This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Watermarks have long protected everything from bank notes and fine art to digital photographs and software, helping prove authenticity and trace an object's origin. Their main function is to protect copyright and verify authenticity.
That same traceability is needed in synthetic biology, where AI is now a useful research tool. To address this, Google DeepMind has introduced SynthIDBio, a method for embedding invisible signatures directly into biological sequences and 3D structures without interfering with their function. AI is actively designing new functional proteins and predicting their three-dimensional shapes.
But along with the benefits come a host of potential problems. These include biosecurity risks, such as the potential misuse of AI-designed biological molecules, and the spread of fake or misleading scientific data. The team released details of its protein watermarking in a paper published in the journal Nature.
There are two kinds of watermarks, digital patterns built directly into the protein design data. Which one is used depends on what the AI is generating. If the model is writing a protein sequence, the watermark is a secret code hidden in the order of the amino acids.
If the model is predicting a 3D biomolecular structure, the watermark is an invisible pattern hidden in the 3D coordinates of the atoms. Then came the jump from computer models to the real world. Researchers made watermarked protein binders in the lab to target three specific molecules.
These included part of the coronavirus spike protein, a human protein involved in blood vessel growth and a human protein involved in regulating immune responses. They then needed to find out two things. The first was whether the watermarks changed how the proteins work, and the second was whether a detector could spot them.
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