Dataset Information

The Inhibition of SARS-CoV-2 3CL M^pro by Graphene and Its Derivatives from Molecular Dynamics Simulations.

ABSTRACT: At present, the most powerful new drugs for COVID-19 are antibody proteins. In addition, there are some star small molecule drugs. However, there are few studies on nanomaterials. Here, we study the intact graphene (IG), defective graphene (DG), and graphene oxide (GO) interacting with COVID-19 protein. We find that they show progressive inhibition of COVID-19 protein. By using molecular dynamics simulations, we study the interactions between SARS-CoV-2 3CL M^pro and graphene-related materials (GRMs): IG, DG, and GO. The results show that M^pro can be absorbed onto the surfaces of investigated materials. DG and GO interacted with M^pro more intensely, causing the decisive part of M^pro to become more flexible. Further analysis shows that compared to IG and GO, DG can inactivate M^pro and inhibit its expression effectively by destroying the active pocket of M^pro. Our work not only provides detailed and reliable theoretical guidance for the application of GRMs in treating with SARS-CoV-2 but also helps in developing new graphene-based anti-COVID-19 materials.

SUBMITTER: Wang J

PROVIDER: S-EPMC8713398 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The Inhibition of SARS-CoV-2 3CL M<sup>pro</sup> by Graphene and Its Derivatives from Molecular Dynamics Simulations.

Wang Jiawen J Yu Yi Y Leng Tianle T Li Youyong Y Lee Shuit-Tong ST

ACS applied materials & interfaces 20211221 1

At present, the most powerful new drugs for COVID-19 are antibody proteins. In addition, there are some star small molecule drugs. However, there are few studies on nanomaterials. Here, we study the intact graphene (IG), defective graphene (DG), and graphene oxide (GO) interacting with COVID-19 protein. We find that they show progressive inhibition of COVID-19 protein. By using molecular dynamics simulations, we study the interactions between SARS-CoV-2 3CL M<sup>pro</sup> and graphene-related m ...[more]

PMID: 34933561

Similar Datasets

Project description:The main protease, Mpro, of SARS-CoV-2 is required to cleave the viral polyprotein into precise functional units for virus replication and pathogenesis. Here, we report quantitative reporters for Mpro function in living cells in which protease inhibition by genetic or chemical methods results in robust signal readouts by fluorescence (enhanced green fluorescent protein [eGFP]) or bioluminescence (firefly luciferase). These gain-of-signal systems are scalable to high-throughput platforms for quantitative discrimination between Mpro mutants and/or inhibitor potencies as evidenced by validation of several reported inhibitors. Additional utility is shown by single Mpro amino acid variants and structural information combining to demonstrate that both inhibitor conformational dynamics and amino acid differences are able to influence inhibitor potency. We further show that a recent variant of concern (Omicron) has an unchanged response to a clinically approved drug, nirmatrelvir, whereas proteases from divergent coronavirus species show differential susceptibility. Together, we demonstrate that these gain-of-signal systems serve as robust, facile, and scalable assays for live cell quantification of Mpro inhibition, which will help expedite the development of next-generation antivirals and enable the rapid testing of emerging variants. IMPORTANCE The main protease, Mpro, of SARS-CoV-2 is an essential viral protein required for the earliest steps of infection. It is therefore an attractive target for antiviral drug development. Here, we report the development and implementation of two complementary cell-based systems for quantification of Mpro inhibition by genetic or chemical approaches. The first is fluorescence based (eGFP), and the second is luminescence based (firefly luciferase). Importantly, both systems rely upon gain-of-signal readouts such that stronger inhibitors yield higher fluorescent or luminescent signal. The high versatility and utility of these systems are demonstrated by characterizing Mpro mutants and natural variants, including Omicron, as well as a panel of existing inhibitors. These systems rapidly, safely, and sensitively identify Mpro variants with altered susceptibilities to inhibition, triage-nonspecific, or off-target molecules and validate bona fide inhibitors, with the most potent thus far being the first-in-class drug nirmatrelvir.

Dataset Information

The Inhibition of SARS-CoV-2 3CL M^pro by Graphene and Its Derivatives from Molecular Dynamics Simulations.

Publications

The Inhibition of SARS-CoV-2 3CL M<sup>pro</sup> by Graphene and Its Derivatives from Molecular Dynamics Simulations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

The Inhibition of SARS-CoV-2 3CL Mpro by Graphene and Its Derivatives from Molecular Dynamics Simulations.

Publications

The Inhibition of SARS-CoV-2 3CL M<sup>pro</sup> by Graphene and Its Derivatives from Molecular Dynamics Simulations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

The Inhibition of SARS-CoV-2 3CL M^pro by Graphene and Its Derivatives from Molecular Dynamics Simulations.