Artificial intelligence has deciphered the structure of nearly every protein known to science, paving the way for the development of new drugs or technologies to address global challenges such as hunger or pollution.
Proteins are the building blocks of life. Formed by chains of amino acids folded into complex shapes, their 3D structure largely determines their function. Once you understand how a protein folds, you can begin to understand how it works and how to change its behavior. Although DNA provides the instructions for creating the chain of amino acids, predicting how they interact to form a 3D shape has been more difficult, and until recently scientists had only deciphered a fraction of the 200 million or so proteins known to science.
In November 2020, the artificial intelligence group DeepMind announced that it had developed a program called AlphaFold that could quickly predict this information using an algorithm. Since then, he has searched 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 for 20 species—including nearly all of the 20,000 proteins expressed by humans—in an open database. Now he has finished the work and released predicted structures for more than 200 million proteins.
“Essentially, you can think of it as covering the entire protein universe. It includes predictable structures for plants, bacteria, animals and many other organisms, opening up huge new opportunities for AlphaFold to impact 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 Prof Matthew Higgins from the University of Oxford announced that they had used the AlphaFold models to help determine the structure of a key malaria parasite protein and understand where antibodies that could block it were likely to bind transmission of the parasite.
“Previously we used a technique called protein crystallography to understand what this molecule looks like, but because it’s quite dynamic and moving around, we just couldn’t get a handle on it,” Higgins said. “When we took the AlphaFold models and combined them with this experimental evidence, suddenly everything made sense. This insight will now be used to design improved vaccines that induce the most potent transmission-blocking antibodies.”
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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 can be modified to digest and recycle plastics. “It took us a long time to sift through this huge database of structures, but we’ve opened up this whole range of new three-dimensional shapes that we’ve never seen before that could actually degrade plastics,” said Prof John McGeehan, who led the job. “There is a complete paradigm shift. We can really accelerate from here – and that helps us focus those precious resources on the things that matter.”
Professor Dame Janet Thornton, group leader and senior scientist at the European Bioinformatics Institute at the European Molecular Biology Laboratory, said: “AlphaFold protein structure predictions are already being used in countless ways. I expect that this latest update will trigger an avalanche of new and exciting discoveries in the coming months and years, all thanks to the fact that the data is openly available to all.”
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