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

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.

New Workflow Predicts Drug Targets Against SARS-CoV-2 via Metabolic Changes in Infected Cells (preprint)

COVID-19 has been characterized as one of the deadliest respiratory diseases, and the emergence of SARS-CoV-2 caught the pharmaceutical industry and the drug development communities off guard. Identifying potential antiviral targets is of great concern, and one way to detect them is by analyzing metabolic changes in infected cells. In this study, we present 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, a tool to create metabolic models using gene expression data, and used this to reconstruct a metabolic network of the human bronchial epithelial cells. We observed that pymCADRE reduces the computational time when flux variability analysis is employed for internal optimizations. Subsequently, we created a fully automated computational tool, named PREDICATE, which analyses one or more nucleotide sequences, introduces given amino acid mutations, and simulates in silico viral infections. Moreover, it predicts a set of host reactions, which, when constrained, inhibit the virus production while preserving the host's optimal state. In the context of SARS-CoV-2, we applied this tool to our metabolic network of bronchial epithelial cells and identified enzymatic reactions with inhibitory effects. From the list of the reported targets, the most promising one was the Nucleoside Diphosphate Kinase, whose inhibitors have already been reported in the literature. Finally, we computationally tested the robustness of our targets in all currently known variants of concern, verifying the inhibitory effect of our target enzyme against SARS-CoV-2. Focusing on the metabolic fluxes of infected cells, we aim at applying our workflow and methods for rapid hypothesis-driven identification of potentially exploitable antivirals to efficiently prevent future pandemics concerning various viruses and host cell types. Availability: The pymCADRE tool and further scripts are publicly available at https://github.com/draeger-lab/ pymCADRE/.