GoKawiil
Tech News
Google DeepMind researchers in London have built a watermarking system, described in Nature, that embeds a hidden statistical signal into both the amino-acid sequence and 3D structure of AI-designed proteins. The tag, called SynthIDBio, is meant to mark proteins as machine-generated without impairing their biological function, allowing them to be identified later in databases.
Researchers have extended Google's SynthID-text watermarking system to ProteinMPNN, a widely used protein sequence design model, creating a method called SynthIDBio-sequence. The approach embeds a detectable signal during the autoregressive sampling of amino acids, using a random seed, scoring function and tournament sampling algorithm similar to the text version. Because protein design typically uses low-temperature sampling that limits randomness, the team also developed a distortionary variant with an added filter to preserve detectability.
David Baker, who shared the 2024 Nobel Prize in Chemistry for computational protein design, is leading a new initiative called AI BioDesign, backed by the Allen Institute, University of Washington and Fred Hutchinson Cancer Center. The project pairs AI models with large-scale lab experiments to design molecules and biological functions that don't exist in nature but are chemically feasible, aiming to build public databases and tools for future medicines and technologies.