Computational protein design repurposed to explore enzyme vitality and help predict antibiotic resistance.

ABSTRACT: 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 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 that might provide resistance through the simple mechanism considered. As an illustration, we chose dihydrofolate reductase, an essential enz

SUBMITTER: Michael E 

PROVIDER: S-EPMC9868620 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature