New Publication: Predicting whether mutations confer resistance to an antibiotic

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Due to the rise of antibiotic resistance, it is increasingly important that your clinician knows which antibiotics will work (and which will not). Traditionally, this is done in hospital microbiology labs by growing a sample taken from the infection site, and then testing how a range of antibiotics affect its growth, or, ideally, kill it. Despite automation, this is a expensive, open to interpretation and can take days or weeks, depending on the organising.

Given the dramatic reduction in cost of whole genome sequencing, it is comparatively cheap and straightforward to simply sequence the genome of the infecting pathogen and then, by comparing to a reference genome, mutations in genes known to confer resistance to antibiotics can be identified. If the mutations have been seen before, and so their effect on the antibiotic is known, this information can be returned rapidly to the clinician. The rub is if the mutation is novel, or just has not been seen enough times. Hence methods that can predict the effect of individual protein mutations on the action of an antibiotic are needed.

This paper demonstrates one such method. The key hypothesis is that bacterial mutations that confer resistance do so by reducing how well the antibiotic binds (but at the same time not affecting the binding of the natural substrate of the protein, otherwise the bacterium would die). Hence to predict if a mutation confers resistance, you have to calculate how the mutation affects the binding free energy of the antibiotic (and maybe also the natural substrate).

Here we validate this approach on trimethoprim in S. aureus. Trimethoprim binds and inhibits the DHFR protein, which is encoded by the dfrB gene. We use alchemical free energy methods to calculate the effect of seven different protein mutations on the binding of the trimethoprim and the natural substrate, dihydrofolic acid. We are able to not only distinguish the three mutations known to confer resistance from the four that do not, but are also able to predict the size of the effect.

The application of genetic sequencing in clinical microbiology is not science fiction; it is now routine in England for new cases of Tuberculosis, hence methods like this are urgently needed. In future work we will investigate (i) optimising the method and (ii) applying it to some of the proteins in M. tuberculosis responsible for antibiotic resistance.

You can view the paper, either on Cell Chemical Biology’s website (which may require a subscription), or you can get a version from my website. If you want to reproduce some of the calculations, you can download the GROMACS input files from this GitHub repository.

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