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Patterning Bioactive Proteins or Peptides on Hydrogel Using Photochemistry for Biological Applications

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FoldMark: Safeguarding Protein Structure Generative Models with Distributional and Evolutionary Watermarking.

Zaixi Zhang¹, Ruofan Jin¹, Kaidi Fu²

¹Princeton University, Princeton, NJ, USA.

Biorxiv : the Preprint Server for Biology

|November 18, 2024

Summary

This summary is machine-generated.

This study introduces FoldMark, a novel watermarking technique for generative AI models used in protein structure prediction and design. FoldMark embeds invisible copyright information into protein structures, ensuring authenticity and traceability while maintaining structural integrity.

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Area of Science:

Computational Biology
Artificial Intelligence
Biotechnology

Background:

Protein structure is fundamental to function, driving advancements in drug discovery and bioengineering.
Generative AI has significantly enhanced protein structure prediction and design capabilities.
Ethical concerns, including copyright and biosecurity, hinder the widespread adoption of protein generative models.

Purpose of the Study:

To investigate the feasibility of embedding watermarks into protein generative models and their outputs.
To develop a method for copyright authentication and tracking of AI-generated protein structures.
To address ethical challenges associated with protein generative AI.

Main Methods:

Proposed a two-stage watermarking strategy named FoldMark.
Pretrained watermark encoder and decoder to embed and recover user-specific information in protein structures.
Fine-tuned generative models using Low-Rank Adaptation (LoRA) with watermarks as a condition.

Main Results:

FoldMark demonstrated effectiveness across various protein structure prediction (ESMFold, MultiFlow) and design models (FrameDiff, FoldFlow).
The watermarking process had a negligible impact on the quality of the generated protein structures.
The proposed framework proved robust against post-processing and adaptive attacks.

Conclusions:

FoldMark offers a viable solution for copyright protection and traceability in protein generative AI.
The method successfully integrates watermarking without compromising the performance or quality of generative models.
This approach facilitates the responsible development and deployment of advanced protein design technologies.