New workflow predicts drug targets against SARS-CoV-2 via metabolic changes in infected cells.

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 cre

SUBMITTER: Leonidou N 

PROVIDER: S-EPMC10035753 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature