用物理启发方法让蛋白生成模型精准设计功能性蛋白。
Controllable protein design with particle-based Feynman-Kac steering
- 基于费曼-卡茨框架，用能量势场引导扩散模型生成目标蛋白。
- 使结合界面能预测提升，设计可成药性提高89.5%。
- 适用于任意不可导目标，适合生物设计与药物开发人员。
蛋白质是大多数生物功能的基础，能够设计出具有特定结构和性质的蛋白质对生物技术发展至关重要。基于扩散的生成模型已成为蛋白质设计的强大工具，但如何引导其生成具备指定属性的蛋白质仍具挑战。费曼-卡茨（Feynman-Kac, FK）框架提供了一种基于用户定义奖励的原理性引导方式。本文通过构建利用ProteinMPNN与结构松弛的引导势场，实现了对RFdiffusion的FK式引导。实验表明，该方法可稳定改善预测的界面能量，并使结合物设计可成药性提升89.5%。结果表明，基于扩散的蛋白质设计可有效引导至任意非可微目标，为可控蛋白质生成提供了模型无关的框架。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。