A 16-billion-parameter model, Pinal, created working proteins from text descriptions, including an enzyme and a protein for capturing CO2
Prepared by Sintelligent with the help of SI. Published in SI: . Updated: .
Proteins can now be "ordered" in words and they already work in the lab; this makes it easier to create enzymes for industry.
According to the abstract of the Nature paper, Pinal designs functional proteins from natural-language descriptions: it first generates a structure from the text, then selects a sequence. The model was applied to four tasks — a fluorescent protein, a PET hydrolase, an alcohol dehydrogenase and the H-protein of a metabolic pathway — and in each case the intended function was observed experimentally; catalytic activity was confirmed for both enzymes. Without iterative experimental optimisation, the designed H-protein raised product yield in a multi-enzyme CO2-fixation pathway 1.7-fold compared with the native E. coli protein. Training used 1.7 billion synthetically annotated protein–text pairs.
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