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The rise of antibiotic resistance threatens modern medicine; to combat it new diagnostic methods are required. Sequencing the whole genome of a pathogen offers the potential to accurately determine which antibiotics will be effective to treat a patient. A key limitation of this approach is that it cannot classify rare or previously unseen mutations. Here we demonstrate that alchemical free energy methods, a well-established class of methods from computational chemistry, can successfully predict whether mutations in Staphylococcus aureus dihydrofolate reductase confer resistance to trimethoprim. We also show that the method is quantitatively accurate by calculating how much the most common resistance-conferring mutation, F99Y, reduces the binding free energy of trimethoprim and comparing predicted and experimentally measured minimum inhibitory concentrations for seven different mutations. Finally, by considering up to 32 free energy calculations for each mutation, we estimate its specificity and sensitivity.

Original publication

DOI

10.1016/j.chembiol.2017.12.009

Type

Journal article

Journal

Cell Chem Biol

Publication Date

15/03/2018

Volume

25

Pages

339 - 349.e4

Keywords

antibiotic susceptibility testing, antimicrobial resistance, clinical microbiology, free energy calculations, molecular dynamics, Anti-Bacterial Agents, Bacterial Proteins, Drug Resistance, Bacterial, Microbial Sensitivity Tests, Mutation, Staphylococcus aureus, Tetrahydrofolate Dehydrogenase, Thermodynamics, Trimethoprim