Scientists have developed artificial intelligence software that can create proteins that may be useful as vaccines, cancer treatments, or even tools for pulling carbon pollution out of the air. This research, reported today in the journal Science, was led by the University of Washington School of Medicine and Harvard University. The article is titled "Scaffolding protein functional sites using deep learning." "The proteins we find in nature are amazing molecules, but designed proteins can do so much more," said senior author David Baker, an HHMI Investigator and professor of biochemistry at UW Medicine. "In this work, we show that machine learning can be used to design proteins with a wide variety of functions." For decades, scientists have used computers to try to engineer proteins.

Scientists have developed artificial intelligence software that can create proteins that may be useful as vaccines, cancer treatments, or even tools for pulling carbon pollution out of the air. This research, reported today in the journal Science, was led by the University of Washington School of Medicine and Harvard University. The article is titled "Scaffolding protein functional sites using deep learning." "The proteins we find in nature are amazing molecules, but designed proteins can do so much more," said senior author David Baker, an HHMI Investigator and professor of biochemistry at UW Medicine. "In this work, we show that machine learning can be used to design proteins with a wide variety of functions."

Scientist are now combining recent advances in evolutionary analysis and deep learning to build three-dimensional models of how most proteins in eukaryotes interact. The research effort has implications for understanding the biochemical processes that are common to all animals, plants, and fungi. The open-access work appears Nov. 11 in Science. As part of a multi-institutional collaboration, the lab of David Baker at the UW Medicine Institute for Protein Design helped guide this new development. "To really understand the cellular conditions that give rise to health and disease, it's essential to know how different proteins in a cell work together," Baker said.

Scientists have waited months for access to high-accuracy protein structure prediction since DeepMind presented remarkable progress in this area at the 2020 Critical Assessment of Structure Prediction, or CASP14, conference. The wait is now over. Researchers at the Institute for Protein Design at the University of Washington School of Medicine in Seattle have largely recreated the performance achieved by DeepMind on this important task. These results will be published by the journal Science. Unlike DeepMind, the UW Medicine team has already made their method, dubbed RoseTTAFold, freely available.