Categories: DeepMindrelease

DeepMind says it will release the structure of every protein known to science

So far the trove consists of 350,000 newly predicted protein structures. DeepMind says it will predict and release the structures for more than 100 million more in the next few months—more or less all proteins known to science. 

“Protein folding is a problem I’ve had my eye on for more than 20 years,” says DeepMind cofounder and CEO Demis Hassabis. “It’s been a huge project for us. I would say this is the biggest thing we’ve done so far. And it’s the most exciting in a way, because it should have the biggest impact in the world outside of AI.”

Proteins are made of long ribbons of amino acids, which twist themselves up into complicated knots. Knowing the shape of a protein’s knot can reveal what that protein does, which is crucial for understanding how diseases work and developing new drugs—or identifying organisms that can help tackle pollution and climate change. Figuring out a protein’s shape takes weeks or months in the lab. AlphaFold can predict shapes to the nearest atom in a day or two.

The new database should make life even easier for biologists. AlphaFold might be available for researchers to use, but not everyone will want to run the software themselves. “It’s much easier to go and grab a structure from the database than it is running it on your own computer,” says David Baker of the Institute for Protein Design at the University of Washington, whose lab has built its own tool for predicting protein structure, called RoseTTAFold, based on AlphaFold’s approach.

In the last few months Baker’s team has been working with biologists who were previously stuck trying to figure out the shape of proteins they were studying. “There’s a lot of pretty cool biological research that’s been really sped up,” he says. A public database containing hundreds of thousands of ready-made protein shapes should be an even bigger accelerator.  

“It looks astonishingly impressive,” says Tom Ellis, a synthetic biologist at Imperial College London studying the yeast genome, who is excited to try the database. But he cautions that most of the predicted shapes have not yet been verified in the lab.  

Atomic precision

In the new version of AlphaFold, predictions come with a confidence score that the tool uses to flag how close it thinks each predicted shape is to the real thing. Using this measure, DeepMind found that AlphaFold predicted shapes for 36% of human proteins with an accuracy that is correct down to the level of individual atoms. This i

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