Google DeepMind creates watermarked AI-designed proteins that still work in lab tests

Summary

Google DeepMind creates watermarked AI-designed proteins that still work in lab tests SynthID Bio embeds a detectable signature into AI-generated protein sequences and structures, with Nature research showing the watermark can survive without significantly disrupting biological function Google DeepMind researchers have demonstrated a way to watermark AI-designed proteins while preserving their biological function, an attempt to bring the provenance systems already used for AI-generated media into synthetic biology. The new method, SynthID Bio, puts a detectable signature directly into either a protein’s amino acid sequence or its predicted three-dimensional structure. The central challenge is very different from watermarking an AI-generated image. Changing a few pixels may leave a picture effectively unchanged. Altering a protein can change how it folds, binds to other molecules or functions altogether. DeepMind’s researchers had to show not simply that their watermark could be detected, but that the resulting biological designs still worked. In laboratory testing reported in Nature, watermarked protein binders remained functional across three targets: VEGF-A, PD-L1 and the receptor-binding domain of the SARS-CoV-2 spike protein. Pushmeet Kohli, Chief Scientist at Google Cloud and VP Science at Google DeepMind, described the result on LinkedIn as the “successful synthesis of AI-designed proteins that are both functional and watermarked.” How do you watermark a protein? SynthID Bio actually covers two different approaches. For protein sequences, the system subtly changes how amino acids are selected while an AI model generates the sequence. The resulting signature can then be detected using a secret watermarking key. For three-dimensional protein structures, the researchers took a different route. They fine-tuned part of AlphaFold 3 so the watermark is built into the predicted atomic coordinates produced by the model. The recommended version of that structural method achieved a detection rate above 99.8% at a 0.1% false-positive rate in the researchers’ tests, without reducing key structural accuracy measures compared with the AlphaFold 3 baseline. The more important test for the sequence watermark happened outside the computer. Researchers synthesized watermarked and non-watermarked protein binders and tested their ability to attach to the three target prot…