Google’s deep-learning program for determining the 3D shapes of proteins is set to transform biology.
In a pivotal breakthrough, an artificial intelligence network developed by Google AI offshoot DeepMind has made a gigantic leap in solving one of biology’s grandest challenges — determining a protein’s 3D shape from its amino-acid sequence.
DeepMind’s program, AlphaFold, outperformed around 100 other teams in a biennial protein-structure prediction challenge known as CASP, the Critical Assessment of Structure Prediction.
The ability to accurately predict protein structures from their amino-acid sequence would be a huge boon to life sciences and medicine. It would vastly accelerate efforts to understand the building blocks of cells and enable quicker and more advanced drug discovery.
Professor John Moult, Co-Founder and Chair of CASP, University of Maryland said: “We have been stuck on this one problem – how do proteins fold up – for nearly 50 years. To see DeepMind produce a solution for this, having worked personally on this problem for so long and after so many stops and starts wondering if we’d ever get there, is a very special moment.”
DeepMind said it had started work with a handful of scientific groups and would focus initially on malaria, sleeping sickness and leishmaniasis, a parasitic disease.
“It marks an exciting moment for the field,” said Demis Hassabis, DeepMind’s founder and chief executive. “These algorithms are now becoming mature enough and powerful enough to be applicable to really challenging scientific problems.”
Venki Ramakrishnan, the president of the Royal Society, called the work “a stunning advance” that had occurred “decades before many people in the field would have predicted”.
“It’s a game changer,” agreed Andrei Lupas, an evolutionary biologist at the Max Planck Institute for Developmental Biology in Germany, who assessed the performance of different teams in CASP. AlphaFold has already helped him find the structure of a protein that has vexed his lab for a decade, and he expects it will alter how he works and the questions he tackles. “This will change medicine,” he added. “It will change research. It will change bioengineering. It will change everything.”