We present PepEDiff, a novel peptide binder generator that designs binding sequences given a target receptor protein sequence and its pocket residues. Peptide binder generation is critical in therapeutic and biochemical applications, yet many existing methods rely heavily on intermediate structure prediction, adding complexity and limiting sequence diversity. Our approach departs from this paradigm by generating binder sequences directly in a continuous latent space derived from a pretrained protein embedding model, without relying on predicted structures, thereby improving structural and sequence diversity. To encourage the model to capture binding-relevant features rather than memorizing known sequences, we perform latent-space exploration and diffusion-based sampling, enabling the generation of peptides beyond the limited distribution of known binders. This zero-shot generative strategy leverages the global protein embedding manifold as a semantic prior, allowing the model to propose novel peptide sequences in previously unseen regions of the protein space. We evaluate PepEDiff on TIGIT, a challenging target with a large, flat protein-protein interaction interface that lacks a druggable pocket. Despite its simplicity, our method outperforms state-of-the-art approaches across benchmark tests and in the TIGIT case study, demonstrating its potential as a general, structure-free framework for zero-shot peptide binder design. The code for this research is available at GitHub: https://github.com/LabJunBMI/PepEDiff-An-Peptide-binder-Embedding-Diffusion-Model

翻译：我们提出PepEDiff，一种新颖的肽段结合剂生成器，能够根据给定的目标受体蛋白序列及其口袋残基设计结合序列。肽段结合剂生成在治疗和生化应用中至关重要，然而许多现有方法严重依赖中间结构预测，增加了复杂性并限制了序列多样性。我们的方法突破了这一范式，直接在源自预训练蛋白质嵌入模型的连续潜在空间中生成结合剂序列，无需依赖预测结构，从而提高了结构和序列多样性。为促使模型捕获结合相关特征而非记忆已知序列，我们进行潜在空间探索和基于扩散的采样，从而能够生成超出已知结合剂有限分布的肽段。这种零样本生成策略利用全局蛋白质嵌入流形作为语义先验，使模型能够在蛋白质空间先前未见区域提出新颖的肽序列。我们在TIGIT上评估PepEDiff，这是一个具有大而平坦的蛋白质-蛋白质相互作用界面且缺乏可成药口袋的挑战性靶点。尽管方法简洁，我们的模型在基准测试和TIGIT案例研究中均优于最先进方法，证明了其作为通用的、无结构零样本肽段结合剂设计框架的潜力。本研究的代码可在GitHub获取：https://github.com/LabJunBMI/PepEDiff-An-Peptide-binder-Embedding-Diffusion-Model