Version 1
: Received: 15 March 2022 / Approved: 22 March 2022 / Online: 22 March 2022 (02:40:09 CET)
Version 2
: Received: 27 July 2022 / Approved: 27 July 2022 / Online: 27 July 2022 (10:37:12 CEST)
Version 3
: Received: 13 January 2023 / Approved: 17 January 2023 / Online: 17 January 2023 (01:50:23 CET)
Leonidou, N.; Renz, A.; Mostolizadeh, R.; Dräger, A. New Workflow Predicts Drug Targets against SARS-CoV-2 via Metabolic Changes in Infected Cells. PLOS Computational Biology, 2023, 19, e1010903. https://doi.org/10.1371/journal.pcbi.1010903.
Leonidou, N.; Renz, A.; Mostolizadeh, R.; Dräger, A. New Workflow Predicts Drug Targets against SARS-CoV-2 via Metabolic Changes in Infected Cells. PLOS Computational Biology, 2023, 19, e1010903. https://doi.org/10.1371/journal.pcbi.1010903.
Leonidou, N.; Renz, A.; Mostolizadeh, R.; Dräger, A. New Workflow Predicts Drug Targets against SARS-CoV-2 via Metabolic Changes in Infected Cells. PLOS Computational Biology, 2023, 19, e1010903. https://doi.org/10.1371/journal.pcbi.1010903.
Leonidou, N.; Renz, A.; Mostolizadeh, R.; Dräger, A. New Workflow Predicts Drug Targets against SARS-CoV-2 via Metabolic Changes in Infected Cells. PLOS Computational Biology, 2023, 19, e1010903. https://doi.org/10.1371/journal.pcbi.1010903.
Abstract
COVID-19is one of the deadliest respiratory diseases, and its emergence caught the pharmaceutical industry off guard.While vaccines have been rapidly developed, treatment options for infected people remain scarce, and COVID-19 poses asubstantial global threat. This study presents a novel workflow to predict robust druggable targets against emergingRNAviruses using metabolic networks and information of the viral structure and its genome sequence.For this purpose, weimplemented pymCADRE and PREDICATE to create tissue-specific metabolic models, construct viral biomass functions andpredict host-based antiviral targets from more than one genome. We observed that pymCADRE reduces the computationaltime of flux variability analysis for internal optimizations. We applied these tools to create a new metabolic network of primarybronchial epithelial cells infected withSARS-CoV-2and identified enzymatic reactions with inhibitory effects.The mostpromising reported targets were from the purine metabolism, while targeting the pyrimidine and carbohydrate metabolismsseemed to be promising approaches to enhance viral inhibition. Finally, we computationally tested the robustness of our targetsin all known variants of concern, verifying our targets’ inhibitory effects. Since laboratory tests are time-consuming and involvecomplex readouts to track processes, our workflow focuses on metabolic fluxes within infected cells and is applicable for rapidhypothesis-driven identification of potentially exploitable antivirals concerning various viruses and host cell types.
Biology and Life Sciences, Biology and Biotechnology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Nantia Leonidou
Commenter's Conflict of Interests: Author