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Integrated human/SARS-CoV-2 metabolic models present novel treatment strategies against COVID-19.
Bannerman, Bridget P; Júlvez, Jorge; Oarga, Alexandru; Blundell, Tom L; Moreno, Pablo; Floto, R Andres.
  • Bannerman BP; Molecular Immunity Unit, Department of Medicine, University of Cambridge, Cambridge, UK bpc28@cam.ac.uk.
  • Júlvez J; The Center for Research and Interdisciplinarity, Paris, France.
  • Oarga A; Department of Computer Science and Systems Engineering, University of Zaragoza, Zaragoza, Spain.
  • Blundell TL; Department of Computer Science and Systems Engineering, University of Zaragoza, Zaragoza, Spain.
  • Moreno P; Department of Biochemistry, University of Cambridge, Cambridge, UK.
  • Floto RA; EMBL-EBI, European Bioinformatics Institute, Hinxton, UK.
Life Sci Alliance ; 4(10)2021 10.
Article in English | MEDLINE | ID: covidwho-1346863
ABSTRACT
The coronavirus disease 2019 (COVID-19) pandemic caused by the new coronavirus (SARS-CoV-2) is currently responsible for more than 3 million deaths in 219 countries across the world and with more than 140 million cases. The absence of FDA-approved drugs against SARS-CoV-2 has highlighted an urgent need to design new drugs. We developed an integrated model of the human cell and SARS-CoV-2 to provide insight into the virus' pathogenic mechanism and support current therapeutic strategies. We show the biochemical reactions required for the growth and general maintenance of the human cell, first, in its healthy state. We then demonstrate how the entry of SARS-CoV-2 into the human cell causes biochemical and structural changes, leading to a change of cell functions or cell death. A new computational method that predicts 20 unique reactions as drug targets from our models and provides a platform for future studies on viral entry inhibition, immune regulation, and drug optimisation strategies. The model is available in BioModels (https//www.ebi.ac.uk/biomodels/MODEL2007210001) and the software tool, findCPcli, that implements the computational method is available at https//github.com/findCP/findCPcli.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Drug Development / SARS-CoV-2 / COVID-19 / COVID-19 Drug Treatment Type of study: Observational study / Prognostic study Topics: Traditional medicine Limits: Humans Language: English Year: 2021 Document Type: Article Affiliation country: Lsa.202000954

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Drug Development / SARS-CoV-2 / COVID-19 / COVID-19 Drug Treatment Type of study: Observational study / Prognostic study Topics: Traditional medicine Limits: Humans Language: English Year: 2021 Document Type: Article Affiliation country: Lsa.202000954