@article{Leonidou2023b,
  author    = {Leonidou, Nantia and Renz, Alina and Mostolizadeh, Reihaneh and Dr\"ager, Andreas},
  title     = {{New workflow predicts drug targets against SARS-CoV-2 via metabolic changes in infected
    cells}},
  journal   = {PLOS Computational Biology},
  publisher = {Public Library of Science},
  year      = {2023},
  month     = mar,
  volume    = {19},
  number    = {3},
  pages     = {1--32},
  url       = {https://doi.org/10.1371/journal.pcbi.1010903},
  doi       = {10.1371/journal.pcbi.1010903},
  keywords  = {host-virus interactions; tissue-specific model; COVID-19; SARS-CoV-2;
    antiviral targets; flux balance analysis; flux variability analysis; reaction
    knockout; host-derived enforcement; metabolic modeling; virus mutations; nucleoside
    diphosphate kinase; software engineering; Python},
  abstract  = {COVID-19 is 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 a substantial global threat. This study presents a
    novel workflow to predict robust druggable targets against emerging RNA viruses using metabolic
    networks and information of the viral structure and its genome sequence. For this purpose, we
    implemented pymCADRE and PREDICATE to create tissue-specific metabolic models, construct viral biomass
    functions and predict host-based antiviral targets from more than one genome. We observed that
    pymCADRE reduces the computational time of flux variability analysis for internal optimizations. We
    applied these tools to create a new metabolic network of primary bronchial epithelial cells infected
    with SARS-CoV-2 and identified enzymatic reactions with inhibitory effects. The most promising
    reported targets were from the purine metabolism, while targeting the pyrimidine and carbohydrate
    metabolisms seemed to be promising approaches to enhance viral inhibition. Finally, we computationally
    tested the robustness of our targets in all known variants of concern, verifying our targets'
    inhibitory effects. Since laboratory tests are time-consuming and involve complex readouts to track
    processes, our workflow focuses on metabolic fluxes within infected cells and is applicable for rapid
    hypothesis-driven identification of potentially exploitable antivirals concerning various viruses and
    host cell types.},
}