scholarly journals Nelfinavir was predicted to be a potential inhibitor of 2019-nCov main protease by an integrative approach combining homology modelling, molecular docking and binding free energy calculation

2020 ◽  
Author(s):  
Zhijian Xu ◽  
Cheng Peng ◽  
Yulong Shi ◽  
Zhengdan Zhu ◽  
Kaijie Mu ◽  
...  
Keyword(s):  
Free Energy ◽  
Binding Free Energy ◽  
Homology Modelling ◽  
Binding Mode ◽  
Free Energy Calculations ◽  
Integrative Approach ◽  
Energy Calculation ◽  
Potential Inhibitor ◽  
Small Molecule Drugs ◽  
Main Protease

Abstract2019-nCov has caused more than 80 deaths as of 27 January 2020 in China, and infection cases have been reported in more than 10 countries. However, there is no approved drug to treat the disease. 2019-nCov Mpro is a potential drug target to combat the virus. We built homology models based on SARS Mpro structures, and docked 1903 small molecule drugs to the models. Based on the docking score and the 3D similarity of the binding mode to the known Mpro ligands, 4 drugs were selected for binding free energy calculations. Both MM/GBSA and SIE methods voted for nelfinavir, with the binding free energy of −24.69±0.52 kcal/mol and −9.42±0.04 kcal/mol, respectively. Therefore, we suggested that nelfinavir might be a potential inhibitor against 2019-nCov Mpro.

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects

2020 ◽  
Author(s):  
Christina Schindler ◽  
Hannah Baumann ◽  
Andreas Blum ◽  
Dietrich Böse ◽  
Hans-Peter Buchstaller ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Large Scale ◽  
Active Drug ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Energy Calculation ◽  
Large Scale Assessment ◽  
New Public ◽  
Binding Free Energy Calculations

Here we present an evaluation of the binding affinity prediction accuracy of the free energy calculation method FEP+ on internal active drug discovery projects and on a large new public benchmark set.<br>

Application of the ESMACS Binding Free Energy Protocol to a Highly Varied Ligand Dataset: Lactate Dehydogenase A

2019 ◽  
Author(s):  
David Wright ◽  
Fouad Husseini ◽  
Shunzhou Wan ◽  
Christophe Meyer ◽  
Herman Van Vlijmen ◽  
...  
Keyword(s):  
Free Energy ◽  
Surface Area ◽  
Binding Free Energy ◽  
Normal Mode Analysis ◽  
Binding Mode ◽  
Accessible Surface Area ◽  
Solvent Accessible Surface Area ◽  
Mode Analysis ◽  
Energy Calculation ◽  
Accessible Surface

<div>Here, we evaluate the performance of our range of ensemble simulation based binding free energy calculation protocols, called ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) for use in fragment based drug design scenarios. ESMACS is designed to generate reproducible binding affinity predictions from the widely used molecular mechanics Poisson-Boltzmann surface area (MMPBSA) approach. We study ligands designed to target two binding pockets in the lactate dehydogenase A target protein, which vary in size, charge and binding mode. When comparing to experimental results, we obtain excellent statistical rankings across this highly diverse set of ligands. In addition, we investigate three approaches to account for entropic contributions not captured by standard MMPBSA calculations: (1) normal mode analysis, (2) weighted solvent accessible surface area (WSAS) and (3) variational entropy. </div>

Computational Simulations on the Binding and Reactivity of a Nitrile Inhibitor of SARS-CoV-2 Main Protease

2021 ◽  
Author(s):  
Carlos A. Ramos-Guzmán ◽  
José Javier Ruiz-Pernía ◽  
Iñaki Tuñón
Keyword(s):  
Free Energy ◽  
Computational Analysis ◽  
Binding Mode ◽  
Free Energy Calculations ◽  
The Novel ◽  
Computational Simulations ◽  
Main Protease ◽  
3Cl Protease ◽  
Alchemical Free Energy ◽  
Alchemical Free Energy Calculations

We present a detailed computational analysis of the binding mode and reactivity of the novel oral inhibitor PF-07321332 developed against SARS-CoV-2 3CL protease. Alchemical free energy calculations suggest that positions...

scholarly journals Drug Repurposing Against SARS-CoV-2 Using E-Pharmacophore Based Virtual Screening and Molecular Docking with Main Protease as the Target

2020 ◽  
Author(s):  
arun kumar ◽  
Sharanya C.S ◽  
Abhithaj J ◽  
Dileep Francis ◽  
Sadasivan C
Keyword(s):  
Free Energy ◽  
Molecular Docking ◽  
Drug Target ◽  
Binding Free Energy ◽  
Drug Repurposing ◽  
Energy Calculation ◽  
Viral Protease ◽  
Energy Estimation ◽  
Main Protease ◽  
Potential Inhibitors

Since its first report in December 2019 from China the COVID-19 pandemic caused by the beta-coronavirus SARS-CoV-2 has spread at an alarming pace infecting about 26 lakh, and claiming the lives of more than 1.8 lakh individuals across the globe. Although social quarantine measures have succeeded in containing the spread of the virus to some extent, the lack of a clinically approved vaccine or drug remains the biggest bottleneck in combating the pandemic. Drug repurposing can expedite the process of drug development by identifying known drugs which are effective against SARS-CoV-2. The SARS-CoV-2 main protease is a promising drug target due to its indispensable role in viral multiplication inside the host. In the present study an E-pharmacophore hypothesis was generated using the crystal structure of the viral protease in complex with an imidazole carbaximide inhibitor as the drug target. Drugs available in the superDRUG2 database were used to identify candidate drugs for repurposing. The hits were further screened using a structure based approach involving molecular docking at different precisions. The most promising drugs were subjected to binding free energy estimation using MM-GBSA. Among the 4600 drugs screened 17 drugs were identified as candidate inhibitors of the viral protease based on the glide scores obtained from molecular docking. Binding free energy calculation showed that six drugs viz, Binifibrate, Macimorelin acetate, Bamifylline, Rilmazafon, Afatinib and Ezetimibe can act as potential inhibitors of the viral protease.

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects

2020 ◽  
Author(s):  
Christina Schindler ◽  
Hannah Baumann ◽  
Andreas Blum ◽  
Dietrich Böse ◽  
Hans-Peter Buchstaller ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Large Scale ◽  
Active Drug ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Energy Calculation ◽  
Large Scale Assessment ◽  
New Public ◽  
Binding Free Energy Calculations

Here we present an evaluation of the binding affinity prediction accuracy of the free energy calculation method FEP+ on internal active drug discovery projects and on a large new public benchmark set.<br>

Application of the ESMACS Binding Free Energy Protocol to a Highly Varied Ligand Dataset: Lactate Dehydogenase A

2019 ◽  
Author(s):  
David Wright ◽  
Fouad Husseini ◽  
Shunzhou Wan ◽  
Christophe Meyer ◽  
Herman Van Vlijmen ◽  
...  
Keyword(s):  
Free Energy ◽  
Surface Area ◽  
Binding Free Energy ◽  
Normal Mode Analysis ◽  
Binding Mode ◽  
Accessible Surface Area ◽  
Solvent Accessible Surface Area ◽  
Mode Analysis ◽  
Energy Calculation ◽  
Accessible Surface

<div>Here, we evaluate the performance of our range of ensemble simulation based binding free energy calculation protocols, called ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) for use in fragment based drug design scenarios. ESMACS is designed to generate reproducible binding affinity predictions from the widely used molecular mechanics Poisson-Boltzmann surface area (MMPBSA) approach. We study ligands designed to target two binding pockets in the lactate dehydogenase A target protein, which vary in size, charge and binding mode. When comparing to experimental results, we obtain excellent statistical rankings across this highly diverse set of ligands. In addition, we investigate three approaches to account for entropic contributions not captured by standard MMPBSA calculations: (1) normal mode analysis, (2) weighted solvent accessible surface area (WSAS) and (3) variational entropy. </div>

scholarly journals An Effective MM/GBSA Protocol for Absolute Binding Free Energy Calculations: A Case Study on SARS-CoV-2 Spike Protein and the Human ACE2 Receptor

Molecules ◽  
2021 ◽  
Vol 26 (8) ◽  
pp. 2383
Author(s):  
Negin Forouzesh ◽  
Nikita Mishra
Keyword(s):  
Free Energy ◽  
Binding Free Energy ◽  
Normal Mode Analysis ◽  
Free Energy Calculations ◽  
Spike Protein ◽  
Upper And Lower Bounds ◽  
Mode Analysis ◽  
Implicit Solvent ◽  
Energy Calculation ◽  
Generalized Born

The binding free energy calculation of protein–ligand complexes is necessary for research into virus–host interactions and the relevant applications in drug discovery. However, many current computational methods of such calculations are either inefficient or inaccurate in practice. Utilizing implicit solvent models in the molecular mechanics generalized Born surface area (MM/GBSA) framework allows for efficient calculations without significant loss of accuracy. Here, GBNSR6, a new flavor of the generalized Born model, is employed in the MM/GBSA framework for measuring the binding affinity between SARS-CoV-2 spike protein and the human ACE2 receptor. A computational protocol is developed based on the widely studied Ras–Raf complex, which has similar binding free energy to SARS-CoV-2/ACE2. Two options for representing the dielectric boundary of the complexes are evaluated: one based on the standard Bondi radii and the other based on a newly developed set of atomic radii (OPT1), optimized specifically for protein–ligand binding. Predictions based on the two radii sets provide upper and lower bounds on the experimental references: −14.7(ΔGbindBondi)<−10.6(ΔGbindExp.)<−4.1(ΔGbindOPT1) kcal/mol. The consensus estimates of the two bounds show quantitative agreement with the experiment values. This work also presents a novel truncation method and computational strategies for efficient entropy calculations with normal mode analysis. Interestingly, it is observed that a significant decrease in the number of snapshots does not affect the accuracy of entropy calculation, while it does lower computation time appreciably. The proposed MM/GBSA protocol can be used to study the binding mechanism of new variants of SARS-CoV-2, as well as other relevant structures.

scholarly journals Identification of the Potential Hits for Hindering Interaction of SARS-CoV-2 Main Protease (M pro ) from the Pool of Antiviral Phytochemicals utilizing Molecular Docking and Molecular Dynamics (MD) Simulations

2021 ◽  
Author(s):  
Chirag N. Patel ◽  
Dharmesh G. Jaiswal ◽  
Siddhi P. Jani ◽  
Naman Mangukia ◽  
Robin M. Parmar ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Molecular Docking ◽  
Virus Assembly ◽  
Binding Free Energy ◽  
Natural Compounds ◽  
Free Energy Calculations ◽  
Host Protein ◽  
Dynamic Simulations ◽  
Main Protease

Abstract The novel SARS-CoV-2 is an etiological factor that triggers Coronavirus disease in 2019 (COVID-19) and tends to be an imminent occurrence of a pandemic. Out of all recognized solved complexes linked to SARS-CoV, Main protease (Mpro) is considered a desirable antiviral phytochemical that play a crucial role in virus assembly and possibly non-interactive capacity to adhere to any viral host protein. In this research, SARS-CoV-2 MPro was chosen as a focus for the detection of possible inhibitors using a variety of different analytical methods such as molecular docking, ADMET analysis, dynamic simulations and binding free energy measurements. Virtual screening of known natural compounds recognized Withanoside V, Withanoside VI, Racemoside B, Racemoside A and Shatavarin IX as future inhibitors of SARS-CoV-2 MPro with stronger energy binding. Also, simulations of molecular dynamics for a 100 ns time scale showed that much of the main SARS-CoV-2 MPro interactions had been maintained in the simulation routes. Binding free energy calculations using the MM/PBSA method ranked the top five possible natural compounds that can act as effective SARS-CoV-2 MPro inhibitors.

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