Computational protein design repurposed to explore enzyme vitality and help predict antibiotic resistance - Michael, Eleni; Saint-Jalme, Rémy; Mignon, David; Simonson, Thomas (Frontiers in Molecular Biosciences, 2023)
In response to antibiotics that inhibit a bacterial enzyme, resistance mutations inevitably arise. Predicting them ahead of time would aid target selection and drug design. The simplest resistance mechanism would be to reduce the antibiotic binding without sacrificing too much substrate binding. The property that reflects this is the enzyme ``vitality'', defined here as the difference between the inhibitor and substrate binding free energies. To predict such mutations, we borrow methodology from computational protein design. We use a Monte Carlo exploration of mutation space and vitality changes, allowing us to rank thousands of mutations and identify ones likely to provide resistance through the simple mechanism considered. As a proof of concept, we considered dihydrofolate reductase, an essential enzyme targeted by several antibiotics. We simulated its complexes with the inhibitor trimethoprim and the substrate dihydrofolate. 20 active site positions were redesigned individually, then in pairs or quartets. We computed the resulting binding free energy and vitality changes. 10 positions exhibited mutations with significant vitality gains. Out of seven known resistance mutations involving active site positions, five were correctly predicted. Couplings between designed positions were predicted to be small, suggesting that over the course of evolution, resistance mutations involving several positions do not need them to mutate all at once: the underlying point mutations can appear and become fixed one after the other.