AI-Generated Proteins Get Watermarks, Ensuring Their Origin

A new development from Google DeepMind is set to revolutionize the field of protein design. The company has created a way to watermark proteins generated by artificial intelligence (AI) during the design process itself. This innovation could help scientists track the origin of these structures and prevent their misuse.

In recent years, generative AI models have become an essential tool for researchers designing new protein structures with specific properties or functions. For instance, muscle-inspired proteins that are stronger than their natural counterparts can be created using these tools. However, as more AI-generated protein structures emerge – particularly since Google DeepMind’s AlphaFold won the Nobel prize in chemistry in 2024 – there is a growing need to track their origin.

One of the challenges researchers face when dealing with AI-generated proteins is keeping their findings under wraps if they want to patent them. Creating a centralized database to store these structures could help, but it would also make it difficult for researchers to maintain confidentiality. Adding metadata to describe how a protein was designed can be easily removed, making this solution impractical.

Google DeepMind has taken a different approach by developing models that tag proteins as they are being designed. One of their new models adds a small protein-binding sequence into the target protein sequence, which can then be detected. The researchers compared the sequences of known proteins – such as the Sars-Cov-2 receptor – with the watermarked equivalent and found that watermarking had very little impact on the strength of interactions with other proteins.

The team has also built upon the latest version of AlphaFold to develop a new model that watermarks protein structures themselves. This model revealed that there is little change to bond lengths and angles within the structures, indicating that watermarking does not compromise their integrity. The researchers believe that this innovation will help uphold scientific integrity by making it harder for others to alter the structure of a protein once it has been designed.

The potential misuse of generative AI tools to create biological agents that could cause harm is another concern addressed by this development. By tagging proteins with their origin, scientists can prevent unauthorized modifications and ensure accountability in research.