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Protein-Designing AI Opens Door to Medicines Humans Couldn’t Dream Up

Published in Artificial Intelligence, Tech News, Tools.

Designing a protein is a bit like making a cabinet. The first step is building the backbone that holds the protein together. But then comes the hard part: figuring out where to install hinges on the scaffold—that is, finding the best “hotspots”—to put on doors, shelves, and other attachments that ultimately make the cabinet fully functional.

In a way, proteins also have hotspots embedded in their structures. True to their name, “functional sites,” these intriguing nooks and crannies form intricate docks for other proteins or drugs to grab onto. The sites are central to performing most of our basic biological processes. They’re also a massive gold mine for designing new treatments and medical drugs.

The problem? Functional sites are hard to map. Scientists traditionally had to mutate suspecting areas on a protein one by one—switching one amino acid to another—to nail down precise binding spots. Like a detective screening hundreds of suspects, of which there could be many, it’s extremely tedious.

A new study in Science overthrew the whole gamebook. Led by Dr. David Baker at the University of Washington, a team tapped into an AI’s “imagination” to dream up a myriad of functional sites from scratch. It’s a machine mind’s “creativity” at its best—a deep learning algorithm that predicts the general area of a protein’s functional site, but then further sculpts the structure.

As a reality check, the team used the new software to generate drugs that battle cancer and design vaccines against common, if sometimes deadly, viruses. In one case, the digital mind came up with a solution that, when tested in isolated cells, was a perfect match for an existing antibody against a common virus. In other words, the algorithm “imagined” a hotspot from a viral protein, making it vulnerable as a target to design new treatments.

The algorithm is deep learning’s first foray into building proteins around their functions, opening a door to treatments that were previously unimaginable. But the software isn’t limited to natural protein hotspots. “The proteins we find in nature are amazing molecules, but designed proteins can do so much more,” said Baker in a press release. The algorithm is “doing things that none of us thought it would be capable of.”

The Protein Hotspot
Baker’s team are no strangers to predicting proteins with artificial minds. A few years back, they rocked the structural biology field by releasing Rosetta, a software that can predict a protein’s 3D structure based on its amino acid sequence alone. They further mapped protein complexes and designed protein “screwdrivers” from scratch to pry apart undesirable protein interactions. Late last year, they released a deep learning network dubbed trRosetta, an AI “architect” that generalizes how strings of amino acids arrange into intricate structures at the nanoscale.