Abstract:
: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.
journal_name
Cell Chem Bioljournal_title
Cell chemical biologyauthors
Fowler PW,Cole K,Gordon NC,Kearns AM,Llewelyn MJ,Peto TEA,Crook DW,Walker ASdoi
10.1016/j.chembiol.2017.12.009subject
Has Abstractpub_date
2018-03-15 00:00:00pages
339-349.e4issue
3eissn
2451-9456issn
2451-9448pii
S2451-9456(17)30437-3journal_volume
25pub_type
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journal_title:Cell chemical biology
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