分层生成蛋白质，精准控制功能特性，效率更高。
ProHiFlo: Hierarchical Flow Matching with Functional Guidance for De Novo Protein Generation
- 分步生成：先构象后原子坐标，降低计算开销。
- 功能引导：用预训练模型指导生成，成功率提升至58.9%。
- 适合蛋白设计者：尤其擅长酶活性位点建模。
从头生成蛋白质在治疗设计、酶工程和合成生物学中具有变革潜力。尽管基于扩散和流匹配的方法已取得进展，但通常仅在单一尺度运行，且缺乏功能约束机制。我们提出ProHiFlo，一种分层流匹配框架，包含三项创新：(1) 从粗到细生成，先建模主链几何再细化为全原子坐标，降低计算成本同时保持精度；(2) 功能引导，利用预训练预测器引导生成以实现目标性质，无需重新训练；(3) 自适应SE(3)等变架构，实现高效多尺度处理。在无条件生成、基序支架构建和功能设计任务上，性能达当前最优，采样步骤减少4步。在酶活性位点支架构建任务中，成功率达58.9%，优于RFDiffusion的41.2%。
原文摘要 · Abstract (English)
De novo protein generation has transformative potential in therapeutic design, enzyme engineering, and synthetic biology. While diffusion-based and flow matching approaches have achieved progress, they typically operate at single resolution and lack mechanisms for incorporating functional constraints. We introduce ProHiFlo, a hierarchical flow matching framework with three innovations: (1) coarse-to-fine generation that models backbone geometry before refining to all-atom coordinates, reducing computational cost while maintaining accuracy; (2) functional guidance leveraging pretrained predictors to steer generation toward desired properties without retraining; (3) adaptive SE(3)-equivariant architecture for efficient multi-scale processing. Experiments on unconditional generation, motif scaffolding, and functional design demonstrate state-ofthe-art performance while requiring 4 fewer sampling steps. On enzyme active site scaffolding, ProHiFlo achieves 58.9% success rate compared to 41.2% for RFDiffusion.
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