Google researchers have developed a method that speeds up protein structure prediction by 8.6 times, potentially accelerating the discovery of molecules for targeted cancer therapies.
The researchers used the approach to design proteins that bind tightly to fibroblast activation protein alpha (FAP), a molecule associated with solid tumors. Such binders could eventually help researchers develop treatments that target cancer-associated tissue more precisely. The results were obtained through laboratory experiments after computationally designing candidate molecules.
The work is part of three Google Research projects exploring how artificial intelligence agents can design biological molecules, analyze experimental results and work with laboratory equipment. The projects also demonstrated improvements in DNA molecule discovery and robotic experimentation.
Google said the research aims to reduce the time and computational resources required to test biological designs. However, the systems remain experimental, and the protein binders have not been demonstrated as cancer treatments.
Designing a protein that attaches to a specific biological target requires researchers to predict how the molecules fold and interact. This involves generating candidate protein structures, designing sequences, and checking whether the resulting molecules are likely to bind.
The final prediction stage can consume substantial computing power, particularly when the target protein is large.
Google researchers developed a technique called “surgical epitope excision,” which focuses structural predictions on the local region where a designed protein is expected to attach, rather than processing the entire target.
According to Google, the approach accelerated structure prediction by 8.6 times without sacrificing structural accuracy in its tests.
The researchers applied the method to FAP, using yeast surface display and fluorescence-activated cell sorting to screen engineered protein candidates.
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