AI predicts the shape of almost every protein known to science

In 2020, an artificial intelligence lab called DeepMind unveiled technology that could predict the shape of proteins – the microscopic mechanisms that control 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. It immediately changed the course of biological research. If scientists can identify the shapes of proteins, they can advance understanding of diseases, develop new medicines, and otherwise unlock the mysteries of life on Earth.

Now DeepMind has published predictions for almost every protein known to science. On Thursday, the London-based lab, owned by the same parent company as Google, announced it had added more than 200 million predictions to an online database freely available to scientists around the world.

With this new version, the scientists behind DeepMind hope to accelerate the study of more obscure organisms and spark a new field called metaproteomics.

“Scientists can now search this entire database looking for patterns — correlations between species and evolutionary patterns that may not previously have been obvious,” Demis Hassabis, DeepMind’s chief executive officer, said in a phone interview.

Proteins begin as chains of chemical bonds and then twist and fold into three-dimensional shapes that define how those 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 disease and infirmity. For example, bacteria resist antibiotics by expressing specific proteins. By understanding how these proteins work, scientists can begin to counteract antibiotic resistance.

In the past, accurately determining a protein’s shape required extensive experiments using X-rays, microscopes, and other tools on a laboratory bench. Given the array of chemical compounds that make up a protein, AlphaFold can now predict its shape.

The technology is not perfect. However, according to independent benchmark tests, it can predict the shape of a protein with an accuracy that rivals physical experiments about 63 percent of the time. With a prediction in hand, Scientology 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 has “supercharged” that work, often saving months of experimentation time. Others have used the tool in the fight against gastroenteritis, malaria and Parkinson’s disease.

Technology has also accelerated research beyond the human body, including efforts to improve honey bee health. DeepMind’s expanded database can help an even larger community of scientists reap similar benefits.

like dr Hassabis is also Dr. Verba believes that the database will provide new ways to understand how proteins behave between different species. He also sees it as an opportunity to train a new generation of scientists. Not all researchers are familiar with this type of structural biology; A database of all known proteins lowers the entry barrier. “It can educate the masses about structural biology,” said Dr. verbal

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