Docking, Binding Free Energy Estimation, and MD Simulation of Newly Designed CQ and HCQ Analogues Against the Spike-ACE2 Complex of SARS-CoV-2

2021 ◽  
Vol 6 (4) ◽  
pp. 77-89
Author(s):  
Anjoomaara H. Patel ◽  
Riya B. Patel ◽  
MahammadHussain J. Memon ◽  
Samiya S. Patel ◽  
Sharav A. Desai ◽  
...  
Keyword(s):  
Free Energy ◽  
Md Simulation ◽  
Binding Free Energy ◽  
Docking Studies ◽  
Radius Of Gyration ◽  
Ligand Interaction ◽  
Energy Estimation ◽  
Efficacious Treatment ◽  
Protein Ligand Interaction ◽  
Derivatives Of

The coronavirus disease 2019 (COVID-19) virus has been spreading rapidly, and scientists are endeavouring to discover drugs for its efficacious treatment. Chloroquine phosphate, an old drug for treatment of malaria, has shown to have apparent efficacy and acceptable safety against COVID-19. As a part of Drug Discovery Hackathon-2020, in this study, the authors have tried making the derivatives of CQ and HCQ using MarvinSketch by ChemAxon. Molecular docking studies of these ligands were performed using Glide by Schrodinger, and ADME profiles were obtained by using QikProp. The obtained results after data analysis demonstrated that ligands HCQ_imidazoll, choloroquine_3c, HCQ_pyrrolC had good binding affinity and complied with all the ADME parameters. The molecular dynamic simulation of these ligands in complex with the 2019-nCoV RBD/ACE-2-B0AT1 complex PDB ID: 6M17 were carried out, and the parameters like RMSD, RMSF, and radius of gyration were observed to understand the fluctuations and protein-ligand interaction.

Spiro-acridine inhibiting tyrosinase enzyme: Kinetic, protein-ligand interaction and molecular docking studies

2019 ◽  
Vol 122 ◽  
pp. 289-297 ◽  
Author(s):  
Thaís Meira Menezes ◽  
Sinara Mônica Vitalino de Almeida ◽  
Ricardo Olímpio de Moura ◽  
Gustavo Seabra ◽  
Maria do Carmo Alves de Lima ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Docking Studies ◽  
Enzyme Kinetic ◽  
Ligand Interaction ◽  
Molecular Docking Studies ◽  
Protein Ligand Interaction ◽  
Tyrosinase Enzyme

scholarly journals SuCOS is Better than RMSD for Evaluating Fragment Elaboration and Docking Poses

2019 ◽  
Author(s):  
Susan Leung ◽  
Michael Bodkin ◽  
Frank von Delft ◽  
Paul Brennan ◽  
Garrett Morris
Keyword(s):  
Binding Mode ◽  
Docking Studies ◽  
Binding Modes ◽  
False Negatives ◽  
Ligand Interaction ◽  
Cross Docking ◽  
Protein Ligand Interaction ◽  
Feature Overlap ◽  
Better Than ◽  
Fragment Based Drug Discovery

One of the fundamental assumptions of fragment-based drug discovery is that the fragment’s binding mode will be conserved upon elaboration into larger compounds. The most common way of quantifying binding mode similarity is Root Mean Square Deviation (RMSD), but Protein Ligand Interaction Fingerprint (PLIF) similarity and shape-based metrics are sometimes used. We introduce SuCOS, an open-source shape and chemical feature overlap metric. We explore the strengths and weaknesses of RMSD, PLIF similarity, and SuCOS on a dataset of X-ray crystal structures of paired elaborated larger and smaller molecules bound to the same protein. Our redocking and cross-docking studies show that SuCOS is superior to RMSD and PLIF similarity. When redocking, SuCOS produces fewer false positives and false negatives than RMSD and PLIF similarity; and in cross-docking, SuCOS is better at differentiating experimentally-observed binding modes of an elaborated molecule given the pose of its non-elaborated counterpart. Finally we show that SuCOS performs better than AutoDock Vina at differentiating actives from decoy ligands using the DUD-E dataset. SuCOS is available at https://github.com/susanhleung/SuCOS . <br>

scholarly journals SuCOS is Better than RMSD for Evaluating Fragment Elaboration and Docking Poses

2019 ◽  
Author(s):  
Susan Leung ◽  
Michael Bodkin ◽  
Frank von Delft ◽  
Paul Brennan ◽  
Garrett Morris
Keyword(s):  
Binding Mode ◽  
Docking Studies ◽  
Binding Modes ◽  
False Negatives ◽  
Ligand Interaction ◽  
Cross Docking ◽  
Protein Ligand Interaction ◽  
Feature Overlap ◽  
Better Than ◽  
Fragment Based Drug Discovery

One of the fundamental assumptions of fragment-based drug discovery is that the fragment’s binding mode will be conserved upon elaboration into larger compounds. The most common way of quantifying binding mode similarity is Root Mean Square Deviation (RMSD), but Protein Ligand Interaction Fingerprint (PLIF) similarity and shape-based metrics are sometimes used. We introduce SuCOS, an open-source shape and chemical feature overlap metric. We explore the strengths and weaknesses of RMSD, PLIF similarity, and SuCOS on a dataset of X-ray crystal structures of paired elaborated larger and smaller molecules bound to the same protein. Our redocking and cross-docking studies show that SuCOS is superior to RMSD and PLIF similarity. When redocking, SuCOS produces fewer false positives and false negatives than RMSD and PLIF similarity; and in cross-docking, SuCOS is better at differentiating experimentally-observed binding modes of an elaborated molecule given the pose of its non-elaborated counterpart. Finally we show that SuCOS performs better than AutoDock Vina at differentiating actives from decoy ligands using the DUD-E dataset. SuCOS is available at https://github.com/susanhleung/SuCOS . <br>

scholarly journals Computational design of novel cyclic urea as HIV-1 protease inhibitor

2007 ◽  
Vol 5 (4) ◽  
pp. 1064-1072 ◽  
Author(s):  
Manga Vijjulatha ◽  
S. Kanth
Keyword(s):  
Biological Activity ◽  
Interaction Energy ◽  
Model Equation ◽  
Computational Design ◽  
Docking Studies ◽  
Computational Techniques ◽  
Ligand Interaction ◽  
Protein Ligand Interaction ◽  
Adme Properties ◽  
Hiv 1

AbstractA series of novel cyclic urea molecules 5,6-dihydroxy-1,3-diazepane-2,4,7-trione as HIV-1 protease inhibitors were designed using computational techniques. The designed molecules were compared with the known cyclic urea molecules by performing docking studies, calculating their ADME (Absorption, Distribution, Metabolism, and Excretion) properties and protein ligand interaction energy. These novel molecules were designed by substituting the P 1/P′ 1 positions (4th and 7th position of 1, 3-diazepan-2-one) with double bonded oxygens. This reduces the molecular weight and increases the bioavailability, indicating better ADME properties. The docking studies showed good binding affinity towards HIV-1 protease. The biological activity of these inhibitors were predicted by a model equation generated by the regression analysis between biological activity (log 1/K i ) of known inhibitors and their protein ligand interaction energy. The synthetic studies are in progress.

scholarly journals Protein Ligand Interaction Fingerprints

2017 ◽  
pp. 1072-1091
Author(s):  
Ali HajiEbrahimi ◽  
Hamidreza Ghafouri ◽  
Mohsen Ranjbar ◽  
Amirhossein Sakhteman
Keyword(s):  
Virtual Screening ◽  
Docking Studies ◽  
Scoring Functions ◽  
Ligand Interaction ◽  
Advantages And Disadvantages ◽  
Protein Affinity ◽  
Binding Cavity ◽  
Protein Ligand Interaction ◽  
Interaction Fingerprints ◽  
Activity Information

A most challenging part in docking-based virtual screening is the scoring functions implemented in various docking programs in order to evaluate different poses of the ligands inside the binding cavity of the receptor. Precise and trustable measurement of ligand-protein affinity for Structure-Based Virtual Screening (SB-VS) is therefore, an outstanding problem in docking studies. Empirical post-docking filters can be helpful as a way to provide various types of structure-activity information. Different types of interaction have been presented between the ligands and the receptor so far. Based on the diversity and importance of PLIF methods, this chapter will focus on the comparison of different protocols. The advantages and disadvantages of all methods will be discussed explicitly in this chapter as well as future sights for further progress in this field. Different classifications approaches for the protein-ligand interaction fingerprints were also discussed in this chapter.

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.

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