Mechanistic Insights into SARS-CoV-2 Main Protease Inhibition Reveals Hotspot Residues

Marimuthu, Parthiban; Gorle, Suresh; Reddy, Karnati Konda

doi:10.1021/acs.jcim.1c00928

Cited by 6 publications

(8 citation statements)

References 42 publications

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“…Cobicistat, flavin adenine dinucleotide, and simeprevir were suggested through drug ranking according to binding energy by MM/PB(GB)SA calculations. Other assessments based on docking and MM/PB(GB)SA calculations focused on specific drugs or compounds such as ravidasvir, lopinavir, ritonavir, saquinavir, teicoplanin, GC-376, calpain XII, calpain II, anti-HIV drugs, doxorubicin, chloroquine, quinoline, hydroxychloroquine, noscapine, echinocandins, coumarins, and their derivatives. − …”

Section: Methods and Approachesmentioning

confidence: 99%

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

Gao¹,

Wang²,

Chen³

et al. 2022

Chem. Rev.

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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.

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Section: Methods and Approachesmentioning

confidence: 99%

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

Gao¹,

Wang²,

Chen³

et al. 2022

Chem. Rev.

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“…Molecular mechanics/generalized Born surface area (MM/GBSA) free-energy calculations were conducted using VMD to evaluate the enthalpic contribution to binding free energy. − This follows examples set by several recent works in which MM/GBSA proved useful in estimating protein–protein binding free energy. − In particular, MM/GBSA and related methods have been used to study the effects of mutations on the complex between the SARS-Cov-2 S RDB and the ACE2 ectodomain. − We consider here the same PDB structure (6M0J) as a basis to use the same method and to estimate the enthalpic contribution to binding free energy between SARS-CoV-2 S RBD and ACE2.…”

Section: Methodsmentioning

confidence: 99%

Entropic Overcompensation of the N501Y Mutation on SARS-CoV-2 S Binding to ACE2

Vergara

Gatchel

Abrams

2022

J. Chem. Inf. Model.

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Recent experimental work has shown that the N501Y mutation in the SARS-CoV-2 S glycoprotein’s receptor binding domain (RBD) increases binding affinity to the angiotensin-converting enzyme 2 (ACE2), primarily by overcompensating for a less favorable enthalpy of binding by greatly reducing the entropic penalty for complex formation, but the basis for this entropic overcompensation is not clear [J. Biol. Chem.2021297101151]. We use all-atom molecular dynamics simulations and free-energy calculations to qualitatively assess the impact of the N501Y mutation on the enthalpy and entropy of binding of RBD to ACE2. Our calculations correctly predict that N501Y causes a less favorable enthalpy of binding to ACE2 relative to the original strain. Furthermore, we show that this is overcompensated for by a more entropically favorable increase in large-scale quaternary flexibility and intraprotein root mean square fluctuations of residue positions upon binding in both RBD and ACE2. The enhanced quaternary flexibility stems from N501Y’s ability to remodel the inter-residue interactions between the two proteins away from interactions central to the epitope and toward more peripheral interactions. These findings suggest that an important factor in determining protein–protein binding affinity is the degree to which fluctuations are distributed throughout the complex and that residue mutations that may seem to result in weaker interactions than their wild-type counterparts may yet result in increased binding affinity thanks to their ability to suppress unfavorable entropy changes upon binding.

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“…Originally, the MD parameters were adopted from Marimuthu et al (2021). In short, the complexes were preprocessed using the Protein Preparation Wizard (Schrödinger Release 2020.4: Preparation Wizard; Schrödinger, LLC, New York, NY, 2020) in the Maestro molecular modeling software by adding hydrogens and charges using the OPLS4e force field, which was used throughout this experiment (Harder et al, 2016;Jorgensen & Tirado-Rives, 1988;Jorgensen et al, 1996;Madhavi Sastry et al, 2013;Shivakumar et al, 2010).…”

Section: Molecular Dynamics Simulationsmentioning

confidence: 99%

Isolation and functional analysis of phage‐displayed antibody fragments targeting the staphylococcal superantigen‐like proteins

et al. 2023

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Staphylococcus aureus produces numerous virulence factors that manipulate the immune system, helping the bacteria avoid phagocytosis. In this study, we are investigating three immune evasion molecules called the staphylococcal superantigen‐like proteins 1, 5, and 10 (SSL1, SSL5, and SSL10). All three SSLs inhibit vital host immune processes and contribute to S. aureus immune evasion. This study aimed to identify single‐chain variable fragment (scFvs) antibodies from synthetic antibody phage libraries, which can recognize either of the three SSLs and could block the interaction between the SSLs and their respective human targets. The antibodies were isolated after three rounds of panning against SSL1, SSL5, and SSL10, and their ability to bind to the SSLs was studied using a time‐resolved fluorescence‐based immunoassay. We successfully obtained altogether 44 unique clones displaying binding activity to either SSL1, SSL5, or SSL10. The capability of the SSL‐recognizing scFvs to inhibit the SSLs' function was tested in an MMP9 enzymatic activity assay, a P‐selectin glycoprotein ligand 1 competitive binding assay, and an IgG1‐mediated phagocytosis assay. We could show that one scFv was able to inhibit SSL1 and maintain MMP9 activity in a concentration‐dependent manner. Finally, the structure of this inhibiting scFv was modeled and used to create putative scFv‐SSL1‐complex models by protein–protein docking. The complex models were subjected to a 100‐ns molecular dynamics simulation to assess the possible binding mode of the antibody.

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Mechanistic Insights into SARS-CoV-2 Main Protease Inhibition Reveals Hotspot Residues

Cited by 6 publications

References 42 publications

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

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

Entropic Overcompensation of the N501Y Mutation on SARS-CoV-2 S Binding to ACE2

Isolation and functional analysis of phage‐displayed antibody fragments targeting the staphylococcal superantigen‐like proteins

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