Proteins are the building blocks of life. Composed of chains of amino acids, folded into complex shapes, their three-dimensional structure largely determines their function. Once you know how a protein folds, you can begin to understand how it works and how to change its behavior. Although DNA provides the instructions for building the chain of amino acids, predicting how they interact to form a three-dimensional shape has been more difficult and, until recently, scientists had deciphered only a fraction of the 200m proteins that were known to science. In November 2020, the AI team DeepMind announced that it had developed a program called AlphaFold that could quickly predict this information using an algorithm. Since then, it has penetrated 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 20,000 proteins expressed by humans – in an open database. Now he has finished the job and released predicted structures for more than 200 million proteins. “Essentially, you can think of it as covering the entire protein universe. It includes prediction structures for plants, bacteria, animals and many other organisms, opening up huge new opportunities for AlphaFold to make an impact on important issues such as sustainability, food insecurity and neglected diseases,” said Demis Hassabis, DeepMind founder and general director. Scientists are already using some of his earlier predictions to help develop new drugs. In May, researchers led by Professor Matthew Higgins at the University of Oxford announced that they had used AlphaFold’s models to help determine the structure of a key malaria parasite protein and find where antibodies that could block it are likely to bind. transmission of the parasite. “Previously, we used a technique called protein crystallography to determine what this molecule looks like, but because it’s quite dynamic and moving, we just couldn’t deal with it,” Higgins said. “When we took the AlphaFold models and combined them with this experimental evidence, suddenly everything made sense. This knowledge will now be used to design improved vaccines that elicit the most powerful antibodies that prevent transmission.” Subscribe to First Edition, our free daily newsletter – every morning at 7am. BST 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 go through this huge database of structures, but it opened up a whole range of new three-dimensional shapes that we’d never seen before that could break down plastics,” said Professor John McGeehan, the who leads the job. “There is a complete paradigm shift. We can really accelerate where we go from here – and that helps us direct those precious resources to the things that matter.” Professor Dame Janet Thornton, team leader and senior scientist at the European Bioinformatics Institute of the European Molecular Biology Laboratory, said: “AlphaFold protein structure predictions are already being used in a myriad of ways. I expect this latest update to trigger an avalanche of new and exciting discoveries in the coming months and years, all thanks to the fact that the data is available openly for everyone to use.”
