Computationally driven discovery of SARS-CoV-2 Mpro inhibitors. From design to experimental validation
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Dateien
Autor:innen
El Khoury, Léa
Jing, Zhifeng
Cuzzolin, Alberto
Deplano, Alessandro
Loco, Daniele
Sattarov, Boris
Hédin, Florent
Ho, Chris
El Ahdab, Dina
Jaffrelot Inizan, Theo
Autor:in (Körperschaft)
Publikationsdatum
01.01.2022
Typ der Arbeit
Studiengang
Typ
01A - Beitrag in wissenschaftlicher Zeitschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Chemical Science
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Reihennummer
Jahrgang / Band
13
Ausgabe / Nummer
Seiten / Dauer
3674-3687
Patentnummer
Verlag / Herausgebende Institution
Royal Society of Chemistry
Verlagsort / Veranstaltungsort
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
We report a fast-track computationally driven discovery of new SARS-CoV-2 main protease (Mpro) inhibitors whose potency ranges from mM for the initial non-covalent ligands to sub-μM for the final covalent compound (IC50 = 830 ± 50 nM). The project extensively relied on high-resolution all-atom molecular dynamics simulations and absolute binding free energy calculations performed using the polarizable AMOEBA force field. The study is complemented by extensive adaptive sampling simulations that are used to rationalize the different ligand binding poses through the explicit reconstruction of the ligand–protein conformation space. Machine learning predictions are also performed to predict selected compound properties. While simulations extensively use high performance computing to strongly reduce the time-to-solution, they were systematically coupled to nuclear magnetic resonance experiments to drive synthesis and for in vitro characterization of compounds. Such a study highlights the power of in silico strategies that rely on structure-based approaches for drug design and allows the protein conformational multiplicity problem to be addressed. The proposed fluorinated tetrahydroquinolines open routes for further optimization of Mpro inhibitors towards low nM affinities.
Schlagwörter
Fachgebiet (DDC)
600 - Technik, Medizin, angewandte Wissenschaften
Veranstaltung
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
2041-6520
2041-6539
2041-6539
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review der ganzen Publikation
Open Access-Status
Gold
Lizenz
Zitation
EL KHOURY, Léa, Zhifeng JING, Alberto CUZZOLIN, Alessandro DEPLANO, Daniele LOCO, Boris SATTAROV, Florent HÉDIN, Chris HO, Dina EL AHDAB, Theo JAFFRELOT INIZAN, Mattia STURLESE, Alice SOSIC, Martina VOLPIANA, Angela LUGATO, Marco BARONE, Barbara GATTO, Maria Ludovica MACCHIA, Massimo BELLANDA, Roberto BATTISTUTTA, Cristiano SALATA, Ivan KONDRATOV, Rustam IMINOV, Andrii KHAIRULIN, Yaroslav MYKHALONOK, Anton POCHEPKO, Volodymyr CHASHKA-RATUSHNYI, Iaroslava KOS, Stefano MORO, Matthieu MONTES, Pengyu REN, Jay W. PONDER, Louis LAGARDÈRE, Jean-Philip PIQUEMAL, Davide SABBADIN und Sebastian WENDEBORN, 2022. Computationally driven discovery of SARS-CoV-2 Mpro inhibitors. From design to experimental validation. Chemical Science. 1 Januar 2022. Bd. 13, S. 3674–3687. DOI 10.1039/D1SC05892D. Verfügbar unter: https://doi.org/10.26041/fhnw-4614