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Virtual screening of plant-derived compounds against SARS-CoV-2 viral proteins using computational tools.

ABSTRACT: The new SARS-CoV-2, responsible for the COVID-19 pandemic, has been threatening public health worldwide for more than a year. The aim of this work was to evaluate compounds of natural origin, mainly from medicinal plants, as potential SARS-CoV-2 inhibitors through docking studies. The viral spike (S) glycoprotein and the main protease M^pro, involved in the recognition of virus by host cells and in viral replication, respectively, were the main molecular targets in this study. Molecular docking was performed using AutoDock, which allowed us to select the plant actives with the highest affinity towards the viral targets and to identify the interaction molecular sites with the SARS-CoV2 proteins. The best energy binding values for S protein were, in kcal/mol: -19.22 for glycyrrhizin, -17.84 for gitoxin, -12.05 for dicumarol, -10.75 for diosgenin, and -8.12 for delphinidin. For M^pro were, in kcal/mol: -9.36 for spirostan, -8.75 for N-(3-acetylglycyrrhetinoyl)-2-amino-propanol, -8.41 for α-amyrin, -8.35 for oleanane, -8.11 for taraxasterol, and -8.03 for glycyrrhetinic acid. In addition, the synthetic drugs umifenovir, chloroquine, and hydroxychloroquine were used as controls for S protein, while atazanavir and nelfinavir were used for M^pro. Key hydrogen bonds and hydrophobic interactions between natural compounds and the respective viral proteins were identified, allowing us to explain the great affinity obtained in those compounds with the lowest binding energies. These results suggest that these natural compounds could potentially be useful as drugs to be experimentally evaluated against COVID-19.

SUBMITTER: Zigolo MA

PROVIDER: S-EPMC7967396 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), previously known as 2019 novel coronavirus (2019-nCoV), has spread rapidly across the globe, creating an unparalleled global health burden and spurring a deepening economic crisis. As of July 7th, 2020, almost seven months into the outbreak, there are no approved vaccines and few treatments available. Developing drugs that target multiple points in the viral life cycle could serve as a strategy to tackle the current as well as future coronavirus pandemics. Here we leverage the power of our recently developed in silico screening platform, VirtualFlow, to identify inhibitors that target SARS-CoV-2. VirtualFlow is able to efficiently harness the power of computing clusters and cloud-based computing platforms to carry out ultra-large scale virtual screens. In this unprecedented structure-based multi-target virtual screening campaign, we have used VirtualFlow to screen an average of approximately 1 billion molecules against each of 40 different target sites on 17 different potential viral and host targets in the cloud. In addition to targeting the active sites of viral enzymes, we also target critical auxiliary sites such as functionally important protein-protein interaction interfaces. This multi-target approach not only increases the likelihood of finding a potent inhibitor, but could also help identify a collection of anti-coronavirus drugs that would retain efficacy in the face of viral mutation. Drugs belonging to different regimen classes could be combined to develop possible combination therapies, and top hits that bind at highly conserved sites would be potential candidates for further development as coronavirus drugs. Here, we present the top 200 in silico hits for each target site. While in-house experimental validation of some of these compounds is currently underway, we want to make this array of potential inhibitor candidates available to researchers worldwide in consideration of the pressing need for fast-tracked drug development.

Project description:The sudden outbreak of novel coronavirus has caused a global concern due to its infection rate and mortality. Despite extensive research, there are still no specific drugs or vaccines to combat SARS-CoV-2 infection. Hence, this study was designed to evaluate some plant-based active compounds for drug candidacy against SARS-CoV-2 by using virtual screening methods and various computational analyses. A total of 27 plant metabolites were screened against SARS-CoV-2 main protease proteins (MPP), Nsp9 RNA binding protein, spike receptor binding domain, spike ecto-domain and HR2 domain using a molecular docking approach. Four metabolites, i.e., asiatic acid, avicularin, guajaverin, and withaferin showed maximum binding affinity with all key proteins in terms of lowest global binding energy. The crucial binding sites and drug surface hotspots were unravelled for each viral protein. The top candidates were further employed for ADME (absorption, distribution, metabolism, and excretion) analysis to investigate their drug profiles. Results suggest that none of the compounds render any undesirable consequences that could reduce their drug likeness properties. The analysis of toxicity pattern revealed no significant tumorigenic, mutagenic, irritating, or reproductive effects by the compounds. However, withaferin was comparatively toxic among the top four candidates with considerable cytotoxicity and immunotoxicity. Most of the target class by top drug candidates belonged to enzyme groups (e.g. oxidoreductases hydrolases, phosphatases). Moreover, results of drug similarity prediction revealed two approved structural analogs of Asiatic acid i.e. Hydrocortisone (DB00741) (previously used for SARS-CoV-1 and MERS) and Dinoprost-tromethamine (DB01160) from DrugBank. In addition, two other biologically active compounds, Mupirocin (DB00410) and Simvastatin (DB00641) could be an option for the treatment of viral infections. The study may pave the way to develop effective medications and preventive measure against SARS-CoV-2. Due to the encouraging results, we highly recommend further in vivo trials for the experimental validation of our findings.

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Virtual screening of plant-derived compounds against SARS-CoV-2 viral proteins using computational tools.

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