Therapeutic peptides show promise in targeting previously undruggable binding sites, with recent advancements in deep generative models enabling full-atom peptide co-design for specific protein receptors. However, the critical role of molecular surfaces in protein-protein interactions (PPIs) has been underexplored. To bridge this gap, we propose an omni-design peptides generation paradigm, called SurfFlow, a novel surface-based generative algorithm that enables comprehensive co-design of sequence, structure, and surface for peptides. SurfFlow employs a multi-modality conditional flow matching (CFM) architecture to learn distributions of surface geometries and biochemical properties, enhancing peptide binding accuracy. Evaluated on the comprehensive PepMerge benchmark, SurfFlow consistently outperforms full-atom baselines across all metrics. These results highlight the advantages of considering molecular surfaces in de novo peptide discovery and demonstrate the potential of integrating multiple protein modalities for more effective therapeutic peptide discovery.

翻译：治疗性肽在靶向以往难以成药的结合位点方面展现出巨大潜力，随着深度生成模型的最新进展，现已能够针对特定蛋白质受体进行全原子肽的协同设计。然而，分子表面在蛋白质-蛋白质相互作用中的关键作用尚未得到充分探索。为弥补这一差距，我们提出了一种全设计肽生成范式，称为 SurfFlow，这是一种新颖的基于表面的生成算法，能够实现对肽的序列、结构和表面的全面协同设计。SurfFlow 采用多模态条件流匹配架构，以学习表面几何形状和生化特性的分布，从而提高肽结合的准确性。在全面的 PepMerge 基准测试中，SurfFlow 在所有指标上均持续优于全原子基线方法。这些结果凸显了在从头肽发现中考虑分子表面的优势，并证明了整合多种蛋白质模态以实现更有效治疗性肽发现的潜力。