Dataset Information

A Computer-Aided Drug Design Approach to Predict Marine Drug-Like Leads for SARS-CoV-2 Main Protease Inhibition.

ABSTRACT: The investigation of marine natural products (MNPs) as key resources for the discovery of drugs to mitigate the COVID-19 pandemic is a developing field. In this work, computer-aided drug design (CADD) approaches comprising ligand- and structure-based methods were explored for predicting SARS-CoV-2 main protease (M^pro) inhibitors. The CADD ligand-based method used a quantitative structure-activity relationship (QSAR) classification model that was built using 5276 organic molecules extracted from the ChEMBL database with SARS-CoV-2 screening data. The best model achieved an overall predictive accuracy of up to 67% for an external and internal validation using test and training sets. Moreover, based on the best QSAR model, a virtual screening campaign was carried out using 11,162 MNPs retrieved from the Reaxys^® database, 7 in-house MNPs obtained from marine-derived actinomycetes by the team, and 14 MNPs that are currently in the clinical pipeline. All the MNPs from the virtual screening libraries that were predicted as belonging to class A were selected for the CADD structure-based method. In the CADD structure-based approach, the 494 MNPs selected by the QSAR approach were screened by molecular docking against M^pro enzyme. A list of virtual screening hits comprising fifteen MNPs was assented by establishing several limits in this CADD approach, and five MNPs were proposed as the most promising marine drug-like leads as SARS-CoV-2 M^pro inhibitors, a benzo[f]pyrano[4,3-b]chromene, notoamide I, emindole SB beta-mannoside, and two bromoindole derivatives.

SUBMITTER: Gaudencio SP

PROVIDER: S-EPMC7764804 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Computer-Aided Drug Design Approach to Predict Marine Drug-Like Leads for SARS-CoV-2 Main Protease Inhibition.

Gaudêncio Susana P SP Pereira Florbela F

Marine drugs 20201210 12

The investigation of marine natural products (MNPs) as key resources for the discovery of drugs to mitigate the COVID-19 pandemic is a developing field. In this work, computer-aided drug design (CADD) approaches comprising ligand- and structure-based methods were explored for predicting SARS-CoV-2 main protease (M<sup>pro</sup>) inhibitors. The CADD ligand-based method used a quantitative structure-activity relationship (QSAR) classification model that was built using 5276 organic molecules extr ...[more]

PMID: 33322052

Similar Datasets

Project description:ConspectusSARS-CoV-2 is the etiological pathogen of the COVID-19 pandemic, which led to more than 6.5 million deaths since the beginning of the outbreak in December 2019. The unprecedented disruption of social life and public health caused by COVID-19 calls for fast-track development of diagnostic kits, vaccines, and antiviral drugs. Small molecule antivirals are essential complements of vaccines and can be used for the treatment of SARS-CoV-2 infections. Currently, there are three FDA-approved antiviral drugs, remdesivir, molnupiravir, and paxlovid. Given the moderate clinical efficacy of remdesivir and molnupiravir, the drug-drug interaction of paxlovid, and the emergence of SARS-CoV-2 variants with potential drug-resistant mutations, there is a pressing need for additional antivirals to combat current and future coronavirus outbreaks.In this Account, we describe our efforts in developing covalent and noncovalent main protease (Mpro) inhibitors and the identification of nirmatrelvir-resistant mutants. We initially discovered GC376, calpain inhibitors II and XII, and boceprevir as dual inhibitors of Mpro and host cathepsin L from a screening of a protease inhibitor library. Given the controversy of targeting cathepsin L, we subsequently shifted the focus to designing Mpro-specific inhibitors. Specifically, guided by the X-ray crystal structures of these initial hits, we designed noncovalent Mpro inhibitors such as Jun8-76-3R that are highly selective toward Mpro over host cathepsin L. Using the same scaffold, we also designed covalent Mpro inhibitors with novel cysteine reactive warheads containing di- and trihaloacetamides, which similarly had high target specificity. In parallel to our drug discovery efforts, we developed the cell-based FlipGFP Mpro assay to characterize the cellular target engagement of our rationally designed Mpro inhibitors. The FlipGFP assay was also applied to validate the structurally disparate Mpro inhibitors reported in the literature. Lastly, we introduce recent progress in identifying naturally occurring Mpro mutants that are resistant to nirmatrelvir from genome mining of the nsp5 sequences deposited in the GISAID database. Collectively, the covalent and noncovalent Mpro inhibitors and the nirmatrelvir-resistant hot spot residues from our studies provide insightful guidance for future work aimed at developing orally bioavailable Mpro inhibitors that do not have overlapping resistance profile with nirmatrelvir.

Dataset Information

A Computer-Aided Drug Design Approach to Predict Marine Drug-Like Leads for SARS-CoV-2 Main Protease Inhibition.

Publications

A Computer-Aided Drug Design Approach to Predict Marine Drug-Like Leads for SARS-CoV-2 Main Protease Inhibition.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets