Garbage in, garbage out: how reliable training data improved a virtual screening approach against SARS-CoV-2 MPro

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dc.citation.titleFrontiers in Pharmacology
dc.citation.volume14
dc.contributor.otherDr. Hilgenfeld, R. provide the MPro expression vector
dc.creatorRuatta, Santiago M.
dc.creatorPrada Gori, Denis N.
dc.creatorFló Díaz, Martín
dc.creatorCarlucci, Renzo
dc.creatorMedrán, Noelia Soledad
dc.creatorLabadie, Guillermo Roberto
dc.creatorMartínez Amezaga, Maitena
dc.creatorDelpiccolo, Carina M. L.
dc.creatorMata, Ernesto Gabino
dc.creatorTalevi, Alan
dc.creatorComini, Marcelo A.
dc.date.accessioned2025-02-11T14:27:01Z
dc.date.available2025-02-11T14:27:01Z
dc.date.issued2023-06-22
dc.description.abstractIntroduction: The identification of chemical compounds that interfere with SARS-CoV-2 replication continues to be a priority in several academic and pharmaceutical laboratories. Computational tools and approaches have the power to integrate, process and analyze multiple data in a short time. However, these initiatives may yield unrealistic results if the applied models are not inferred from reliable data and the resulting predictions are not confirmed by experimental evidence. Methods: We undertook a drug discovery campaign against the essential major protease (MPro) from SARS-CoV-2, which relied on an in silico search strategy –performed in a large and diverse chemolibrary– complemented by experimental validation. The computational method comprises a recently reported ligand-based approach developed upon refinement/learning cycles, and structure-based approximations. Search models were applied to both retrospective (in silico) and prospective (experimentally confirmed) screening. Results: The first generation of ligand-based models were fed by data, which to a great extent, had not been published in peer-reviewed articles. The first screening campaign performed with 188 compounds (46 in silico hits and 100 analogues, and 40 unrelated compounds: flavonols and pyrazoles) yielded three hits against MPro (IC50 ≤ 25 μM): two analogues of in silico hits (one glycoside and one benzo-thiazol) and one flavonol. A second generation of ligand-based models was developed based on this negative information and newly published peer-reviewed data for MPro inhibitors. This led to 43 new hit candidates belonging to different chemical families. From 45 compounds (28 in silico hits and 17 related analogues) tested in the second screening campaign, eight inhibited MPro with IC50 = 0.12–20 μM and five of them also impaired the proliferation of SARS-CoV-2 in Vero cells (EC50 7–45 μM). Discussion: Our study provides an example of a virtuous loop between computational and experimental approaches applied to target-focused drug discovery against a major and global pathogen, reaffirming the well-known “garbage in, garbage out” machine learning principle.
dc.description.filFil: Ruatta, Santiago M. Institut Pasteur de Montevideo. Laboratorio de Biología Redox de Tripanosomátidos; Uruguay.
dc.description.filFil: Ruatta, Santiago M. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina.
dc.description.filFil: Prada Gori, Denis N. Universidad Nacional de La Plata. Facultad De Ciencias Exactas. Laboratorio de Investigación y Desarrollo de Bioactivos (LIDeB); Argentina.
dc.description.filFil: Fló Díaz, Martín. Institut Pasteur de Montevideo. Laboratorio de Inmunovirología; Uruguay.
dc.description.filFil: Carlucci, Renzo. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario (IQUIR-CONICET); Argentina.
dc.description.filFil: Medrán, Noelia Soledad. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario (IQUIR-CONICET); Argentina.
dc.description.filFil: Labadie, Guillermo Roberto. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario (IQUIR-CONICET); Argentina.
dc.description.filFil: Martínez Amezaga, Maitena. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario (IQUIR-CONICET); Argentina.
dc.description.filFil: Delpiccolo, Carina M. L. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario (IQUIR-CONICET); Argentina.
dc.description.filFil: Mata, Ernesto Gabino. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario (IQUIR-CONICET); Argentina.
dc.description.filFil: Talevi, Alan. Universidad Nacional de La Plata. Facultad De Ciencias Exactas. Laboratorio de Investigación y Desarrollo de Bioactivos (LIDeB); Argentina.
dc.description.filFil: Talevi, Alan. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET); Argentina.
dc.description.filFil: Comini, Marcelo A. Institut Pasteur de Montevideo. Laboratorio de Biología Redox de Tripanosomátidos; Uruguay.
dc.description.sponsorshipUrgence COVID-19 Fundraising Campaign of Institut Pasteur
dc.description.sponsorshipInternational Centre for Genetic Engineering and Biotechnology: CRP/URY20-03
dc.description.sponsorshipFondo para la Convergencia Estructural del Mercosur (FOCEM): grant number COF 03/11
dc.description.sponsorshipNational Research Foundation of Korea (NRF): MSIT, No. NRF-2017M3A9G6068254 (grant funded by the Korea Government)
dc.description.sponsorshipGerman Research Foundation: KU 1371/9-1
dc.description.sponsorshipConsejo Nacional de Investigaciones Científicas y Técnicas (CONICET)
dc.description.sponsorshipPrograma de Alimentos y Salud Humana (PAyS): IDB-R.O.U. 4950/OC-UR
dc.description.sponsorshipConsejo Nacional de Humanidades, Ciencias y Tecnologías (CONACyT): Proyecto No. 251726
dc.description.versionpeerreviewed
dc.format.extent1-23
dc.identifier.e-issn1663-9812
dc.identifier.urihttps://hdl.handle.net/2133/28819
dc.language.isoen
dc.publisherFrontiers
dc.relation.publisherversionhttps://doi.org/10.3389/fphar.2023.1193282
dc.relation.publisherversionhttps://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2023.1193282/full
dc.rightsopenAccess
dc.rights.holderRuatta, Santiago M.
dc.rights.holderPrada Gori, Denis N.
dc.rights.holderFló Díaz, Martín
dc.rights.holderCarlucci, Renzo
dc.rights.holderMedrán, Noelia Soledad
dc.rights.holderLabadie, Guillermo Roberto
dc.rights.holderMartínez Amezaga, Maitena
dc.rights.holderDelpiccolo, Carina M. L.
dc.rights.holderMata, Ernesto Gabino
dc.rights.holderTalevi, Alan
dc.rights.holderComini, Marcelo A.
dc.rights.holderUniversidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas
dc.rights.textAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectIn silico screening
dc.subjectCoronavirus
dc.subjectCOVID-19
dc.subjectProtease
dc.subjectTarget-based
dc.subjectDrug discovery
dc.subjectRubbish in Rubbish out
dc.subjectArtificial intelligence
dc.subjectGIGO
dc.subjectAI
dc.titleGarbage in, garbage out: how reliable training data improved a virtual screening approach against SARS-CoV-2 MPro
dc.typearticulo
dc.type.collectionarticulo
dc.type.versionpublishedVersion

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