A.I. Predicts the shape of nearly every protein known to science

DeepMind has expanded its database of microscopic biological mechanisms, in hopes of accelerating research on all living things.

< p class="css-at9mc1 evys1bk0 ">In 2020, an artificial intelligence lab called DeepMind unveiled technology that can predict the shape of proteins - the microscopic mechanisms that govern the behavior of the human body and all other living things .

A year later, the lab shared the tool, called AlphaFold, with scientists and published predicted shapes for more than 350,000 proteins, including all proteins expressed by the human genome. This immediately changed the course of biological research. If scientists can identify the shapes of proteins, they can accelerate the ability to understand disease, create new medicines, and probe the mysteries of life on Earth.

Now , DeepMind has published predictions for nearly every protein known to science. On Thursday, the London-based lab, 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 launch a new field called metaproteomics.

"Scientists can now explore this whole database and look for patterns - correlations between species and evolutionary patterns that may not have been obvious until now," said Demis Hassabis, the director general of DeepMind, in a phone interview.

Proteins start out as chains of chemical compounds, then twist and bend into three-dimensional shapes that define how these molecules They bond with others. If scientists can identify the shape of a particular protein, they can decipher how it works.

This knowledge is often a vital 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.

In the past, identifying the shape of a protein required extensive experimentation involving x-rays, microscopes and other tools on a lab bench. Now, given the chain of chemical compounds that make up a protein, AlphaFold can predict its shape.

Technology isn't perfect. But it can predict the shape of a protein with an accuracy that rivals physical experiments about 63% 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 uses technology to understand the coronavirus and to prepare for similar pandemics, said technology has "supercharged" this work, often saving months of experimentation time. Others have used the tool as they battle gastroenteritis, malaria and Parkinson's disease.

Technology has also accelerated research at beyond the human body, including an effort to improve bee health. DeepMind's expanded database can help an even broader community of scientists reap similar benefits.

Like Dr Hassabis, Dr Verba believes the database data will provide new ways to understand protein behavior across species. He also sees it as a way to train a new generation of scientists. Not all researchers are versed in this type of structural biology; a database of all known proteins lowers the bar on entry. "It can bring structural biology to the masses," said Dr. Verba.