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

As early as December 2020, DeepMind shocked the biological world Solved 50 years of major challenges with AlphaFold, An artificial intelligence tool for predicting protein structure.Last week, the London-based company released Full details of the tool And released its source code.

Now, the company announced that it has Use its AI to predict the shape of almost all proteins in the human body, And hundreds of thousands of other protein shapes found in the 20 most widely studied organisms, including yeast, fruit flies, and mice. This breakthrough could allow biologists around the world to better understand diseases and develop new drugs.

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

“Protein folding is a problem that I have been focusing on for more than 20 years,” said Demis Hassabis, co-founder of DeepMind. “This is a huge project for us. I want to say that this is the biggest thing we have done so far. In a sense, this is the most exciting, because it should be useful for other than artificial intelligence. The world has the greatest impact.”

Proteins are made up of long bands of amino acids that twist themselves into complex knots. Understanding the shape of protein knots can reveal the role of the protein, which is essential for understanding how diseases work and developing new drugs or identifying organisms that can help fight pollution and climate change.

The database should make life easier for biologists. AlphaFold may be available to researchers, but not everyone wants to run the software themselves. “Getting structures from a database is much easier than running on your own computer,” said David Baker of the Institute of Protein Design at the University of Washington. Rose TTA folding And based on AlphaFold’s method.

In the past few months, Baker’s team has been collaborating with biologists, who have been trying to figure out the shape of the protein they are studying. “There are a lot of really cool biological studies that have really accelerated,” he said. A public database containing hundreds of thousands of ready-made protein shapes should be a bigger accelerator.

“It looks impressive,” said Tom Ellis, a synthetic biologist at Imperial College London who studies yeast genomes, and he is happy to try the database. But he warned that most of the predicted shapes have not yet been verified in the laboratory.

In the new version of AlphaFold, predictions come with a confidence score, which the tool uses to mark how close it thinks each predicted shape is to the real thing. Using this method, DeepMind found that AlphaFold predicts the shape of 36% of human proteins with accuracy to the level of a single atom. Hassabis said this is enough for drug development.

Previously, after decades of research, only 17% of the protein in the human body had its structure determined in the laboratory. If AlphaFold’s prediction is as accurate as DeepMind said, then the tool has more than doubled this number in just a few weeks.

Even predictions that are not completely accurate at the atomic level are still useful. For more than half of the protein in the human body, AlphaFold has predicted a shape that should be enough for researchers to figure out the function of the protein. The rest of AlphaFold’s current predictions are either incorrect or are for one-third of the proteins in the human body that have no structure before they are combined with other proteins. “They are very soft,” Hassabis said.

Mohammed AlQuraish, a systems biologist at Columbia University, who has developed his own protein structure prediction software, said: “It can be applied at this level of quality. This fact is impressive.” He also pointed out that having a large biological body The structure of most proteins will make it possible to study how these proteins work as a system rather than in isolation. “This is what I think is most exciting,” he said.

DeepMind is releasing its tools and forecasts for free and will not state whether there are plans to profit from them in the future. But this possibility is not ruled out. In order to establish and operate the database, DeepMind is cooperating with the European Molecular Biology Laboratory, an international research institution that already has a large protein information database.

For now, AlQuraishi can’t wait to see how the researchers deal with the new data. “It’s magnificent,” he said, “I don’t think any of us thought we would be here so soon. It’s really puzzling.”

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