Profiling Molecular Simulations of SARS-CoV-2 Main Protease (Mpro) Binding to Repurposed Drugs Using Neural Network Force Fields

2020 ◽  
Author(s):  
Aayush Gupta
Keyword(s):  
Neural Network ◽  
Protein Interactions ◽  
Force Fields ◽  
Md Simulations ◽  
Interaction Sites ◽  
Main Protease ◽  
Rational Drug ◽  
Interaction Map ◽  
Novel Coronavirus ◽  
Approved Drugs

<div> <p> </p><div> <div> <div> <p> </p><div> <div> <div> <p> </p><div> <div> <div> <p>With the current pandemic situation caused by a novel coronavirus disease (COVID-19), there is an urgent call to develop a working therapeutic against it. Efficient computations aid to minimize the efforts by identifying a subset of drugs that can potentially bind to COVID-19 main protease or target protein (M<sup>PRO</sup>). The results of computations are always accompanied by their accuracy which depends on the details described by the model used. Machine learning models trained on millions of points and with unmatched accuracies are the best bet to employ in the process. In this work, I first identified and described the interaction sites of M<sup>PRO</sup> protein using a geometric deep learning model. Secondly, I conducted virtual screening (at one of the sites identified) on FDA approved drugs and picked 91 drugs having the highest binding affinity (below -8.0 kcal/mol). Then, I carried out 10 ns of molecular dynamics (MD) simulations using classical force fields and classified 37 drugs to be binding (includes drugs like Lopinavir, Saquinavir, Indinavir etc.) based on RMSD between MD-binding trajectories. To drastically improve the dynamics profile of selected 37 drugs, I brought in the highly accurate neural network force field (ANI) trained on coupled-cluster methods (CCSD(T)) data points and performed 1 ns of binding dynamics of each drug with protein. With the accurate approach, 19 drugs were qualified based on their RMSD cutoffs, and again with their free energy (ANI/MM/PBSA) computations another 7 drugs were rejected. The final selection of 12 drugs was validated based on MD trajectory clustering approach where 11 of 12 drugs (Targretin, Eltrombopag, Rifaximin, Deflazacort, Ergotamine, Doxazosin, Lastacaft, Rifampicin, Victrelis, Trajenta, Toposar, Indinavir) were confirmed to be binding. Further investigations were made to study their interactions with the protein and an accurate 2D- interaction map was generated. These findings and mapping of drug-protein interactions are highly accurate and could be potentially used to guide rational drug discovery against the COVID-19. </p> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>

scholarly journals Profiling Molecular Simulations of SARS-CoV-2 Main Protease (Mpro) Binding to Repurposed Drugs Using Neural Network Force Fields

2020 ◽  
Author(s):  
Aayush Gupta
Keyword(s):  
Neural Network ◽  
Protein Interactions ◽  
Force Fields ◽  
Md Simulations ◽  
Interaction Sites ◽  
Main Protease ◽  
Rational Drug ◽  
Interaction Map ◽  
Novel Coronavirus ◽  
Approved Drugs

<div> <p> </p><div> <div> <div> <p> </p><div> <div> <div> <p> </p><div> <div> <div> <p>With the current pandemic situation caused by a novel coronavirus disease (COVID-19), there is an urgent call to develop a working therapeutic against it. Efficient computations aid to minimize the efforts by identifying a subset of drugs that can potentially bind to COVID-19 main protease or target protein (M<sup>PRO</sup>). The results of computations are always accompanied by their accuracy which depends on the details described by the model used. Machine learning models trained on millions of points and with unmatched accuracies are the best bet to employ in the process. In this work, I first identified and described the interaction sites of M<sup>PRO</sup> protein using a geometric deep learning model. Secondly, I conducted virtual screening (at one of the sites identified) on FDA approved drugs and picked 91 drugs having the highest binding affinity (below -8.0 kcal/mol). Then, I carried out 10 ns of molecular dynamics (MD) simulations using classical force fields and classified 37 drugs to be binding (includes drugs like Lopinavir, Saquinavir, Indinavir etc.) based on RMSD between MD-binding trajectories. To drastically improve the dynamics profile of selected 37 drugs, I brought in the highly accurate neural network force field (ANI) trained on coupled-cluster methods (CCSD(T)/CBS) data points and performed 1 ns of binding dynamics of each drug with protein. With the accurate approach, 19 drugs were qualified based on their RMSD cutoffs, and again with their free energy (ANI/MM/PBSA) computations another 7 drugs were rejected. The final selection of 12 drugs was validated based on MD trajectory clustering approach where 11 of 12 drugs (Targretin, Eltrombopag, Rifaximin, Deflazacort, Ergotamine, Doxazosin, Lastacaft, Rifampicin, Victrelis, Trajenta, Toposar, and Indinavir) were confirmed to be binding. Further investigations were made to study their interactions with the protein and an accurate 2D- interaction map was generated. These findings and mapping of drug-protein interactions are highly accurate and could be potentially used to guide rational drug discovery against the COVID-19. </p> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>

scholarly journals Profiling Molecular Simulations of SARS-CoV-2 Main Protease (Mpro) Binding to Repurposed Drugs Using Neural Network Force Fields

2020 ◽  
Author(s):  
Aayush Gupta
Keyword(s):  
Neural Network ◽  
Protein Interactions ◽  
Force Fields ◽  
Network Models ◽  
Md Simulations ◽  
Cluster Method ◽  
Neural Network Models ◽  
Interaction Sites ◽  
Main Protease ◽  
Novel Coronavirus

<p> </p><div> <div> <div> <p> </p><div> <div> <div> <p> </p><div> <div> <div> <p>With the current pandemic situation caused by a novel coronavirus disease (COVID-19), there is an urgent call to develop a working therapeutic against it. Efficient computations can minimize the efforts by identifying a subset of drugs that can potentially bind to the COVID-19 main protease or target protein (M<sup>PRO</sup>). The results of computations are accompanied by an evaluation of their accuracy, which depends on the details described by the model used. Neural network models trained on millions of points and with unmatched accuracies are the best approach to employ in this process. In this work, I first identified and described the interaction sites of the M<sup>PRO</sup> protein using a geometric deep learning model. Second, I conducted virtual screening (at one of the sites identified) on FDA-approved drugs and selected 91 drugs with the highest binding affinities (below -8.0 kcal/mol). Then, I conducted 10 ns of molecular dynamics (MD) simulations using classical force fields and classified 37 drugs to be binding (including Lopinavir, Saquinavir, and Indinavir) based on the RMSD between MD-binding trajectories. To drastically improve the dynamics profile of the 37 selected drugs, I used the highly accurate neural network force field (ANI) method trained on coupled-cluster method (CCSD(T)/CBS) data points and performed 1 ns of binding dynamics for each drug with the protein. Using this approach, 19 drugs were qualified based on their RMSD cutoffs, and based on free energy (ANI/MM/PBSA) computations, 7 of the drugs were rejected. The final selection of 12 drugs was validated based on an MD trajectory clustering approach where 11 of the 12 drugs (Targretin, Eltrombopag, Rifaximin, Deflazacort, Ergotamine, Doxazosin, Lastacaft, Rifampicin, Victrelis, Trajenta, Toposar, and Indinavir) were confirmed to exhibit binding. Further investigations were performed to study their interactions with the protein and an accurate 2D-interaction map was generated. These findings and mappings of drug-protein interactions are highly accurate and may be potentially used to guide rational drug discovery against COVID-19.</p> </div> </div> </div> </div> </div> </div> </div> </div> </div>

scholarly journals Profiling Molecular Simulations of SARS-CoV-2 Main Protease (Mpro) Binding to Repurposed Drugs Using Neural Network Force Fields

2020 ◽  
Author(s):  
Aayush Gupta
Keyword(s):  
Neural Network ◽  
Protein Interactions ◽  
Force Fields ◽  
Network Models ◽  
Md Simulations ◽  
Cluster Method ◽  
Neural Network Models ◽  
Interaction Sites ◽  
Main Protease ◽  
Novel Coronavirus

<p> </p><div> <div> <div> <p> </p><div> <div> <div> <p> </p><div> <div> <div> <p>With the current pandemic situation caused by a novel coronavirus disease (COVID-19), there is an urgent call to develop a working therapeutic against it. Efficient computations can minimize the efforts by identifying a subset of drugs that can potentially bind to the COVID-19 main protease or target protein (M<sup>PRO</sup>). The results of computations are accompanied by an evaluation of their accuracy, which depends on the details described by the model used. Neural network models trained on millions of points and with unmatched accuracies are the best approach to employ in this process. In this work, I first identified and described the interaction sites of the M<sup>PRO</sup> protein using a geometric deep learning model. Second, I conducted virtual screening (at one of the sites identified) on FDA-approved drugs and selected 91 drugs with the highest binding affinities (below -8.0 kcal/mol). Then, I conducted 10 ns of molecular dynamics (MD) simulations using classical force fields and classified 37 drugs to be binding (including Lopinavir, Saquinavir, and Indinavir) based on the RMSD between MD-binding trajectories. To drastically improve the dynamics profile of the 37 selected drugs, I used the highly accurate neural network force field (ANI) method trained on coupled-cluster method (CCSD(T)/CBS) data points and performed 1 ns of binding dynamics for each drug with the protein. Using this approach, 19 drugs were qualified based on their RMSD cutoffs, and based on free energy (ANI/MM/PBSA) computations, 7 of the drugs were rejected. The final selection of 12 drugs was validated based on an MD trajectory clustering approach where 11 of the 12 drugs (Targretin, Eltrombopag, Rifaximin, Deflazacort, Ergotamine, Doxazosin, Lastacaft, Rifampicin, Victrelis, Trajenta, Toposar, and Indinavir) were confirmed to exhibit binding. Further investigations were performed to study their interactions with the protein and an accurate 2D-interaction map was generated. These findings and mappings of drug-protein interactions are highly accurate and may be potentially used to guide rational drug discovery against COVID-19.</p> </div> </div> </div> </div> </div> </div> </div> </div> </div>

scholarly journals A SARS-CoV-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing

2020 ◽  
Author(s):  
David E. Gordon ◽  
Gwendolyn M. Jang ◽  
Mehdi Bouhaddou ◽  
Jiewei Xu ◽  
Kirsten Obernier ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Drug Targets ◽  
Affinity Purification ◽  
Drug Repurposing ◽  
Human Protein ◽  
Protein Protein Interaction ◽  
Human Proteins ◽  
Interaction Map ◽  
Novel Coronavirus ◽  
Approved Drugs

ABSTRACTAn outbreak of the novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 290,000 people since the end of 2019, killed over 12,000, and caused worldwide social and economic disruption1,2. There are currently no antiviral drugs with proven efficacy nor are there vaccines for its prevention. Unfortunately, the scientific community has little knowledge of the molecular details of SARS-CoV-2 infection. To illuminate this, we cloned, tagged and expressed 26 of the 29 viral proteins in human cells and identified the human proteins physically associated with each using affinity-purification mass spectrometry (AP-MS), which identified 332 high confidence SARS-CoV-2-human protein-protein interactions (PPIs). Among these, we identify 66 druggable human proteins or host factors targeted by 69 existing FDA-approved drugs, drugs in clinical trials and/or preclinical compounds, that we are currently evaluating for efficacy in live SARS-CoV-2 infection assays. The identification of host dependency factors mediating virus infection may provide key insights into effective molecular targets for developing broadly acting antiviral therapeutics against SARS-CoV-2 and other deadly coronavirus strains.

scholarly journals High-resolution mining of the SARS-CoV-2 main protease conformational space: supercomputer-driven unsupervised adaptive sampling

2021 ◽  
Author(s):  
Théo Jaffrelot Inizan ◽  
Frédéric Célerse ◽  
Olivier Adjoua ◽  
Dina El Ahdab ◽  
Luc-Henri Jolly ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
High Resolution ◽  
Adaptive Sampling ◽  
Force Fields ◽  
Sampling Strategy ◽  
Md Simulations ◽  
Conformational Space ◽  
Many Body ◽  
Polarizable Force Fields ◽  
Main Protease

We provide an unsupervised adaptive sampling strategy capable of producing μs-timescale molecular dynamics (MD) simulations of large biosystems using many-body polarizable force fields (PFFs).

scholarly journals Virtual Screening of Potential Inhibitors for SARS-CoV-2 Main Protease

2020 ◽  
Author(s):  
Carlos Javier Alméciga-Díaz ◽  
Luisa N. Pimentel-Vera ◽  
Angela Caro ◽  
Angela Mosquera ◽  
Camilo Andrés Castellanos Moreno ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Clinical Information ◽  
Virus Genome ◽  
Pharmacological Agents ◽  
Main Protease ◽  
Novel Coronavirus ◽  
Approved Drugs ◽  
Potential Inhibitors

Coronavirus Disease 2019 (Covid-19) was first described in December 2019 in Wuhan, Hubei Province, China; and produced by a novel coronavirus designed as the acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Covid-19 has become a pandemic reaching over 1.3 million confirmed cases and 73,000 deaths. Several efforts have been done to identify pharmacological agents that can be used to treat patients and protect healthcare professionals. The sequencing of the virus genome not only has offered the possibility to develop a vaccine, but also to identified and characterize the virus proteins. Among these proteins, main protease (Mpro) has been identified as a potential therapeutic target, since it is essential for the processing other viral proteins. Crystal structures of SARS-CoV-2 Mpro and inhibitors has been described during the last months. To describe additional compounds that can inhibit SARS-CoV-2 Mpro, in this study we performed a molecular docking-based virtual screening against a library of experimental and approved drugs. Top 10 hits included Pictilisib, Nimorazole, Ergoloid mesylates, Lumacaftor, Cefuroxime, Cepharanhine, and Nilotinib. These compounds were predicted to have higher binding affinity for SARS-CoV-2 Mpro than previously reported inhibitors for this protein, suggesting a higher potential to inhibit virus replication. Since the identified drugs have both pre-clinical and clinical information, we consider that these results may contribute to the identification of treatment alternative for Covid-19. Nevertheless, in vitro and in vivo confirmation should be performed before these compounds could be translated to the clinic.

scholarly journals Repurposing of FDA Approved Drugs for the Identification of Potential Inhibitors of SARS-CoV-2 Main Protease

2020 ◽  
Author(s):  
Abhik Kumar Ray ◽  
Parth Sarthi Sen Gupta ◽  
Saroj Kumar Panda ◽  
Satyaranjan Biswal ◽  
Malay Kumar Rana
Keyword(s):  
Virtual Screening ◽  
Binding Energies ◽  
Great Promise ◽  
Main Protease ◽  
Promising Target ◽  
Inhibitory Potential ◽  
Novel Coronavirus ◽  
Approved Drugs ◽  
Potential Inhibitors ◽  
Fda Approved Drugs

<p>COVID-19, responsible for several deaths, demands a cumulative effort of scientists worldwide to curb the pandemic. The main protease, responsible for the cleavage of the polyprotein and formation of replication complex in virus, is considered as a promising target for the development of potential inhibitors to treat the novel coronavirus. The effectiveness of FDA approved drugs targeting the main protease in previous SARS-COV (s) reported earlier indicates the chances of success for the repurposing of FDA drugs against SARS-COV-2. Therefore, in this study, molecular docking and virtual screening of FDA approved drugs, primarily of three categories: antiviral, antimalarial, and peptide, are carried out to investigate their inhibitory potential against the main protease. Virtual screening has identified 53 FDA drugs on the basis of their binding energies (< -7.0 kcal/mol), out of which the top two drugs Velpatasvir (-9.1 kcal/mol) and Glecaprevir (-9.0 kcal/mol) seem to have great promise. These drugs have a stronger affinity to the SARS-CoV-2 main protease than the crystal bound inhibitor α-ketoamide 13B (-6.7 kcal/mol) or Indinavir (-7.5 kcal/mol) that has been proposed in a recent study as one of the best drugs for SARS-CoV-2. The <i>in-silico</i> efficacies of the screened drugs could be instructive for further biochemical and structural investigation for repurposing. The molecular dynamics studies on the shortlisted drugs are underway. </p>

scholarly journals Evaluation of Yashtimadhu (Glycyrrhiza glabra) active Phytochemicals Against Novel Coronavirus (SARS-CoV-2)

2020 ◽  
Author(s):  
Dharmendra Kumar Maurya
Keyword(s):  
Viral Entry ◽  
Virus Disease ◽  
Scientific Basis ◽  
Docking Studies ◽  
Angiotensin Converting Enzyme 2 ◽  
Main Protease ◽  
Docking Approach ◽  
Novel Coronavirus ◽  
Approved Drugs ◽  
Fda Approved Drugs

Abstract Corona Virus Disease 2019 (COVID-19) caused by a novel coronavirus emerged from Wuhan, China in December 2019. It has spread to more than 205 countries and become pandemic now. Currently, there are no FDA approved drugs or vaccines available and hence several studies are going on in search of suitable drug that can target viral proteins or host receptor for the prevention and management of COVID-19. The search for plant-based anti-viral agents against the SARS-CoV-2 is promising because several of plants have been shown to possess anti-viral activities against different viruses. Here, we used molecular docking approach to explore the use of Indian Ayurvedic herbs, Yashtimadhu in prevention and management of COVID-19. In the present study we have evaluated the effectiveness of phytochemicals found in Yashtimadhu against Main Protease (Mpro), Spike (S) protein and RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 as well as human angiotensin converting enzyme 2 (ACE2) receptor and furin protease. Apart from this, we have also performed in-silico drug-likeness and predicted pharmacokinetics of the selected phytochemicals found in the Yashtimadhu. Our study shows that several phytochemicals found in this plant have potential to bind with important proteins of SARS-CoV-2 which are essential for viral infection and replication. Overall our study provides scientific basis in terms of binding of active ingredients present in Yashtimadhu with SARS-CoV-2 target proteins. Our docking studies reveal that Yashtimadhu may inhibit the viral severity by interfering with viral entry as well as its multiplication in the infected persons. Thus Yashtimadhu may be helpful in the prevention and management of the COVID-19.

scholarly journals In Silico Identification and Docking-Based Drug Repurposing Against the Main Protease of SARS-CoV-2, Causative Agent of COVID-19

2020 ◽  
Author(s):  
Yogesh Kumar ◽  
Harvijay Singh
Keyword(s):  
Virtual Screening ◽  
Active Site ◽  
Viral Infections ◽  
Drug Repurposing ◽  
Docking Study ◽  
Docking Studies ◽  
Main Protease ◽  
Novel Coronavirus ◽  
Approved Drugs

<div>The rapidly enlarging COVID-19 pandemic caused by novel SARS-coronavirus 2 is a global</div><div>public health emergency of unprecedented level. Therefore the need of a drug or vaccine that</div><div>counter SARS-CoV-2 is an utmost requirement at this time. Upon infection the ssRNA genome</div><div>of SARS-CoV-2 is translated into large polyprotein which further processed into different</div><div>nonstructural proteins to form viral replication complex by virtue of virus specific proteases:</div><div>main protease (3-CL protease) and papain protease. This indispensable function of main protease</div><div>in virus replication makes this enzyme a promising target for the development of inhibitors and</div><div>potential treatment therapy for novel coronavirus infection. The recently concluded α-ketoamide</div><div>ligand bound X-ray crystal structure of SARS-CoV-2 Mpro (PDB ID: 6Y2F) from Zhang et al.</div><div>has revealed the potential inhibitor binding mechanism and the determinants responsible for</div><div>involved molecular interactions. Here, we have carried out a virtual screening and molecular</div><div>docking study of FDA approved drugs primarily targeted for other viral infections, to investigate</div><div>their binding affinity in Mpro active site. Virtual screening has identified a number of antiviral</div><div>drugs, top ten of which on the basis of their bending energy score are further examined through </div><div>molecular docking with Mpro. Docking studies revealed that drug Lopinavir-Ritonavir, Tipranavir</div><div>and Raltegravir among others binds in the active site of the protease with similar or higher</div><div>affinity than the crystal bound inhibitor α-ketoamide. However, the in-vitro efficacies of the drug</div><div>molecules tested in this study, further needs to be corroborated by carrying out biochemical and</div><div>structural investigation. Moreover, this study advances the potential use of existing drugs to be</div><div>investigated and used to contain the rapidly expanding SARS-CoV-2 infection.</div>

Sign in / Sign up

Export Citation Format

Share Document