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Characterization of the non-covalent interaction between the PF-07321332 inhibitor and the SARS-CoV-2 main protease.
Macchiagodena, Marina; Pagliai, Marco; Procacci, Piero.
  • Macchiagodena M; Dipartimento di Chimica "Ugo Schiff", Università degli Studi di Firenze, Via della Lastruccia 3, Sesto Fiorentino I-50019, Italy.
  • Pagliai M; Dipartimento di Chimica "Ugo Schiff", Università degli Studi di Firenze, Via della Lastruccia 3, Sesto Fiorentino I-50019, Italy.
  • Procacci P; Dipartimento di Chimica "Ugo Schiff", Università degli Studi di Firenze, Via della Lastruccia 3, Sesto Fiorentino I-50019, Italy. Electronic address: procacci@unifi.it.
J Mol Graph Model ; 110: 108042, 2022 01.
Article in English | MEDLINE | ID: covidwho-1517349
ABSTRACT
We have studied the non-covalent interaction between PF-07321332 and SARS-CoV-2 main protease at the atomic level using a computational approach based on extensive molecular dynamics simulations with explicit solvent. PF-07321332, whose chemical structure has been recently disclosed, is a promising oral antiviral clinical candidate with well-established anti-SARS-CoV-2 activity in vitro. The drug, currently in phase III clinical trials in combination with ritonavir, relies on the electrophilic attack of a nitrile warhead to the catalytic cysteine of the protease. Nonbonded interaction between the inhibitor and the residues of the binding pocket, as well as with water molecules on the protein surface, have been characterized using two different force fields and the two possible protonation states of the main protease catalytic dyad HIS41-CYS145. When the catalytic dyad is in the neutral state, the non-covalent binding is likely to be stronger. Molecular dynamics simulations seems to lend support for an inhibitory mechanism in two

steps:

a first non-covalent addition with the dyad in neutral form and then the formation of the thiolate-imidazolium ion pair and the ligand relocation for finalising the electrophilic attack.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Limits: Humans Language: English Journal: J Mol Graph Model Journal subject: Molecular Biology Year: 2022 Document Type: Article Affiliation country: J.jmgm.2021.108042

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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Limits: Humans Language: English Journal: J Mol Graph Model Journal subject: Molecular Biology Year: 2022 Document Type: Article Affiliation country: J.jmgm.2021.108042