Researcher will create 'deep-fake' beta-barrel proteins to detect pollution

If you've ever used a text-based artificial-intelligence image generator like Craiyon or DALL-E, you know with a few word prompts that the AI tools create images that are both realistic and completely synthesized. The machine learning that powers such websites will scan millions of images on the internet, analyze them and assemble facets of them into fresh, but fake, images. Now, University of Kansas researchers are working to use a similar machine-learning process to build new proteins designed to detect water pollutants. With a new three-year, $1.5 million grant from the National Science Foundation's Molecular Foundations for Biotechnology program, a KU researcher will use machine learning to create "deep-fake" membrane beta-barrel proteins -- a class of naturally successful biosensors -- designed to detect polluting metal ions in water. "These beta barrels are super useful because they can bring things across membranes," said principal investigator Joanna Slusky, associate professor of molecular biosciences at KU. "Barrels make good enzymes -- there are so many different things that barrels can do."