Scientists are using AI to dream up revolutionary new proteins
Published in Tools.
In June, South Korean regulators authorized the first-ever medicine, a COVID-19 vaccine, to be made from a novel protein designed by humans. The vaccine is based on a spherical protein ‘nanoparticle’ that was created by researchers nearly a decade ago, through a labour-intensive trial-and error-process1.
Now, thanks to gargantuan advances in artificial intelligence (AI), a team led by David Baker, a biochemist at the University of Washington (UW) in Seattle, reports in Science that it can design such molecules in seconds instead of months.
Such efforts are a part of a scientific sea change, as AI tools such as DeepMind’s protein-structure-prediction software AlphaFold are embraced by life scientists. In July, DeepMind revealed that the latest version of AlphaFold had predicted structures for every protein known to science. And recent months have seen an explosive growth in AI tools — some based on AlphaFold — that can quickly dream up completely new proteins. Previously, this had been a painstaking pursuit with high failure rates.
“Since AlphaFold, there’s been a shift in the way we work with protein design,” says Noelia Ferruz, a computational biologist at the University of Girona, Spain. “We are witnessing very exciting times.”
Most efforts are focused on tools that can help to make original proteins, shaped unlike anything in nature, without much focus on what these molecules can do. But researchers — and a growing number of companies that are applying AI to protein design — would like to design proteins that can do useful things, from cleaning up toxic waste to treating diseases. Among the companies that are working towards this goal are DeepMind in London and Meta (formerly Facebook) in Menlo Park, California.
“The methods are already really powerful. They’re going to get more powerful,” says Baker. “The question is what problems are you going to solve with them.”
Baker’s laboratory has spent the past three decades making new proteins. Software called Rosetta, which his lab started developing in the 1990s, splits the process into steps. Initially, researchers conceived a shape for a novel protein — often by cobbling together bits of other proteins — and the software deduced a sequence of amino acids that corresponded to this shape.
But these ‘first draft’ proteins rarely folded into the desired shape when made in the lab, and instead ended up stuck in different confirmations. So another step was needed to tweak the protein sequence such that it folded only into a single desired structure. This step, which involved simulating all the ways in which different sequences might fold, was computationally expensive, says Sergey Ovchinnikov, an evolutionary biologist at Harvard University in Cambridge, Massachusetts, who used to work in Baker’s lab. “You would literally have, like, 10,000 computers running for weeks doing this.”