In 2020, an artificial intelligence lab called DeepMind unveiled technology that can predict the shape of proteins — the microscopic mechanisms that drive the behavior of the human body and all other living things.
A year later, the lab shared the tool, called AlphaFold, with scientists and released predicted shapes for more than 350,000 proteins, including all proteins expressed by the human genome. It immediately changed the course of biological research. If scientists can identify the shapes of proteins, they could accelerate the ability to understand disease, create new drugs and otherwise explore the mysteries of life on Earth.
Now DeepMind has released predictions for almost every protein known to science. On Thursday, the London-based lab, which is owned by the same parent company as Google, said it had added more than 200 million predictions to an online database freely available to scientists around the world.
With this new release, the scientists behind DeepMind hope to accelerate research into more obscure organisms and spark a new field called metaproteomics.
“Scientists can now explore this entire database and look for patterns — correlations between species and evolutionary patterns that may not have been apparent until now,” said Demis Hassabis, DeepMind’s chief executive, in a telephone interview.
Proteins start out as arrays of chemical compounds, then twist and fold into three-dimensional shapes that determine how these molecules bind to others. If scientists can determine the shape of a particular protein, they can decipher how it works.
This knowledge is often an essential part of the fight against illness and disease. For example, bacteria resist antibiotics by expressing certain proteins. If scientists can understand how these proteins work, they can begin to fight antibiotic resistance.
Previously, locating the shape of a protein required extensive experiments with X-rays, microscopes and other tools on a lab bench. Now, given the array of chemical compounds that make up a protein, AlphaFold can predict its shape.
The technology is not perfect. But it can predict the shape of a protein with an accuracy that rivals physical experiments about 63 percent of the time, according to independent benchmark tests. With a prediction in hand, scientists can verify its accuracy relatively quickly.
Kliment Verba, a researcher at the University of California, San Francisco, who is using the technology to understand the coronavirus and prepare for similar pandemics, said the technology had “charged” this work, often saving months of experimentation time. . Others have used the tool in their fight to fight gastroenteritis, malaria and Parkinson’s disease.
The technology has also accelerated research outside the human body, including an effort to improve honeybee health. DeepMind’s extensive database can help an even larger community of scientists reap similar benefits.
Just like dr. Hassabis believes Dr. Verba that the database will provide new ways to understand how proteins behave between species. He also sees it as a way to train a new generation of scientists. Not all researchers are versed in this kind of structural biology; a database of all known proteins lowers the entry barrier. “It can bring structural biology to the masses,” said Dr. Verba.
