The machine learning process allows websites to scan millions of images on the internet to create fresh 'deep-fakes' could also be leveraged to detect water pollution, scientists say.
The team at the University of Kansas is looking at using a similar machine-learning process to generate a type of protein structure known as beta barrels that could be used in sensors to detect metal pollutants.
"These beta barrels are super useful because they can bring things across membranes," said principal investigator Joanna Slusky. "Barrels make good enzymes – there are so many different things that barrels can do."
Slusky and her co-principal investigators, professors Rachel Kolodny and Margarita Osadchy of Haifa University in Israel (along with KU postdoctoral fellow Daniel Montezano), will develop a new machine-learning process that generates beta-barrels with scaffolds similar to those found in nature, but with different sequences...