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Metabologenomics approach to the discovery of novel compounds from Streptomyces sp. GMR22 as anti-SARS-CoV-2 drugs.

ABSTRACT: COVID-19 is spreading rapidly yet there is no clinically proven drug available now. Soil-derived Streptomyces sp. GMR22 has a large genome size (11.4 Mbp) and a huge BGCs (Biosynthetic Gene Clusters) encoding secondary metabolites. This bacterium is a potential source for producing a wide variety of compounds which are able to block SARS-CoV-2, the causative agent of COVID-19. This study aimed to predict the secondary metabolites of Streptomyces sp. GMR22 and to evaluate the ability as SARS-CoV-2 inhibitor. The AntiSMASH 5.0 was used for genome mining analysis and targeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) was used for metabolite analysis. In silico molecular docking was performed on important target proteins of SARS-CoV-2 i.e., spike protein (PDB ID: 6LXT), Receptor Binding Domain (RBD)-ACE2 (Angiotensin-Converting Enzyme 2) (PDB ID: 6VW1), 3CLpro (3-chymotrypsin-like protease) (PDB ID: 6M2N), and RdRp (RNA-dependent RNA polymerase) (PDB ID: 6M71). Two compounds from GMR22 extract, echoside A and echoside B were confirmed by targeted LC-HRMS and potential as SARS-CoV-2 inhibitor. Echoside A and echoside B showed higher docking score than remdesivir as COVID-19 drug on four target proteins, i.e., spike protein (-7.9 kcal/mol and -7.8 kcal/mol), RBD-ACE2 (-7.5 kcal/mol and -8.2 kcal/mol), 3CLpro (-8.4 kcal/mol and -9.4 kcal/mol) and RdRp (-7.3 kcal/mol and -8.0 kcal/mol). A combination of genome mining and metabolomic approaches can be used as integrated strategy to elucidate the potential of GMR22 as a resource in the discovery of anti-COVID -19 compound.

SUBMITTER: Melinda YN

PROVIDER: S-EPMC8560767 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Given the scale and rapid spread of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, or 2019-nCoV), there is an urgent need to identify therapeutics that are effective against COVID-19 before vaccines are available. Since the current rate of SARS-CoV-2 knowledge acquisition via traditional research methods is not sufficient to match the rapid spread of the virus, novel strategies of drug discovery for SARS-CoV-2 infection are required. Structure-based virtual screening for example relies primarily on docking scores and does not take the importance of key residues into consideration, which may lead to a significantly higher incidence rate of false-positive results. Our novel in silico approach, which overcomes these limitations, can be utilized to quickly evaluate FDA-approved drugs for repurposing and combination, as well as designing new chemical agents with therapeutic potential for COVID-19. As a result, anti-HIV or antiviral drugs (lopinavir, tenofovir disoproxil, fosamprenavir and ganciclovir), antiflu drugs (peramivir and zanamivir) and an anti-HCV drug (sofosbuvir) are predicted to bind to 3CLPro in SARS-CoV-2 with therapeutic potential for COVID-19 infection by our new protocol. In addition, we also propose three antidiabetic drugs (acarbose, glyburide and tolazamide) for the potential treatment of COVID-19. Finally, we apply our new virus chemogenomics knowledgebase platform with the integrated machine-learning computing algorithms to identify the potential drug combinations (e.g. remdesivir+chloroquine), which are congruent with ongoing clinical trials. In addition, another 10 compounds from CAS COVID-19 antiviral candidate compounds dataset are also suggested by Molecular Complex Characterizing System with potential treatment for COVID-19. Our work provides a novel strategy for the repurposing and combinations of drugs in the market and for prediction of chemical candidates with anti-COVID-19 potential.

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Metabologenomics approach to the discovery of novel compounds from Streptomyces sp. GMR22 as anti-SARS-CoV-2 drugs.

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