In 2020, an artificial intelligence lab called DeepMind unveiled technology that could 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 researchers 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 can accelerate the ability to understand diseases, create new medicines and otherwise investigate the mysteries of life on Earth.
Now DeepMind has released predictions for almost 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 accessible to researchers worldwide.
With this new release, the researchers behind DeepMind hope to accelerate research into more obscure organisms and trigger 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,” Demis Hassabis, CEO of DeepMind, said in a phone interview.
Proteins begin as strings of chemical compounds, then twist and fold into three-dimensional shapes that define how these molecules bind to others. If scientists can find the shape of a particular protein, they can decipher how it works.
This knowledge is often an important 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 combat antibiotic resistance.
In the past, finding the shape of a protein required extensive experimentation with X-rays, microscopes and other tools on a lab bench. Now, given the string 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, researchers 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 “supercharged” this work, often saving months of experimentation time. Others have used the tool while struggling to fight gastroenteritis, malaria and Parkinson’s disease.
The technology has also accelerated research beyond the human body, including an effort to improve the health of honeybees. DeepMind’s expanded database could help an even larger community of researchers reap similar benefits.
Like Dr. Hassabis, Dr. Verba believes the database will provide new ways of understanding how proteins behave across species. He also sees it as a way to educate a new generation of researchers. Not all researchers are familiar with this type of structural biology; a database of all known proteins lowers the entry limit. “It could bring structural biology to the masses,” Dr. Verba said.