Computer software is being used by scientists to predict what form a dangerous strain of bacteria might mutate into, much like a chess player would predict about his opponent's strategies.
The predictive software, used by researchers at Duke University Medical Center, could result in better drug design to beat antibiotic-resistant mutations.
AdvertisementBruce Donald, Duke's William and Sue Gross Professor of Computer Science and Biochemistry, said: "This work shows a way to predict bacterial resistance to antibiotics under development, before research progresses and tests of the antibiotics begin in people, and even before doing laboratory procedures to explore potential resistance.
"The protein-design algorithms that predict mutations could be used in a drug-design strategy against any pathogen target that could gain resistance through mutation. It's very expensive and labor-intensive to go back to square one and redesign a drug when a bacterium gains resistance to a drug's existing structure."
Certain bacteria, like MRSA (methicillin-resistant Staphylococcus aureus) are dangerous because they mutate swiftly and cleverly to evade drugs designed to block the pathogen's essential biological pathways. In this study, the researchers examined mutations in a MRSA enzyme called dihydrofolate reductase (DHFR), which is targeted by several drugs. Almost every living organism has a version of DHFR, because it is an enzyme needed at a critical step in a pathway that takes folic acid and turns it into thymidine, one of the four building blocks of DNA-the "T" in the A-C-G-T nucleotides.
Donald said: "We are excited about the prediction power we have, in this case with MRSA, because we used a sophisticated algorithm that models protein and drug flexibility while searching for mutants. We used our algorithm to find mutation candidates that satisfy both a positive design - structures that still allow the bacterial enzyme to do its work - and also negative design - they block the ability of a brand new antibiotic drug to do its job. The algorithm found candidates that would be able to block the antibiotic while at the same time allowing the native reaction of the bacterial enzyme to occur."
"We're basically trying to do a pre-emptive strike, and this study is a step toward identifying antibiotics that can pre-emptively deal with possible resistance in nature," said lead author Ivelin Georgiev, who did the work while he was a graduate student in the Donald lab and has since moved to the National Institutes of Health.
The study appears in the Proceedings of the National Academy of Sciences.