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      The SARS-CoV-2 papain-like protease suppresses type I interferon responses by deubiquitinating STING

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          Abstract

          The SARS-CoV-2 papain-like protease (PLpro), which has deubiquitinating activity, suppresses the type I interferon (IFN-I) antiviral response. We investigated the mechanism by which PLpro antagonizes cellular antiviral responses. In HEK392T cells, PLpro removed K63-linked polyubiquitin chains from Lys 289 of the stimulator of interferon genes (STING). PLpro-mediated deubiquitination of STING disrupted the STING-IKKε-IRF3 complex that induces the production of IFN-β and IFN-stimulated cytokines and chemokines. In human airway cells infected with SARS-CoV-2, the combined treatment with the STING agonist diABZi and the PLpro inhibitor GRL0617 resulted in the synergistic inhibition of SARS-CoV-2 replication and increased IFN-I responses. The PLpros of seven human coronaviruses (SARS-CoV-2, SARS-CoV, MERS-CoV, HCoV-229E, HCoV-HKU1, HCoV-OC43, and HCoV-NL63) and four SARS-CoV-2 variants of concern (α, β, γ, and δ) all bound to STING and suppressed STING-stimulated IFN-I responses in HEK293T cells. These findings reveal how SARS-CoV-2 PLpro inhibits IFN-I signaling through STING deubiquitination and a general mechanism used by seven human coronaviral PLpros to dysregulate STING and to facilitate viral innate immune evasion. We also identified simultaneous pharmacological STING activation and PLpro inhibition as a potentially effective strategy for antiviral therapy against SARS-CoV-2.

          Abstract

          A SARS-CoV-2 protease deubiquitinates STING to block antiviral signaling.

          SARS-CoV-2 takes the STING out of host defenses

          The papain-like proteases of human-infecting coronaviruses are important for processing viral polyproteins and have deubiquitinase activities that enable the virus to evade innate defenses. Cao et al. demonstrated that the papain-like protease of SARS-CoV-2, PLpro, suppressed antiviral signaling in cells by deubiquitinating the stimulator of interferon genes (STING). PLpro specifically removed K63-linked polyubiquitin chains at Lys 289 of STING, thereby blocking the interaction of STING with binding partners that are required for STING to activate interferon response factor 3 (IRF3) and downstream type I interferon responses. A STING agonist and a PLpro inhibitor synergistically inhibited SARS-CoV-2 replication in human lung cells, demonstrating the importance of PLpro-mediated STING deubiquitination for SARS-CoV-2 replication in host cells. —AMV

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          Most cited references69

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

            The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk ) is an openly accessible, extensive database of high-accuracy protein-structure predictions. Powered by AlphaFold v2.0 of DeepMind, it has enabled an unprecedented expansion of the structural coverage of the known protein-sequence space. AlphaFold DB provides programmatic access to and interactive visualization of predicted atomic coordinates, per-residue and pairwise model-confidence estimates and predicted aligned errors. The initial release of AlphaFold DB contains over 360,000 predicted structures across 21 model-organism proteomes, which will soon be expanded to cover most of the (over 100 million) representative sequences from the UniRef90 data set.
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              Molnupiravir for Oral Treatment of Covid-19 in Nonhospitalized Patients

              Abstract Background New treatments are needed to reduce the risk of progression of coronavirus disease 2019 (Covid-19). Molnupiravir is an oral, small-molecule antiviral prodrug that is active against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Methods We conducted a phase 3, double-blind, randomized, placebo-controlled trial to evaluate the efficacy and safety of treatment with molnupiravir started within 5 days after the onset of signs or symptoms in nonhospitalized, unvaccinated adults with mild-to-moderate, laboratory-confirmed Covid-19 and at least one risk factor for severe Covid-19 illness. Participants in the trial were randomly assigned to receive 800 mg of molnupiravir or placebo twice daily for 5 days. The primary efficacy end point was the incidence hospitalization or death at day 29; the incidence of adverse events was the primary safety end point. A planned interim analysis was performed when 50% of 1550 participants (target enrollment) had been followed through day 29. Results A total of 1433 participants underwent randomization; 716 were assigned to receive molnupiravir and 717 to receive placebo. With the exception of an imbalance in sex, baseline characteristics were similar in the two groups. The superiority of molnupiravir was demonstrated at the interim analysis; the risk of hospitalization for any cause or death through day 29 was lower with molnupiravir (28 of 385 participants [7.3%]) than with placebo (53 of 377 [14.1%]) (difference, −6.8 percentage points; 95% confidence interval, −11.3 to −2.4; P=0.001). In the analysis of all participants who had undergone randomization, the percentage of participants who were hospitalized or died through day 29 was lower in the molnupiravir group than in the placebo group (6.8% [48 of 709] vs. 9.7% [68 of 699]; difference, −3.0 percentage points; 95% confidence interval, −5.9 to −0.1). Results of subgroup analyses were largely consistent with these overall results; in some subgroups, such as patients with evidence of previous SARS-CoV-2 infection, those with low baseline viral load, and those with diabetes, the point estimate for the difference favored placebo. One death was reported in the molnupiravir group and 9 were reported in the placebo group through day 29. Adverse events were reported in 216 of 710 participants (30.4%) in the molnupiravir group and 231 of 701 (33.0%) in the placebo group. Conclusions Early treatment with molnupiravir reduced the risk of hospitalization or death in at-risk, unvaccinated adults with Covid-19. (Funded by Merck Sharp and Dohme; MOVe-OUT ClinicalTrials.gov number, NCT04575597.)
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                Author and article information

                Contributors
                Journal
                Science Signaling
                Sci. Signal.
                American Association for the Advancement of Science (AAAS)
                1945-0877
                1937-9145
                May 02 2023
                May 02 2023
                : 16
                : 783
                Affiliations
                [1 ]State Key Laboratory of Chemical Oncogenomics, Guangdong Provincial Key Laboratory of Chemical Genomics, Laboratory of Structural Biology and Drug Discovery, Laboratory of Ubiquitination and Targeted Therapy, School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen 518055, PR China.
                [2 ]National Clinical Research Center for Infectious Diseases, Shenzhen Third People’s Hospital, Southern University of Science and Technology, Shenzhen 518112, PR China.
                [3 ]Institute of Chemical Biology, Shenzhen Bay Laboratory, Shenzhen, Guangdong 518132, China.
                Article
                10.1126/scisignal.add0082
                37130168
                c96ee18f-b06d-4672-af71-4e8c43e87403
                © 2023

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