Proteins underpin most biological function, and the ability to design them with tailored structures and properties is central to advances in biotechnology. Diffusion-based generative models have emerged as powerful tools for protein design, but steering them toward proteins with specified properties remains challenging. The Feynman-Kac (FK) framework provides a principled way to guide diffusion models using user-defined rewards. In this paper, we enable FK-based steering of RFdiffusion through the development of guiding potentials that leverage ProteinMPNN and structural relaxation to guide the diffusion process towards desired properties. We show that steering can be used to consistently improve predicted interface energetics and increase binder designability by $89.5\%$. Together, these results establish that diffusion-based protein design can be effectively steered toward arbitrary, non-differentiable objectives, providing a model-independent framework for controllable protein generation.

翻译：蛋白质是绝大多数生物功能的基础，而设计具有特定结构与性质的蛋白质的能力是生物技术发展的核心。基于扩散的生成模型已成为蛋白质设计的强大工具，但如何引导这些模型生成具有指定性质的蛋白质仍具挑战性。费曼-卡克框架提供了一种利用用户定义奖励来引导扩散模型的原则性方法。本文通过开发引导势能，结合ProteinMPNN与结构弛豫技术，实现了基于费曼-卡克框架对RFdiffusion的引导，从而将扩散过程导向所需性质。研究表明，该引导方法能够显著提升预测的界面能量特征，并将结合物可设计性提高89.5%。这些结果共同证实：基于扩散的蛋白质设计可被有效引导至任意不可微目标，为可控蛋白质生成提供了模型无关的通用框架。