Huge advances in artificial intelligence mean researchers can design completely original molecules in seconds instead of months.
, researchers feed random amino-acid sequences into a structure-prediction network; this alters the structure so that it becomes ever-more protein-like, as judged by the network’s predictions. In a 2021 paper, Baker’s team created more than 100 small, ‘hallucinated’ proteins in the lab and found signs that about one-fifth resembled the predicted shape.AlphaFold, and a similar tool developed by Baker’s lab called RoseTTAFold, were trained to predict the structure of individual protein chains.
When Baker and his team applied this second network to their hallucinated protein nanoparticles, it had much greater success making the molecules experimentally. The researchers determined the structure of 30 of their new proteins using cryo-electron microscopy and other experimental techniques, and 27 of them matched the AI-led designs. The team’s creations included giant rings with complex symmetries, unlike anything found in nature.
Many teams gauge their network’s ability to accurately determine the sequence of an existing protein from its structure. But this doesn’t apply for all methods, and it’s not clear how this metric, known as recovery rate, applies to the design of novel proteins, say scientists.
But not all scientists developing AI tools for protein design have easy access to experimental set-ups, notes Jinbo Xu, a computational biologist at the Toyota Technological Institute at Chicago in Illinois. Finding a lab to collaborate with can take time, so Xu is establishing his own wet lab to put his team’s creations to the test.
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