Docking-based virtual screening studies aiming at the covalent inhibition of SARS-CoV-2 MPro by targeting the cysteine 145

Soulère, Laurent; Barbier, Thibaut; Queneau, Yves

doi:10.1016/j.compbiolchem.2021.107463

Cited by 11 publications

(12 citation statements)

References 43 publications

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“…Ghaleb et al also studied some pyridine N -oxide compounds, indicating the most potent one with a predicted pIC 50 (the negative log of the IC 50 when converted to molar) value of 5.294. More similar works can be found in the literature. − …”

Section: Methods and Approachessupporting

confidence: 56%

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 Approachessupporting

confidence: 56%

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

Gao¹,

Wang²,

Chen³

et al. 2022

Chem. Rev.

View full text Add to dashboard Cite

show abstract

“…In subsequent simulations, we observed that these hydrogen bonds were not stable and that CCG‐50014 instead formed new hydrogen bonds and stacking interactions with the residues H41, M49, E166, and Q189. While a limited work has gone into investigating covalent inhibitors that may form disulfide bonds with the catalytic cysteine residue, 64 other studies have focused on several potential covalent inhibitors 65 , 66 , 67 , 68 , 69 , 70 , 71 which may modify the catalytic cysteine residue of M Pro via a C‐S bond. The covalent inhibitors investigated in these studies formed interactions with the residues N142, G143, H164, E166, P168, and Q189, the residues that we also observed to interact with CCG‐50014 in our work.…”

Section: Resultsmentioning

confidence: 99%

Molecular interactions and inhibition of the SARS‐CoV‐2 main protease by a thiadiazolidinone derivative

et al. 2022

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We report molecular interactions and inhibition of the main protease (M Pro ) of SARS‐CoV‐2, a key enzyme involved in the viral life cycle. By using a thiadiazolidinone (TDZD) derivative as a chemical probe, we explore the conformational dynamics of M Pro via docking protocols and molecular dynamics simulations in all‐atom detail. We reveal the local and global dynamics of M Pro in the presence of this inhibitor and confirm the inhibition of the enzyme with an IC 50 value of 1.39 ± 0.22 μM, which is comparable to other known inhibitors of this enzyme.

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“…Undoubtedly, this is a rational strategy to predict the chances of the ligand warhead group forming a covalent adduct with its targeted nucleophilic reactive residue. A typical example of this could be found in the work of Soulère and co-workers [ 103 ]. The researchers conducted a docking-based virtual screening of covalent inhibitors with the use of the Arguslab program.…”

Section: Can Covalent Docking Be Achieved Using Conventional-docking ...mentioning

confidence: 94%

Docking covalent targets for drug discovery: stimulating the computer-aided drug design community of possible pitfalls and erroneous practices

et al. 2022

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The continuous approval of covalent drugs in recent years for the treatment of diseases has led to an increased search for covalent agents by medicinal chemists and computational scientists worldwide. In the computational parlance, molecular docking which is a popular tool to investigate the interaction of a ligand and a protein target, does not account for the formation of covalent bond, and the increasing application of these conventional programs to covalent targets in early drug discovery practice is a matter of utmost concern. Thus, in this comprehensive review, we sought to educate the docking community about the realization of covalent docking and the existence of suitable programs to make their future virtual-screening events on covalent targets worthwhile and scientifically rational. More interestingly, we went beyond the classical description of the functionality of covalent-docking programs down to selecting the ‘best’ program to consult with during a virtual-screening campaign based on receptor class and covalent warhead chemistry. In addition, we made a highlight on how covalent docking could be achieved using random conventional docking software. And lastly, we raised an alert on the growing erroneous molecular docking practices with covalent targets. Our aim is to guide scientists in the rational docking pursuit when dealing with covalent targets, as this will reduce false-positive results and also increase the reliability of their work for translational research. Graphical abstract

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Docking-based virtual screening studies aiming at the covalent inhibition of SARS-CoV-2 MPro by targeting the cysteine 145

Abstract: Graphical abstrcat

Cited by 11 publications

References 43 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

Molecular interactions and inhibition of the SARS‐CoV‐2 main protease by a thiadiazolidinone derivative

Docking covalent targets for drug discovery: stimulating the computer-aided drug design community of possible pitfalls and erroneous practices

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