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Computational Investigation of the Interaction of Anti-Influenza Drugs with CoVID-19 Protein

Computational Molecular Bioscience 2020年第2期10卷 45-60页

作者： kassim F. adebambo Department of Clinical and Pharmaceutical sciences University of Hertfordshire Hertfordshire UK

Coronavirus (CoVID-19) is a new outbreak of coronavirus disease which started in the Wuhan, China, the spread of this virus has now reached a global stage, urgent need is therefore needed to find new drug molecules which can either be used as a first aid intervention or slow down the multiplication rate of the virus within the system. In order to address this, this research looked into the existing antiviral drugs and screened them for their inhibitory properties towards the CoVID-19 protein. Recently, the crystal structure of the CoVID-19 (6LU7) protein has been established, this gives us the possible drug target site in CoVID-19. The binding affinity of the six compounds was screened using MOE (Molecular Operating Environment) software, four compounds (Zanamivir, Peramivir, Rimantidine, and Oseltamivir) out these six compounds have been approved by the Food Drug and Administration (FDA). The molecular docking calculation, Higher Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO) calculation were used to hypothesise the bioactivity of the FDA approved drug against the CoVID-19 protein. The calculation showed that Pimodivir tops the list of the anti influenza drug which can be used as first aid treatment for patient. Apart from Pimodivir, Laninamivir Octanoate is also a very good drug which might be used to inhibit CoVID-19 protein. It was also discovered that based on binding property of Rimantadine, it might be suitable for Fragment Based Drug Design (FBDD) approach which might lead to the discovery of completely new drug entity. Stability of the new protein structure was studied using GROMACS molecular dynamic simulation software. The results showed that the stability of the protein structure was achieved over a range of time, this confirmed that 6LU7 crystal structure might be a suitable protein crystal structure suitable for the development of new drug towards the treatment of CoVID-19. Finally, based on the molecular docking resul

关键词： CoVID-19 Molecular Docking 6LU7 Protein Simulations FDA Approved Drugs MOE Software

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Molecular Docking Investigation of New Inhibitors of <i>Falciparum vivax</i>

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Computational Molecular Bioscience 2018年第2期8卷 43-67页

作者： kassim adebambo Sinthusan Gunaratnam Pharmaceutical Chemistry Unit Department of Pharmacy Pharmacology and Postgraduate Medicine University of Hertfordshire Hatfield UK

Despite the vigorous research and development, as of 2017, there is currently no widely available antimalarial vaccine. An effective, commercially available vaccine would be a huge game changer;however, it seems like there is still a long way to go until that target is reached. Therefore, the purpose of this study was to use molecular docking technique to identify new inhibitors for a novel antimalarial target with the overall aim of finding hit compounds which could be further optimized to become potential drug candidates. The docking protocol AutoDockVina was used alongside the molecular visualisation software UCSF Chimera to dock 100 naphthoquinones (labelled TM1-100) and 66 aryl diketones (labelled TM101-166) with the chosen target, Plasmodium vivax N-myristoyltransferase (PvNMT). Each docking session yielded the best 9 binding modes between the ligand and target. The hydrogen bond interactions of all binding modes were analysed, and the top six target molecules (TM) were short listed as the possible hit compounds (TM40, TM65, TM66, TM81, TM94 and TM165). These compounds displayed more than six hydrogen bonds under 3 angstroms over the 9 binding modes. Using Lipinski’s rule of 5, the potential hit compounds were further analysed to determine the drug-likeness and all were found to obey the parameters. Following the same method used to dock the ligands, twelve FDA approved antimalarial drugs were also docked with PvNMT for comparison purposes. Apart from proguanil, the other eleven antimalarial drugs displayed fewer hydrogen bonds under 3 angstroms over the 9 binding modes compared to all six of the potential hit compounds. This study discovered six compounds which displayed stronger interactions with the target protein compared to majority of the FDA approved drugs. The results of this investigation gave us new molecules that could be further investigated for the designing of novel drug-like compounds for the treatment of Malaria.

关键词： Falciparum vivax Hydrogen Bond Interaction Molecular Docking Lipinski’s Rule N-Myristoyltransferase (PvNMT)

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Molecular Docking Study of the Binding Interaction of Hydroxychloroquine, Dexamethasone and Other Anti-Inflammatory Drugs with SARS-CoV-2 Protease and SARS-CoV-2 Spikes Glycoprotein

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Computational Molecular Bioscience 2021年第2期11卷 19-49页

作者： kassim F. adebambo Nadia Haji Department of Clinical Pharmaceutical and Biological Science University of Hertfordshire Hatfield England

Aims: The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is still accountable for millions of deaths worldwide and declared as a global pandemic by the World Health Organisation. Despite efforts, there is still limited evidence available on a successful potent inhibitor with a low toxicity profile that can aid in the prevention and/or treatment of COVID-19. This study will focus on four main aspects: 1) screening 19 Food Drug and Administration (FDA) approved drugs using computational molecular docking;2) assessing drug toxicity profiles using biological data;3) recommending potential therapies against COVID-19 and 4) supplementing currently used therapies. Methods: 19 FDA approved drugs were investigated against the crystal structure of SARS-CoV-2 protease (6LU7) and SARS-CoV-2 glycoprotein (6VXX) using a computational molecular docking software, Molecular Operating Environment (MOE). Separately, on MOE, 6LU7 and 6VXX were loaded, prepared, and the binding pockets located. The drug’s canonical SMILES were imported, minimised, and docked on the prepared proteins using a search algorithm to establish the highest stability conformation. Drugs were ranked depending on binding properties and biological data to assess safety;steric clashes and voids in the binding site were also analysed. Results and discussion: Out of the nineteen (19) FDA approved drugs, 18 inhibited 6LU7 and 13 inhibited 6VXX. High-ranked drugs based on binding properties for 6LU7 were hydroxychloroquine, dexamethasone, naproxen, etoricoxib, and ibuprofen. For 6VXX were hydroxychloroquine, celecoxib, etoricoxib, meloxicam, and parecoxib. Considering safety profile, the top 3 drugs in descending order for 6LU7 were etoricoxib, naproxen and dexamethasone and for 6VXX were etoricoxib, meloxicam, and parecoxib. Compared to the literature, the results were consistent for de

关键词： Anti-Inflammatory Drugs COVID-19 FDA Approved Drugs MOE Software SARS-CoV-2 Spike Glycoprotein

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Molecular Docking Study of the Binding Interaction of Hydroxychloroquine, Dexamethasone and Other Anti-Inflammatory Drugs with SARS-CoV-2 Protease and SARS-CoV-2 Spikes Glycoprotein

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Computational Molecular Bioscience（计算分子生物学） 2021年第2期

作者： kassim F. adebambo Nadia Haji

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