Using genomic and structural information from SARS-CoV-2, we created a biomass function capturing its amino and nucleic acid requirements and incorporated this into a metabolic model of the human lung cell to predict metabolic perturbations that inhibit virus reproduction. Viruses rely on their host for reproduction. Here, we made use of genomic and structural information to create a biomass function capturing the amino and nucleic acid requirements of SARS-CoV-2. Incorporating this biomass function into a stoichiometric metabolic model of the human lung cell and applying metabolic flux balance analysis, we identified host-based metabolic perturbations inhibiting SARS-CoV-2 reproduction. Our results highlight reactions in the central metabolism, as well as amino acid and nucleotide biosynthesis pathways. By incorporating host cellular maintenance into the model based on available protein expression data from human lung cells, we find that only few of these metabolic perturbations are able to selectively inhibit virus reproduction. Some of the catalysing enzymes of such reactions have demonstrated interactions with existing drugs, which can be used for experimental testing of the presented predictions using gene knockouts and RNA interference techniques. In summary, the developed computational approach offers a platform for rapid, experimentally testable generation of drug predictions against existing and emerging viruses based on their biomass requirements.
【초록키워드】 SARS-CoV-2, drugs, virus, nucleic acid, human lung, RNA interference, information, genomic, predict, platform, Amino acid, cellular, nucleotide, Interaction, Analysis, Pathways, Perturbation, gene knockout, reaction, enzyme, protein expression, human lung cells, central metabolism, offer, Host, approach, Cell, highlight, inhibit, can be used, demonstrated, inhibiting, 【제목키워드】 SARS-CoV-2, human lung, Perturbation, Cell,