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Computer Program Predicts MRSA's Next Move
“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,” said Doctor Bruce Donald, Duke’s William and Sue Gross Professor of Computer Science and Biochemistry.
“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.”
The researchers examined mutations in a MRSA enzyme called dihydrofolate reductase (DHFR), which is targeted by several drugs. “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,” Donald said. “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 Doctor Ivelin Georgiev,.
Donald said that some bacteria, such as MRSA, escape antibiotics by evolving mutations to change the shape of the active site of their enzymes. “Our algorithm tries to predict that process,” he said.
The Duke team built on its computer program for designing enzyme structures to uncover the possible “chess moves” that MRSA might make to evade a drug that binds to DHFR to slow or stop its actions. That algorithm features a “dead-end elimination” feature that can process all of the possible chemical interactions to sort through outcomes that would not work well for the bacterium.
MEDICA.de; Source: Duke Medicine