'They have shown that this is not some impossible thing': Academic lab copies Google’s big biological breakthrough
By Jason Mast, Editor, 071521
When Demis Hassabis, CEO of Google’s AI outfit DeepMind, announced last year that they had cracked one of the toughest puzzles in biology — successfully predicting a protein’s shape from its amino acid sequence — Minkyung Baek watched with a curious mixture of dread and excitement.
“It felt like I just lost my job,” said Baek, a postdoc at the University of Washington’s Institute for Protein Design.
Baek had spent the last three years trying to do the exact same thing, working with what for years had been the leading protein design technology at the leading protein design lab. Now DeepMind had fully eclipsed her. She felt the scientific community’s excitement, even as she pondered her professional future.
DeepMind’s breakthrough, though, had a caveat: The company hadn’t actually shown how they cracked the puzzle, nor had they made their headline-grabbing software available to any researchers outside of Google’s disparate offices. Baek wondered if she could, with the few breadcrumbs Google left, reconstruct DeepMind’s software and distribute it to the world.
On Thursday, she showed she could. Baek and her team at UW’s Baker lab detailed machine learning software in Science that was almost — albeit not quite — as powerful as DeepMind in predicting a protein’s structure from its sequence, and demonstrated how it could be deployed to probe questions intricately linked to understanding disease and designing new drugs. They have made the tool, known as RoseTTaFold, available on GitHub, where UW claims it has already been downloaded by over 140 research teams.
“If you wanted to be negative about it, you could say they’re playing catch-up and got results that were not quite as good,” said John Moult, a
