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.

Microbial communities are key engines that drive earth's biogeochemical cycles. However, existing ecosystem models have only limited ability to predict microbial dynamics and require the calibration of multiple population-specific empirical equations. In contrast, we build on a new kinetic "Microbial Transition State" (MTS) theory of growth derived from first principles. We show how the theory coupled to simple mass and energy balance calculations provides a framework with intrinsically important qualitative properties to model microbial community dynamics. We first show how the theory can simultaneously account for the influence of all the resources needed for growth (electron donor, acceptor, and nutrients) while still producing consistent dynamics that fulfill the Liebig rule of a single limiting substrate. We also show consistent patterns of energy-dependent microbial successions in mixed culture without the need for calibration of population-specific parameters. We then show how this approach can be used to model a simplified activated sludge community. To this end, we compare MTS-derived dynamics with those of a widely used activated sludge model and show that similar growth yields and overall dynamics can be obtained using two parameters instead of twelve. This new kinetic theory of growth grounded by a set of generic physical principles parsimoniously gives rise to consistent microbial population and community dynamics, thereby paving the way for the development of a new class of more predictive microbial ecosystem models.

Distant homology search tools are of great help to predict viral protein functions. However, due to the lack of profile databases dedicated to viruses, they can lack sensitivity. We constructed HMM profiles for more than 80,000 proteins from both phages and archaeal viruses, and performed all pairwise comparisons with HHsearch program. The whole resulting database can be explored through a user-friendly "Phagonaute" interface to help predict functions. Results are displayed together with their genetic context, to strengthen inferences based on remote homology. Beyond function prediction, this tool permits detections of co-occurrences, often indicative of proteins completing a task together, and observation of conserved patterns across large evolutionary distances. As a test, Herpes simplex virus I was added to Phagonaute, and 25% of its proteome matched to bacterial or archaeal viral protein counterparts. Phagonaute should therefore help virologists in their quest for protein functions and evolutionary relationships.