scholarly journals Repurposing of FDA ‐approved Drugs against Active Site and Potential Allosteric Drug Binding Sites of COVID ‐19 Main Protease

2021 ◽  
Author(s):  
Merve Yuce ◽  
Erdem Cicek ◽  
Tugce Inan ◽  
Aslihan Basak Dag ◽  
Ozge Kurkcuoglu ◽  
...  
Keyword(s):  
Active Site ◽  
Binding Sites ◽  
Drug Binding ◽  
Main Protease ◽  
Drug Binding Sites ◽  
Approved Drugs ◽  
Fda Approved Drugs

scholarly journals Repurposing of FDA-approved Drugs against Active Site and Potential Allosteric Drug Binding Sites of COVID-19 Main Protease

2021 ◽  
Author(s):  
Merve Yuce ◽  
Erdem Cicek ◽  
Tuğçe İnan ◽  
Aslıhan Başak Dağ ◽  
Özge Kürkçüoğlu ◽  
...  
Keyword(s):  
Active Site ◽  
Social Life ◽  
Binding Free Energy ◽  
Antiviral Drug ◽  
Drug Binding ◽  
Allosteric Site ◽  
Main Protease ◽  
Allosteric Sites ◽  
Approved Drugs ◽  
Fda Approved Drugs

The novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) still has serious negative effects on health, social life, and economics. Recently, vaccines from various companies have been urgently approved to control SARS-CoV-2 infections. However, any specific antiviral drug has not been confirmed so far for regular treatment. An important target is the main protease (Mpro), which plays a major role in replication of the virus. In this study, Gaussian and residue network models are employed to reveal two distinct potential allosteric sites on Mpro that can be evaluated as drug targets besides the active site. Then, FDA-approved drugs are docked to three distinct sites with flexible docking using AutoDock Vina to identify potential drug candidates. 14 best molecule hits for the active site of Mpro are determined. 6 of these also exhibit high docking scores for the potential allosteric regions. Full-atom molecular dynamics simulations with MM-GBSA method indicate that compounds docked to active and potential allosteric sites form stable interactions with high binding free energy (∆Gbind) values. ∆Gbind values reach -52.06 kcal/mol for the active site, -51.08 kcal/mol for the potential allosteric site 1, and -42.93 kcal/mol for the potential allosteric site 2. Energy decomposition calculations per residue elucidate key binding residues stabilizing the ligands that can further serve to design pharmacophores. This systematic and efficient computational analysis successfully determines ivermectine, diosmin and selinexor currently subjected to clinical trials, and further proposes bromocriptine, elbasvir as Mpro inhibitor candidates to be evaluated against SARS-CoV-2 infection

scholarly journals COVID19 Approved Drug Repurposing: Pocket Similarity Approach

2020 ◽  
Author(s):  
Mohamed Fadlalla
Keyword(s):  
Binding Site ◽  
Binding Sites ◽  
Drug Repurposing ◽  
Drug Binding ◽  
Drug Binding Site ◽  
Main Protease ◽  
Major Outbreak ◽  
Target Binding ◽  
Approved Drugs

<p>SARS CoV 2 has spread worldwide and caused a major outbreak of coronavirus disease 2019 (COVID-19). To date, no licensed drug or a vaccine is available against COVID19.</p><p>Starting from all of the resolved SARS CoV2 crystal structures, this study aims to find inhibitors for all of the SARS CoV2 proteins. To achieve this, I used PocketMatch to test the similarity of approved drugs binding sites against all of the binding sites found on SARS CoV 2 proteins. After that docking was used to confirm the results.</p><p>I found drugs that inhibit the main protease, Nsp12 and Nsp3. The discovered drugs are either in clinical trials (Sildenafil, Lopinavir, Ritonavir) or have in vitro antiviral activity (Nelfinavir, Indinavir, Amprenavir, depiqulinum , Gemcitabine, Raltitrexed, Aprepitant, montelukast, Ouabain, Raloxifene) whether against SARS CoV 2 or other viruses. In addition to this, further analysis of pockets revealed a steroidal pocket that might open the door to hypotheses on why the mortality of men is higher than women.</p><p>Many of the in silico repurposing studies test binding of the compound to the target using docking. The significance of this study adds to the similarity between the drug binding site and the target binding site. This takes into consideration the dynamic behaviour of the pocket after ligand binding.</p><div><br></div>

scholarly journals Drug Binding Dynamics of the Dimeric SARS-CoV-2 Main Protease, Determined by Molecular Dynamics Simulation

2020 ◽  
Author(s):  
Teruhisa S. KOMATSU ◽  
Noriaki Okimoto ◽  
Yohei M. KOYAMA ◽  
Yoshinori HIRANO ◽  
Gentaro MORIMOTO ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Active Site ◽  
Binding Sites ◽  
Complex Structure ◽  
Drug Binding ◽  
Dynamics Simulation ◽  
Trajectory Data ◽  
Main Protease ◽  
Dynamic Sampling

<div> <div> <div> <p>We performed molecular dynamics simulation of the dimeric SARS-CoV-2 (severe acute respiratory syndrome corona virus 2) main protease (Mpro) to examine the binding dynamics of small molecular ligands. Seven HIV inhibitors, darunavir, indinavir, lopinavir, nelfinavir, ritonavir, saquinavir, and tipranavir, were used as the potential lead drugs to investigate access to the drug binding sites in Mpro. The frequently accessed sites on Mpro were classified based on contacts between the ligands and the protein, and the differences in site distributions of the encounter complex were observed among the ligands. All seven ligands showed binding to the active site at least twice in 28 simulations of 200 ns each. We further investigated the variations in the complex structure of the active site with the ligands, using microsecond order simulations. Results revealed a wide variation in the shapes of the binding sites and binding poses of the ligands. Additionally, the C-terminal region of the other chain often interacted with the ligands and the active site. Collectively, these findings indicate the importance of dynamic sampling of protein- ligand complexes and suggest the possibilities of further drug optimisations. <br></p><p><br></p><p><br> </p><div> <div> <div> <p>Raw trajectory data analysed in this paper and movie examples are available at the zenodo repository.<br></p> </div> </div> </div> </div> </div> </div>

scholarly journals Repurposing of FDA approved drugs targeting Main protease MPro for SARS-CoV-2

2020 ◽  
Author(s):  
Suryakant Tiwari ◽  
Raghav Jain ◽  
Indrani Banerjee
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Active Site ◽  
Ligand Docking ◽  
Kcal Mole ◽  
Main Protease ◽  
Alternative Approach ◽  
Repurposed Drugs ◽  
Approved Drugs ◽  
Fda Approved Drugs

Abstract SARS-CoV-2 is one of the greatest pandemics in the history. There is no medicine or vaccine yet discovered to control the outbreak. The paper deals with repurposing existing drugs to control the outbreak of SARS-CoV-2 virus.Ten FDA-approved drugs namely Indinavir, Nelfinavir, Letermovir, Irinotecan, Elbasvir, Saquinavir, Darunavir, Raltegravir, Atazanavir and Amprenavir were studied. In silico methods for virtual screening of protein-ligand docking of these drugs against SARS-CoV-2 MPro was performed. The binding efficiency of the drugs against viral main protease MPro was significantly high to inhibit SARS-CoV-2.The results confirmed that Atazanavir, Nelfinavir, and Letermovir not only occupied the active site of Mpro but also showed increased binding affinity (-10.36 kcal/mole, -9.47 kcal/mole and -9.43 kcal/mole) even more than of control drugs of Lopinavir (-8.71 kcal/mole) and Ritonavir (-8.08 kcal/mole). These repurposed drugs can be used in combination or individually as an alternative approach for rapid drug discovery against SARS-CoV-2

scholarly journals Drug binding dynamics of the dimeric SARS-CoV-2 main protease, determined by molecular dynamics simulation

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Teruhisa S. Komatsu ◽  
Noriaki Okimoto ◽  
Yohei M. Koyama ◽  
Yoshinori Hirano ◽  
Gentaro Morimoto ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Active Site ◽  
Binding Sites ◽  
Complex Structure ◽  
Drug Binding ◽  
Dynamics Simulation ◽  
Main Protease ◽  
Encounter Complex ◽  
Dynamic Sampling

Abstract We performed molecular dynamics simulation of the dimeric SARS-CoV-2 (severe acute respiratory syndrome corona virus 2) main protease (Mpro) to examine the binding dynamics of small molecular ligands. Seven HIV inhibitors, darunavir, indinavir, lopinavir, nelfinavir, ritonavir, saquinavir, and tipranavir, were used as the potential lead drugs to investigate access to the drug binding sites in Mpro. The frequently accessed sites on Mpro were classified based on contacts between the ligands and the protein, and the differences in site distributions of the encounter complex were observed among the ligands. All seven ligands showed binding to the active site at least twice in 28 simulations of 200 ns each. We further investigated the variations in the complex structure of the active site with the ligands, using microsecond order simulations. Results revealed a wide variation in the shapes of the binding sites and binding poses of the ligands. Additionally, the C-terminal region of the other chain often interacted with the ligands and the active site. Collectively, these findings indicate the importance of dynamic sampling of protein–ligand complexes and suggest the possibilities of further drug optimisations.

scholarly journals Finding Druggable Sites in Proteins using TACTICS

2021 ◽  
Author(s):  
Daniel J. Evans ◽  
Remy A. Yovanno ◽  
Sanim Rahman ◽  
David W. Cao ◽  
Morgan Q. Beckett ◽  
...  
Keyword(s):  
Binding Sites ◽  
Learning Algorithm ◽  
Drug Binding ◽  
Molecular Structures ◽  
Dynamics Simulation ◽  
Conformational Heterogeneity ◽  
Main Protease ◽  
Binding Pockets ◽  
Drug Binding Sites ◽  
Fragment Docking

AbstractStructure-based drug discovery efforts require knowledge of where drug-binding sites are located on target proteins. To address the challenge of finding druggable sites, we developed a machine-learning algorithm called TACTICS (Trajectory-based Analysis of Conformations To Identify Cryptic Sites), which uses an ensemble of molecular structures (such as molecular dynamics simulation data) as input. First, TACTICS uses k-means clustering to select a small number of conformations that represent the overall conformational heterogeneity of the data. Then, TACTICS uses a random forest model to identify potentially bindable residues in each selected conformation, based on protein motion and geometry. Lastly, residues in possible binding pockets are scored using fragment docking. As proof-of-principle, TACTICS was applied to the analysis of simulations of the SARS-CoV-2 main protease and methyltransferase and the Yersinia pestis aryl carrier protein. Our approach recapitulates known small-molecule binding sites and predicts the locations of sites not previously observed in experimentally determined structures. The TACTICS code is available at https://github.com/Albert-Lau-Lab/tactics_protein_analysis.

scholarly journals Drug Binding Dynamics of the Dimeric SARS-CoV-2 Main Protease, Determined by Molecular Dynamics Simulation

2020 ◽  
Author(s):  
Teruhisa S. KOMATSU ◽  
Noriaki Okimoto ◽  
Yohei M. KOYAMA ◽  
Yoshinori HIRANO ◽  
Gentaro MORIMOTO ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Active Site ◽  
Binding Sites ◽  
Complex Structure ◽  
Drug Binding ◽  
Dynamics Simulation ◽  
Trajectory Data ◽  
Main Protease ◽  
Dynamic Sampling

<div> <div> <div> <p>We performed molecular dynamics simulation of the dimeric SARS-CoV-2 (severe acute respiratory syndrome corona virus 2) main protease (Mpro) to examine the binding dynamics of small molecular ligands. Seven HIV inhibitors, darunavir, indinavir, lopinavir, nelfinavir, ritonavir, saquinavir, and tipranavir, were used as the potential lead drugs to investigate access to the drug binding sites in Mpro. The frequently accessed sites on Mpro were classified based on contacts between the ligands and the protein, and the differences in site distributions of the encounter complex were observed among the ligands. All seven ligands showed binding to the active site at least twice in 28 simulations of 200 ns each. We further investigated the variations in the complex structure of the active site with the ligands, using microsecond order simulations. Results revealed a wide variation in the shapes of the binding sites and binding poses of the ligands. Additionally, the C-terminal region of the other chain often interacted with the ligands and the active site. Collectively, these findings indicate the importance of dynamic sampling of protein- ligand complexes and suggest the possibilities of further drug optimisations. <br></p><p><br></p><p><br> </p><div> <div> <div> <p>Raw trajectory data analysed in this paper and movie examples are available at the zenodo repository.<br></p> </div> </div> </div> </div> </div> </div>

scholarly journals COVID19 Approved Drug Repurposing: Pocket Similarity Approach

2020 ◽  
Author(s):  
Mohamed Fadlalla
Keyword(s):  
Binding Site ◽  
Binding Sites ◽  
Drug Repurposing ◽  
Drug Binding ◽  
Drug Binding Site ◽  
Main Protease ◽  
Major Outbreak ◽  
Target Binding ◽  
Approved Drugs

<p>SARS CoV 2 has spread worldwide and caused a major outbreak of coronavirus disease 2019 (COVID-19). To date, no licensed drug or a vaccine is available against COVID19.</p><p>Starting from all of the resolved SARS CoV2 crystal structures, this study aims to find inhibitors for all of the SARS CoV2 proteins. To achieve this, I used PocketMatch to test the similarity of approved drugs binding sites against all of the binding sites found on SARS CoV 2 proteins. After that docking was used to confirm the results.</p><p>I found drugs that inhibit the main protease, Nsp12 and Nsp3. The discovered drugs are either in clinical trials (Sildenafil, Lopinavir, Ritonavir) or have in vitro antiviral activity (Nelfinavir, Indinavir, Amprenavir, depiqulinum , Gemcitabine, Raltitrexed, Aprepitant, montelukast, Ouabain, Raloxifene) whether against SARS CoV 2 or other viruses. In addition to this, further analysis of pockets revealed a steroidal pocket that might open the door to hypotheses on why the mortality of men is higher than women.</p><p>Many of the in silico repurposing studies test binding of the compound to the target using docking. The significance of this study adds to the similarity between the drug binding site and the target binding site. This takes into consideration the dynamic behaviour of the pocket after ligand binding.</p><div><br></div>

scholarly journals Drug Binding Dynamics of the Dimeric SARS-CoV-2 Main Protease, Determined by Molecular Dynamics Simulation

2020 ◽  
Author(s):  
Teruhisa S. KOMATSU ◽  
Noriaki Okimoto ◽  
Yohei M. KOYAMA ◽  
Yoshinori HIRANO ◽  
Gentaro MORIMOTO ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Active Site ◽  
Binding Sites ◽  
Complex Structure ◽  
Drug Binding ◽  
Dynamics Simulation ◽  
Trajectory Data ◽  
Main Protease ◽  
Dynamic Sampling

<div> <div> <div> <p>We performed molecular dynamics simulation of the dimeric SARS-CoV-2 (severe acute respiratory syndrome corona virus 2) main protease (Mpro) to examine the binding dynamics of small molecular ligands. Seven HIV inhibitors, darunavir, indinavir, lopinavir, nelfinavir, ritonavir, saquinavir, and tipranavir, were used as the potential lead drugs to investigate access to the drug binding sites in Mpro. The frequently accessed sites on Mpro were classified based on contacts between the ligands and the protein, and the differences in site distributions of the encounter complex were observed among the ligands. All seven ligands showed binding to the active site at least twice in 28 simulations of 200 ns each. We further investigated the variations in the complex structure of the active site with the ligands, using microsecond order simulations. Results revealed a wide variation in the shapes of the binding sites and binding poses of the ligands. Additionally, the C-terminal region of the other chain often interacted with the ligands and the active site. Collectively, these findings indicate the importance of dynamic sampling of protein- ligand complexes and suggest the possibilities of further drug optimisations. <br></p><p><br></p><p><br> </p><div> <div> <div> <p>Raw trajectory data analysed in this paper and movie examples are available at the zenodo repository.<br></p> </div> </div> </div> </div> </div> </div>

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