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Prediction of the SARS-CoV-2 (2019-nCoV) 3C-like protease (3CL ^pro) structure: virtual screening reveals velpatasvir, ledipasvir, and other drug repurposing candidates.

ABSTRACT: We prepared the three-dimensional model of the SARS-CoV-2 (aka 2019-nCoV) 3C-like protease (3CL ^pro) using the crystal structure of the highly similar (96% identity) ortholog from the SARS-CoV. All residues involved in the catalysis, substrate binding and dimerisation are 100% conserved. Comparison of the polyprotein PP1AB sequences showed 86% identity. The 3C-like cleavage sites on the coronaviral polyproteins are highly conserved. Based on the near-identical substrate specificities and high sequence identities, we are of the opinion that some of the previous progress of specific inhibitors development for the SARS-CoV enzyme can be conferred on its SARS-CoV-2 counterpart. With the 3CL ^pro molecular model, we performed virtual screening for purchasable drugs and proposed 16 candidates for consideration. Among these, the antivirals ledipasvir or velpatasvir are particularly attractive as therapeutics to combat the new coronavirus with minimal side effects, commonly fatigue and headache. The drugs Epclusa (velpatasvir/sofosbuvir) and Harvoni (ledipasvir/sofosbuvir) could be very effective owing to their dual inhibitory actions on two viral enzymes.

SUBMITTER: Chen YW

PROVIDER: S-EPMC7062204 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:COVID-19 caused by a novel coronavirus (SARS-CoV-2) has been spreading all over the world since the end of 2019, and no specific drug has been developed yet. 3C-like protease (3CLpro) acts as an important part of the replication of novel coronavirus and is a promising target for the development of anticoronavirus drugs. In this paper, eight machine learning models were constructed using naïve Bayesian (NB) and recursive partitioning (RP) algorithms for 3CLpro on the basis of optimized two-dimensional (2D) molecular descriptors (MDs) combined with ECFP_4, ECFP_6, and MACCS molecular fingerprints. The optimal models were selected according to the results of 5-fold cross verification, test set verification, and external test set verification. A total of 5766 natural compounds from the internal natural product database were predicted, among which 369 chemical components were predicted to be active compounds by the optimal models and the EstPGood values were more than 0.6, as predicted by the NB (MD + ECFP_6) model. Through ADMET analysis, 31 compounds were selected for further biological activity determination by the fluorescence resonance energy transfer (FRET) method and cytopathic effect (CPE) detection. The results indicated that (+)-shikonin, shikonin, scutellarein, and 5,3',4'-trihydroxyflavone showed certain activity in inhibiting SARS-CoV-2 3CLpro with the half-maximal inhibitory concentration (IC50) values ranging from 4.38 to 87.76 μM. In the CPE assay, 5,3',4'-trihydroxyflavone showed a certain antiviral effect with an IC50 value of 8.22 μM. The binding mechanism of 5,3',4'-trihydroxyflavone with SARS-CoV-2 3CLpro was further revealed through CDOCKER analysis. In this study, 3CLpro prediction models were constructed based on machine learning algorithms for the prediction of active compounds, and the activity of potential inhibitors was determined by the FRET method and CPE assay, which provide important information for further discovery and development of antinovel coronavirus drugs.

Project description:BackgroundSevere acute respiratory syndrome (SARS) has been initiating pandemics since the beginning of the century. In December 2019, the world was hit again by a devastating SARS episode that has so far infected almost four million individuals worldwide, with over 200,000 fatalities having already occurred by mid-April 2020, and the infection rate continues to grow exponentially. SARS coronavirus 2 (SARS-CoV-2) is a single stranded RNA pathogen which is characterised by a high mutation rate. It is vital to explore the mutagenic capability of the viral genome that enables SARS-CoV-2 to rapidly jump from one host immunity to another and adapt to the genetic pool of local populations.MethodsFor this study, we analysed 2301 complete viral sequences reported from SARS-CoV-2 infected patients. SARS-CoV-2 host genomes were collected from The Global Initiative on Sharing All Influenza Data (GISAID) database containing 9 genomes from pangolin-CoV origin and 3 genomes from bat-CoV origin, Wuhan SARS-CoV2 reference genome was collected from GeneBank database. The Multiple sequence alignment tool, Clustal Omega was used for genomic sequence alignment. The viral replicating enzyme, 3-chymotrypsin-like cysteine protease (3CLpro) that plays a key role in its pathogenicity was used to assess its affinity with pharmacological inhibitors and repurposed drugs such as anti-viral flavones, biflavanoids, anti-malarial drugs and vitamin supplements.ResultsOur results demonstrate that bat-CoV shares > 96% similar identity, while pangolin-CoV shares 85.98% identity with Wuhan SARS-CoV-2 genome. This in-depth analysis has identified 12 novel recurrent mutations in South American and African viral genomes out of which 3 were unique in South America, 4 unique in Africa and 5 were present in-patient isolates from both populations. Using state of the art in silico approaches, this study further investigates the interaction of repurposed drugs with the SARS-CoV-2 3CLpro enzyme, which regulates viral replication machinery.ConclusionsOverall, this study provides insights into the evolving mutations, with implications to understand viral pathogenicity and possible new strategies for repurposing compounds to combat the nCovid-19 pandemic.

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.

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Prediction of the SARS-CoV-2 (2019-nCoV) 3C-like protease (3CL ^pro) structure: virtual screening reveals velpatasvir, ledipasvir, and other drug repurposing candidates.

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Prediction of the SARS-CoV-2 (2019-nCoV) 3C-like protease (3CL pro) structure: virtual screening reveals velpatasvir, ledipasvir, and other drug repurposing candidates.

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OmicsDI is part of the ELIXIR infrastructure

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Prediction of the SARS-CoV-2 (2019-nCoV) 3C-like protease (3CL ^pro) structure: virtual screening reveals velpatasvir, ledipasvir, and other drug repurposing candidates.