This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Biochemists at the University of Wisconsin–Madison are using AI to tackle one of modern medicine's most pressing challenges: the rise of antibiotic-resistant bacterial infections. Using data collected in their lab, biochemistry professor Vatsan Raman and his team built an AI model to identify new possibilities for fighting bacteria with one of their natural enemies.
Their findings, published in the journal Cell Systems, could help accelerate the development of alternatives to traditional antibiotic drugs. For decades, antibiotics have been the frontline defense against bacterial illnesses such as strep throat and urinary tract infections. But bacteria evolve quickly, developing resistance to drugs faster than we can develop new treatments.
The result is a slew of highly infectious diseases for which we have fewer effective treatments. To address this growing threat, scientists are exploring new ways to target unwanted bacteria. One promising approach is phage therapy, which uses naturally occurring and engineered viruses known as bacteriophages, or phages, to infect and destroy specific bacteria.
"Phage therapy gives us an opportunity to look at the strategies that evolution has provided and harness them in new ways to be more effective at killing off bacterial infection," says Raman. But this is no easy task. Treating a bacterial infection requires a therapeutic that can efficiently decimate the pathogen's entire population.
Natural phages, as Raman explains it, have evolved for mediocrity, not maximum lethality. If phages kill off an entire population of bacteria, they lose the hosts they need to survive and reproduce. Meanwhile, bacteria have also spent millions of years evolving alongside phages and have developed their own protective defenses to evade phage infections.
To design and engineer phages that overcome these evolutionary limitations, researchers in the Raman Lab developed an AI model that helps scientists identify phage mutations with the greatest potential to improve efficiency at targeting and killing bacteria. "The model can learn the rules by which phages evolve to be successful and can use those rules to engineer phages that are highly effective against pathogens," says Raman. To evaluate the model, the researchers presented it with several tests that mirror real-world obstacles facing phage therapy development.
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