Tuning Proton Transfer Thermodynamics in SARS-Cov-2 Main Protease: Implications for Catalysis and Inhibitor Design.
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ABSTRACT:
SUBMITTER: Zanetti-Polzi L
PROVIDER: S-EPMC7668740 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:African swine fever (ASF) is a highly contagious hemorrhagic viral disease of domestic and wild pigs that is responsible for serious economic and production losses. It is caused by the African swine fever virus (ASFV), a large and complex icosahedral DNA virus of the Asfarviridae family. Currently, there is no effective treatment or approved vaccine against the ASFV. pS273R, a specific SUMO-1 cysteine protease, catalyzes the maturation of the pp220 and pp62 polyprotein precursors into core-shell proteins. Here, we present the crystal structure of the ASFV pS273R protease at a resolution of 2.3 Å. The overall structure of the pS273R protease is represented by two domains named the "core domain" and the N-terminal "arm domain." The "arm domain" contains the residues from M1 to N83, and the "core domain" contains the residues from N84 to A273. A structure analysis reveals that the "core domain" shares a high degree of structural similarity with chlamydial deubiquitinating enzyme, sentrin-specific protease, and adenovirus protease, while the "arm domain" is unique to ASFV. Further, experiments indicated that the "arm domain" plays an important role in maintaining the enzyme activity of ASFV pS273R. Moreover, based on the structural information of pS273R, we designed and synthesized several peptidomimetic aldehyde compounds at a submolar 50% inhibitory concentration, which paves the way for the design of inhibitors to target this severe pathogen.IMPORTANCE African swine fever virus, a large and complex icosahedral DNA virus, causes a deadly infection in domestic pigs. In addition to Africa and Europe, countries in Asia, including China, Vietnam, and Mongolia, were negatively affected by the hazards posed by ASFV outbreaks in 2018 and 2019, at which time more than 30 million pigs were culled. Until now, there has been no vaccine for protection against ASFV infection or effective treatments to cure ASF. Here, we solved the high-resolution crystal structure of the ASFV pS273R protease. The pS273R protease has a two-domain structure that distinguishes it from other members of the SUMO protease family, while the unique "arm domain" has been proven to be essential for its hydrolytic activity. Moreover, the peptidomimetic aldehyde compounds designed to target the substrate binding pocket exert prominent inhibitory effects and can thus be used in a potential lead for anti-ASFV drug development.
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Project description:The global pandemic of coronavirus disease (COVID-19) caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) created a rush to discover drug candidates. Despite the efforts, so far no vaccine or drug has been approved for treatment. Artificial intelligence offers solutions that could accelerate the discovery and optimization of new antivirals, especially in the current scenario dominated by the scarcity of compounds active against SARS-CoV-2. The main protease (Mpro) of SARS-CoV-2 is an attractive target for drug discovery due to the absence in humans and the essential role in viral replication. In this work, we developed a deep learning platform for de novo design of putative inhibitors of SARS-CoV-2 main protease (Mpro). Our methodology consists of 3 main steps: (1) training and validation of general chemistry-based generative model; (2) fine-tuning of the generative model for the chemical space of SARS-CoV- Mpro inhibitors and (3) training of a classifier for bioactivity prediction using transfer learning. The fine-tuned chemical model generated > 90% valid, diverse and novel (not present on the training set) structures. The generated molecules showed a good overlap with Mpro chemical space, displaying similar physicochemical properties and chemical structures. In addition, novel scaffolds were also generated, showing the potential to explore new chemical series. The classification model outperformed the baseline area under the precision-recall curve, showing it can be used for prediction. In addition, the model also outperformed the freely available model Chemprop on an external test set of fragments screened against SARS-CoV-2 Mpro, showing its potential to identify putative antivirals to tackle the COVID-19 pandemic. Finally, among the top-20 predicted hits, we identified nine hits via molecular docking displaying binding poses and interactions similar to experimentally validated inhibitors.
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