Generative design and validation of therapeutic peptides for glioblastoma based on a potential target ATP5A

TL;DR

This paper introduces POTFlow, a novel generative model that designs therapeutic peptides targeting ATP5A for glioblastoma, demonstrating effective inhibition and survival benefits in experimental models.

Contribution

The study presents the first lead-conditioned flow-based generative model for peptide design, integrating structural constraints and optimal transport for improved exploration and optimization.

Findings

POTFlow outperforms five popular peptide design methods.

Generated peptides inhibit glioblastoma cell viability.

Peptides significantly prolong survival in PDX models.

Abstract

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.