DeepMind has already notched up a streak of wins, showcasing AIs that have learned to play a variety of complex games with superhuman skill, from Go and StarCraft to Atari’s entire back catalogue. But Demis Hassabis, DeepMind’s public face and co-founder, has always stressed that these successes were just stepping stones towards a larger goal: AI that actually helps us understand the world.
Today DeepMind and the organizers of the long-running Critical Assessment of protein Structure Prediction (CASP) competition announced an AI that should have the huge impact that Hassabis has been after. The latest version of DeepMind’s AlphaFold, a deep-learning system that can accurately predict the structure of proteins to within the width of an atom, has cracked one of biology’s grand challenges. “It’s the first use of AI to solve a serious problem,” says John Moult at the University of Maryland, who leads the team that runs CASP.
A protein is made from a ribbon of amino acids that folds itself up with many complex twists and turns and tangles. This structure determines what it does. And figuring out what proteins do is key to understanding the basic mechanisms of life, when it works and when it doesn’t. Efforts to develop vaccines for covid-19 have focused on the virus’s spike protein, for example. The way the coronavirus snags onto human cells depends on the shape of this protein and the shapes of the proteins on the outsides of those cells. The spike is just one protein among billions across all living things; there are tens of thousands of different types of protein inside the human body alone.
In this year’s CASP, AlphaFold predicted the structure of dozens of proteins with a margin of error of just 1.6 angstroms—that’s 0.16 nanometers, or atom-sized. This far outstrips all other computational methods and for the first time matches the accuracy of techniques used in the lab, such as cryo-electron microscopy, nuclear magnetic resonance and x-ray crystallography. These techniques are expensive and slow: it can take hundreds of thousands of dollars and years of trial and error for each protein. AlphaFold can find a protein’s shape in a few days.
Want to publish your own articles on DistilINFO Publications?
Send us an email, we will get in touch with you.
The breakthrough could help researchers design new drugs and understand diseases. In the longer term, predicting protein structure will also help design synthetic proteins, such as enzymes that digest waste or produce biofuels. Researchers are also exploring ways to introduce synthetic proteins that will increase crop yields and make plants more nutritious.
“It’s a very substantial advance,” says Mohammed AlQuraishi, a systems biologist at Columbia University who has developed his own software for predicting protein structure. “It’s something I simply didn’t expect to happen nearly this rapidly. It’s shocking, in a way.”
“This really is a big deal,” says David Baker, head of the Institute for Protein Design at the University of Washington and leader of the team behind Rosetta, a family of protein analysis tools. “It’s an amazing achievement, like what they did with Go.”
Source: Technology Review
