Protein structure is key to understanding protein function and is essential for progress in bioengineering, drug discovery, and molecular biology. Recently, with the incorporation of generative AI, the power and accuracy of computational protein structure prediction/design have been improved significantly. However, ethical concerns such as copyright protection and harmful content generation (biosecurity) pose challenges to the wide implementation of protein generative models. Here, we investigate whether it is possible to embed watermarks into protein generative models and their outputs for copyright authentication and the tracking of generated structures. As a proof of concept, we propose a two-stage method FoldMark as a generalized watermarking strategy for protein generative models. FoldMark first pretrain watermark encoder and decoder, which can minorly adjust protein structures to embed user-specific information and faithfully recover the information from the encoded structure. In the second step, protein generative models are fine-tuned with watermark Low-Rank Adaptation (LoRA) modules to preserve generation quality while learning to generate watermarked structures with high recovery rates. Extensive experiments are conducted on open-source protein structure prediction models (e.g., ESMFold and MultiFlow) and de novo structure design models (e.g., FrameDiff and FoldFlow) and we demonstrate that our method is effective across all these generative models. Meanwhile, our watermarking framework only exerts a negligible impact on the original protein structure quality and is robust under potential post-processing and adaptive attacks.

翻译：蛋白质结构是理解蛋白质功能的关键，对于生物工程、药物发现和分子生物学领域的进展至关重要。近年来，随着生成式人工智能的引入，计算蛋白质结构预测/设计的能力和准确性得到了显著提升。然而，诸如版权保护和有害内容生成（生物安全）等伦理问题对蛋白质生成模型的广泛应用构成了挑战。本文探讨了是否可能将水印嵌入蛋白质生成模型及其输出中，以实现版权认证和生成结构的追踪。作为概念验证，我们提出了一种两阶段方法FoldMark，作为蛋白质生成模型的通用水印策略。FoldMark首先预训练水印编码器和解码器，该组件能够微调蛋白质结构以嵌入用户特定信息，并能够从编码后的结构中准确恢复信息。第二步，通过水印低秩自适应（LoRA）模块对蛋白质生成模型进行微调，以在保持生成质量的同时，学习生成具有高恢复率的水印结构。我们在开源蛋白质结构预测模型（如ESMFold和MultiFlow）和从头结构设计模型（如FrameDiff和FoldFlow）上进行了大量实验，结果表明我们的方法在所有生成模型中均有效。同时，我们的水印框架仅对原始蛋白质结构质量产生可忽略的影响，并且在潜在的后处理和自适应攻击下具有鲁棒性。