用约束扩散模型精准设计蛋白质，确保结构功能双达标
Constrained Diffusion for Protein Design with Hard Structural Constraints
- 将约束优化与扩散生成结合，实现严格结构约束下的蛋白设计
- 在键角和几何约束上100%满足，且结构多样性不下降
- 适合需要精确结构控制的蛋白工程与药物设计场景
扩散模型为捕捉真实蛋白结构流形提供了强大工具，可快速支持蛋白工程任务的设计。然而，当功能设计需要精确约束时，现有方法存在严重失效问题。为此，我们提出一种结构引导的约束扩散框架，确保在保持立体化学和几何可行性的同时，严格满足功能需求。该方法将邻近可行性更新与ADMM分解引入生成过程，能有效扩展至复杂约束集。我们在具有挑战性的蛋白设计任务上进行评估，包括基序支架设计和空位约束口袋设计，并引入一个针对PDZ域基序支架的新标注基准数据集。所提方法达到当前最优性能，在键合和几何约束上完全满足，且结构多样性未降低。
原文摘要 · Abstract (English)
Diffusion models offer a powerful means of capturing the manifold of realistic protein structures, enabling rapid design for protein engineering tasks. However, existing approaches observe critical failure modes when precise constraints are necessary for functional design. To this end, we present a constrained diffusion framework for structure-guided protein design, ensuring strict adherence to functional requirements while maintaining precise stereochemical and geometric feasibility. The approach integrates proximal feasibility updates with ADMM decomposition into the generative process, scaling effectively to the complex constraint sets of this domain. We evaluate on challenging protein design tasks, including motif scaffolding and vacancy-constrained pocket design, while introducing a novel curated benchmark dataset for motif scaffolding in the PDZ domain. Our approach achieves state-of-the-art, providing perfect satisfaction of bonding and geometric constraints with no degradation in structural diversity.
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