs ◽  
András Málnási-Csizmadia ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Binding Site ◽  
Local Anesthetic ◽  
Binding Sites ◽  
Physiological Function ◽  
Therapeutic Potential ◽  
Inhibition Mechanism ◽  
Channel Block ◽  
Drug Candidates ◽  
Unique Mechanism

AbstractSodium channel inhibitor drugs can exert their effect by either blocking, or modulating the channel. The extent of modulation versus channel block is crucial regarding the therapeutic potential of drug candidates. Modulation can be selective for pathological hyperactivity, while channel block affects vital physiological function as much as pathological activity. Previous results indicated that riluzole, a drug with neuroprotective and antiepileptic effects, may have a unique mechanism of action, where modulation is predominant, and channel block is negligible. We studied the effects of riluzole on rNaV1.4 channels expressed in HEK cells. We observed that inhibition by riluzole disappeared and reappeared at a rate that could not be explained by association/dissociation dynamics. In order to verify the mechanism of non-blocking modulation, we synchronized photolabeling with the voltage clamp protocol of patch-clamp experiments. Using this method, we could bind a photoreactive riluzole analog covalently to specific conformations of the channel. Photolabeling was ineffective at resting conformation, but effective at inactivated conformation, as judged from persisting modulated gating after removal of unbound photoactive drug from the solution. Mutation of the key residue of the local anesthetic binding site (F1579A) did not fully prevent ligand binding and inhibition, however, it eliminated most of the modulation caused by ligand binding. Our results indicate that riluzole binds with highest affinity to the local anesthetic binding site, which transmits inhibition by the unique non-blocking modulation mechanism. Our results also suggest the existence of one or more additional binding sites, with lower affinity, and different inhibition mechanism.

scholarly journals Identification of promising drug candidates against Non-Structural Protein 15 (NSP15) from SARS-CoV-2: an in silico assisted drug-repurposing studies

2020 ◽  
Author(s):  
Rameez Jabeer Khan ◽  
Rajat Kumar Jha ◽  
Ekampreet Singh ◽  
Monika Jain ◽  
Gizachew Muluneh Amera ◽  
...  
Keyword(s):  
Free Energy ◽  
In Silico ◽  
Binding Free Energy ◽  
Drug Repurposing ◽  
Md Simulations ◽  
Therapeutic Agents ◽  
Global Dynamics ◽  
Host Immune System ◽  
Structural Protein ◽  
Drug Candidates

<div>The recent COVID-19 pandemic caused by SARS-CoV-2 has recorded a high number of infected people across the globe. The notorious nature of the virus makes it necessary for us to identify promising therapeutic agents in a time-sensitive manner. The current study utilises an <i>in silico</i> based drug repurposing approach to identify potential drug candidates targeting non-structural protein 15 (NSP15), i.e. a uridylate specific endoribonuclease of SARS-CoV-2</div><div>which plays an indispensable role in RNA processing and viral immune evasion from the host immune system. NSP15 was screened against an in-house library of 123 antiviral drugs obtained from the DrugBank database from which three promising drug candidates were identified based on their estimated free energy of binding (<i>ΔG</i>), estimated inhibition constant (<i>Ki</i>), the orientation of drug molecules in the active site and the key interacting residues of</div><div>NSP15. The MD simulations were performed for the selected NSP15-drug complexes along with free protein to mimic on their physiological state. The binding free energies of the selected NSP15-drug complexes were also calculated using the trajectories of MD simulations of NSP15-drug complexes through MM/PBSA (Molecular Mechanics with Poisson-Boltzmann and surface area solvation) approach where NSP15-Simeprevir (-242.559 kJ/mol) and NSP15-Paritaprevir (-149.557 kJ/mol) exhibited the strongest binding affinities. Together with the results of molecular docking, global dynamics, essential dynamics and binding free energy analysis, we propose that Simeprevir and Paritaprevir are promising drug candidates for the inhibition of NSP15 and could act as potential therapeutic agents against SARS-CoV-2.</div>

scholarly journals Rapid decomposition and visualisation of protein–ligand binding free energies by residue and by water

2014 ◽  
Vol 169 ◽  
pp. 477-499 ◽  
Author(s):  
Christopher J. Woods ◽  
Maturos Malaisree ◽  
Julien Michel ◽  
Ben Long ◽  
Simon McIntosh-Smith ◽  
...  
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Binding Free Energy ◽  
Chem Phys ◽  
Binding Affinities ◽  
Rapid Decomposition ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Site Water ◽  
Dynamics Simulations

Recent advances in computational hardware, software and algorithms enable simulations of protein–ligand complexes to achieve timescales during which complete ligand binding and unbinding pathways can be observed. While observation of such events can promote understanding of binding and unbinding pathways, it does not alone provide information about the molecular drivers for protein–ligand association, nor guidance on how a ligand could be optimised to better bind to the protein. We have developed the waterswap (C. J. Woods et al., J. Chem. Phys., 2011, 134, 054114) absolute binding free energy method that calculates binding affinities by exchanging the ligand with an equivalent volume of water. A significant advantage of this method is that the binding free energy is calculated using a single reaction coordinate from a single simulation. This has enabled the development of new visualisations of binding affinities based on free energy decompositions to per-residue and per-water molecule components. These provide a clear picture of which protein–ligand interactions are strong, and which active site water molecules are stabilised or destabilised upon binding. Optimisation of the algorithms underlying the decomposition enables near-real-time visualisation, allowing these calculations to be used either to provide interactive feedback to a ligand designer, or to provide run-time analysis of protein–ligand molecular dynamics simulations.

scholarly journals Drug repurposing and polypharmacology to fight SARS-CoV-2 through the inhibition of the main protease

2020 ◽  
Author(s):  
Luca Pinzi ◽  
Annachiara Tinivella ◽  
Fabiana Caporuscio ◽  
Giulio Rastelli
Keyword(s):  
Crystal Structure ◽  
Therapeutic Indication ◽  
Severe Disease ◽  
Drug Repurposing ◽  
Drug Candidates ◽  
Main Protease ◽  
The World ◽  
Novel Coronavirus ◽  
Potential Inhibitors ◽  
Drugbank Database

Abstract There is an urgent need to develop therapeutic options to fight the outbreak of a novel Coronavirus (SARS-CoV-2), which causes a disease named COVID-19 and is spreading rapidly around the world. Drug repurposing can significantly accelerate the identification of drug candidates suitable for clinical evaluation. Moreover, drugs with polypharmacological effects may increase antiviral activity and/or counteract severe disease complications concurrently affecting COVID-19 patients. Herein, we present the results of a computational drug repurposing campaign in search for potential inhibitors of the main protease of SARS-CoV-2. To this aim, the complete DrugBank database, including drug metabolites, was docked to the recently solved crystal structure of the SARS-CoV-2 Mpro and the results were post-processed by using our in-house tool BEAR. Here we report 32 promising drugs that could be repositioned to fight SARS-CoV-2. Some of them have already entered clinical trials against COVID-19, thus supporting our results, but the vast majority of the selected compounds is new and has never been considered before. For each repurposed compound its therapeutic relevance and the potential beneficial polypharmacological effects that may arise thanks to its original therapeutic indication are thoroughly discussed.

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