Inhibition of SARS-CoV-2 3CL Mpro by Natural and Synthetic Inhibitors: Potential Implication for Vaccine Production Against COVID-19

Structural analysis of the coronavirus main protease for the design of pan-variant inhibitors

With the rapid rate of SARS-CoV-2 Main protease (M^pro) structures deposition, a computational method that can combine all the useful structural features becomes crucial. This research focuses on the frequently occurring atoms and residues to find a generalized strategy for inhibitor design given a large amount of protein complexes from SARS-CoV in contrast to SARS-CoV-2 M^pro. By superposing large numbers of the ligands onto the protein template and grid box, we can analyse which part of the structure is conserved from position-specific interaction for both data sets for the development of pan-M^pro antiviral design. The difference in conserved recognition sites from the crystal structures can be used to determine specificity determining residues for designing selective drugs. We can display pictures of the imaginary shape of the ligand by unionising all atoms from the ligand. We also pinpoint the most probable atom adjustments to imitate the frequently found densities from the ligand atoms statistics. With molecular docking, Molecular Dynamics simulation, and MM-PBSA methods, a carbonyl replacement at the nitrile warhead (N5) of Paxlovid's Nirmatrelvir (PF-07321332) was suggested. By gaining insights into the selectivity and promiscuity regions for proteins and ligands, crucial residues are highlighted, and the antiviral design strategies are proposed.

High-Throughput Assay for Identifying Diverse Antagonists of the Binding Interaction between the ACE2 Receptor and the Dynamic Spike Proteins of SARS-CoV-2

SARS-CoV-2, a coronavirus strain that started a worldwide pandemic in early 2020, attaches to human cells by binding its spike (S) glycoprotein to a host receptor protein angiotensin-converting enzyme 2 (ACE2). Blocking the interaction between the S protein and ACE2 has emerged as an important strategy for preventing viral infection. We systematically developed and optimized an AlphaLISA assay to investigate binding events between ACE2 and the ectodomain of the SARS-CoV-2 S protein (S-614G: residues 1-1208 with a D614G mutation). Using S-614G permits discovering potential allosteric inhibitors that stabilize the S protein in a conformation that impedes its access to ACE2. Over 30,000 small molecules were screened in a high-throughput format for activity against S-614G and ACE2 binding using the AlphaLISA assay. A viral entry assay was used to validate hits using lentiviral particles pseudotyped with the full-length S protein of the Wuhan-1 strain. Two compounds identified in the screen, oleic acid and suramin, blocked the attachment of S-614G to ACE2 and S protein-driven cell entry into Calu-3 and ACE2-overexpressing HEK293T cells. Oleic acid inhibits S-614G binding to ACE2 far more potently than to the receptor-binding domain (RBD, residues 319-541 of SARS-CoV-2 S), potentially indicating a noncompetitive mechanism. The results indicate that using the full-length ectodomain of the S protein can be important for identifying allosteric inhibitors of ACE2 binding. The approach reported here represents a rapidly adaptable format for discovering receptor-binding inhibitors to S-proteins of future coronavirus strains.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Three-Dimensional Structure Characterization and Inhibition Study of Exfoliative Toxin D From Staphylococcus aureus

The Staphylococcus aureus exfoliative toxins (ETs) are the main toxins that produce staphylococcal scalded skin syndrome (SSSS), an abscess skin disorder. The victims of the disease are usually newborns and kids, as well as grown-up people. Five ETs namely, exfoliative toxins A, B, C, D, and E have been identified in S. aureus. The three-dimensional (3D) structure of exfoliative toxins A, B, C and E is known, while that of exfoliative toxin D (ETD) is still unknown. In this work, we have predicted the 3D structure of ETD using protein modeling techniques (software used for 3D structure modeling comprising the MODELLER 9v19 program, SWISS-Model, and I-TESSER). The validation of the build model was done using PROCHECK (Ramachandran plot), ERRAT2, and Verify 3D programs. The results from 3D modeling show that the build model was of good quality as indicated by a GMQE score of 0.88 and by 91.1% amino acid residues in the most favored region of the Ramachandran plot, the ERRAT2 quality factor of 90.1%, and a verify3D score of >0.2 for 99.59% of amino acid residues. The 3D structure analysis indicates that the overall structure of ETD is similar to the chymotrypsin-like serine protease fold. The structure is composed of 13 β-strands and seven α-helices that fold into two well-defined six-strand β-barrels whose axes are roughly perpendicular to each other. The active site residues include histidine-97, aspartic acid-147, and serine-221. This represents the first structure report of ETD. Structural comparison with the other ETs shows some differences, particularly in the loop region, which also change the overall surface charge of these toxins. This may convey variable substrate specificity to these toxins. The inhibition of these toxins by natural (2S albumin and flocculating proteins from Moringa oleifera seeds) and synthetic inhibitors (suramin) was also carried out in this study. The results from docking indicate that the inhibitors bind near the C-terminal domain which may restrict the movement of this domain and may halt the access of the substrate to the active site of this enzyme. Molecular dynamic simulation was performed to see the effect of inhibitor binding to the enzyme. This work will further elucidate the structure–function relationship of this enzyme. The inhibition of this enzyme will lead to a new treatment for SSSS.