DeepMind discovers the structure of 200 million proteins in a scientific leap forward

Artificial intelligence has deciphered the structure of virtually every protein known to science, paving the way for the development of new drugs or technologies to address global challenges such as famine or pollution.

Proteins are the building blocks of life. Made up of chains of amino acids, folded into complex shapes, their 3D structure largely determines their function. Once you know how a protein folds, you can begin to understand how it works and how to modify its behavior. Although DNA provides the instructions for making the chain of amino acids, predicting how they interact to form a 3D shape was trickier, and until recently scientists had only deciphered a fraction of the roughly 200 million proteins known to science.

In November 2020, the artificial intelligence group DeepMind announced that it had developed a program called AlphaFold capable of quickly predicting this information using an algorithm. Since then, he has analyzed the genetic codes of every organism whose genome has been sequenced and predicted the structures of the hundreds of millions of proteins they collectively contain.

Last year, DeepMind published the protein structures of 20 species – including nearly all of the 20,000 human-expressed proteins – on an open database. Now he has completed the work and published predicted structures for over 200 million proteins.

“Essentially, you can think of it as spanning the entire protein universe. It includes predictive structures for plants, bacteria, animals, and many other organisms, opening up huge new opportunities for AlphaFold to have impact on important issues, such as sustainability, food insecurity and neglected diseases,” said Demis Hassabis, DeepMind founder and CEO.

Scientists are already using some of his earlier predictions to help develop new drugs. In May, researchers led by Professor Matthew Higgins from the University of Oxford announced that they had used AlphaFold’s models to determine the structure of a key malaria parasite protein and determine where antibodies likely to block transmission of the parasite were likely to bind.

“We used to use a technique called protein crystallography to figure out what this molecule looks like, but because it’s quite dynamic and moving around, we just couldn’t get it under control,” Higgins said. “When we took the AlphaFold models and combined them with this experimental evidence, it suddenly all made sense. This idea will now be used to design improved vaccines that induce the most potent transmission-blocking antibodies.”

AlphaFold’s models are also being used by scientists at the University of Portsmouth’s Center for Enzyme Innovation, to identify enzymes from the natural world that could be modified to digest and recycle plastics. “It took us a long time to sift through this huge database of structures, but we opened up a whole range of new three-dimensional shapes that we had never seen before that could actually break down plastics,” said the professor. John McGeehan, who directs the work. “There is a complete paradigm shift. We can really speed up where we’re going from here – and it helps direct those precious resources to the things that matter.

Professor Dame Janet Thornton, Group Leader and Senior Scientist at the European Institute of Bioinformatics at the European Molecular Biology Laboratory, said: “AlphaFold protein structure predictions are already being used in multiple ways. I expect this latest update to trigger an avalanche of new and exciting discoveries in the months and years to come, and all thanks to the fact that the data is openly available and usable by everyone.

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