用生成模型设计靶向脑胶质瘤的治疗肽，实验验证有效。
Generative design and validation of therapeutic peptides for glioblastoma based on a potential target ATP5A
- 基于先验结构信息的流匹配模型，聚焦先导肽附近区域优化。
- 在类器官模型中显著抑制肿瘤细胞存活，延长小鼠生存期。
- 首个先导肽条件下的生成框架，适合药物研发人员参考。
脑胶质瘤（GBM）是最具侵袭性的肿瘤之一，亟需新型治疗策略。本文提出一种从计算到实验的全流程框架，结合生成建模与实验验证，优化靶向ATP5A的治疗肽。该框架引入首个先导肽条件下的生成模型，聚焦于先导肽附近的几何相关区域，缓解从头设计的组合复杂性。具体提出POTFlow模型——一种基于先验与最优传输的流匹配方法，利用二级结构（如α-螺旋、β-折叠、环区）作为几何约束，并通过最优传输进一步缩短生成路径。相比五种主流方法，POTFlow性能达到领先水平。应用于GBM时，所生成肽可选择性抑制细胞活力，并在患者来源异种移植（PDX）模型中显著延长生存期。作为首个先导肽条件的流匹配模型，POTFlow具备通用性，为治疗肽设计提供新范式。
原文摘要 · Abstract (English)
Glioblastoma (GBM) remains the most aggressive tumor, urgently requiring novel therapeutic strategies. Here, we present a dry-to-wet framework combining generative modeling and experimental validation to optimize peptides targeting ATP5A, a potential peptide-binding protein for GBM. Our framework introduces the first lead-conditioned generative model, which focuses exploration on geometrically relevant regions around lead peptides and mitigates the combinatorial complexity of de novo methods. Specifically, we propose POTFlow, a \underline{P}rior and \underline{O}ptimal \underline{T}ransport-based \underline{Flow}-matching model for peptide optimization. POTFlow employs secondary structure information (e.g., helix, sheet, loop) as geometric constraints, which are further refined by optimal transport to produce shorter flow paths. With this design, our method achieves state-of-the-art performance compared with five popular approaches. When applied to GBM, our method generates peptides that selectively inhibit cell viability and significantly prolong survival in a patient-derived xenograft (PDX) model. As the first lead peptide-conditioned flow matching model, POTFlow holds strong potential as a generalizable framework for therapeutic peptide design.
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