What it is
Generative AI tools such as AlphaFold 3 and protein design models are accelerating advances in protein structure prediction and in the creation of new functional proteins, and tracking the provenance (origin) of AI-generated protein sequences and structures is becoming increasingly important for challenges that include biosecurity. The authors introduce SynthIDBio, a family of methods that watermark protein sequences and structures to establish the provenance of those generated with AI. SynthIDBio-sequence embeds a watermark into protein sequences while preserving function: watermarked designed protein binders had a binding affinity comparable with their unwatermarked counterparts, and the watermark was detected with near-perfect accuracy.
Why it matters
The authors present the work as a proof of concept that function-preserving biological watermarking is feasible, and as a potential tool for establishing provenance in the rapidly expanding era of AI-driven biological engineering, where the challenges include biosecurity and concerns about information veracity. A second method, SynthIDBio-structure, is a fine-tuned AlphaFold3 model that embeds an imperceptible watermark into biomolecular structures.
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Filed underBiochemical and Structural Characterization, Cell Image Analysis Techniques, Machine Learning in Bioinformatics