Software predicts MRSA's response to new drugs before they are tested on patients

Jan. 5, 2015

New drugs are needed as antibiotic resistance increases—and so are ways to maximize the effective lifespan of these drugs. To address the latter, Duke University researchers used software they developed to predict a constantly-evolving infectious bacterium's countermoves to new drugs ahead of time, before they are tested on patients. In a study appearing in the journal Proceedings of the National Academy of Sciences (PNAS), the team used their program to identify the genetic changes that will allow methicillin-resistant Staphylococcus aureus(MRSA) to develop resistance to a class of new experimental drugs that show promise against MRSA.

Until now, scientists trying to predict the genetic changes that would enable a bacterium to evade a particular drug have looked up possible mutations from “libraries” of resistance mutations that have been observed previously. But this approach falls short when it comes to anticipating how bacteria will adapt to new drugs, where the microbes can't be counted on to change in repeatable, predictable ways.

To overcome this problem, researchers used a protein design algorithm they developed, called OSPREY, to identify DNA sequence changes in the bacteria that would enable the resulting protein to block the drug from binding, while still performing its normal work within the cell.

The team focused on a new class of experimental drugs that work by binding and inhibiting a bacterial enzyme called dihydrofolate reductase (DHFR), which plays an essential role in building DNA and other processes. The drugs, called propargyl-linked antifolates, show promise as a treatment for MRSA infections but have not yet been tested in humans.

From a ranked list of possible mutations, the researchers zeroed in on four tiny differences, known as single nucleotide polymorphisms (SNPs), that would theoretically confer resistance. Though none of the mutations they identified had been reported previously, experiments with live bacteria in the lab showed their predictions were right.

When the scientists treated MRSA with the new drugs and sequenced the bacteria that survived, more than half of the surviving colonies carried the predicted mutation that conferred the greatest resistance—a tiny change that reduced the drugs' effectiveness by 58-fold.

Read the study abstract at the PNAS website