标题： 使用扩散模型生成结构引导的pMHC-I文库 显示英文标题

标题： Generation of structure-guided pMHC-I libraries using Diffusion Models

摘要： 个性化疫苗和T细胞免疫疗法关键在于识别能够引发强大免疫反应的肽-MHC I类（pMHC-I）相互作用。 然而，当前的基准和模型继承了质谱和结合实验数据集中的偏差，限制了新肽配体的发现。 为了解决这个问题，我们引入了一个基于结构的pMHC-I肽基准，该基准是通过在晶体结构相互作用距离条件下使用扩散模型设计的。 涵盖二十个高优先级的HLA等位基因，这个基准独立于之前表征的肽，但能再现经典的锚定残基偏好，表明在没有实验数据集偏差的情况下具有结构泛化能力。 利用这一资源，我们证明最先进的基于序列的预测器在识别这些结构稳定的构型的结合潜力方面表现不佳，表明在传统评估中不可见的等位基因特异性限制。 我们的几何感知设计流程生成的肽具有较高的预测结构完整性和比现有数据集更高的残基多样性，代表了一个无偏模型训练和评估的关键资源。 我们的代码和数据可在以下地址获取：https://github.com/sermare/struct-mhc-dev. 显示英文摘要

摘要： Personalized vaccines and T-cell immunotherapies depend critically on identifying peptide-MHC class I (pMHC-I) interactions capable of eliciting potent immune responses. However, current benchmarks and models inherit biases present in mass-spectrometry and binding-assay datasets, limiting discovery of novel peptide ligands. To address this issue, we introduce a structure-guided benchmark of pMHC-I peptides designed using diffusion models conditioned on crystal structure interaction distances. Spanning twenty high-priority HLA alleles, this benchmark is independent of previously characterized peptides yet reproduces canonical anchor residue preferences, indicating structural generalization without experimental dataset bias. Using this resource, we demonstrate that state-of-the-art sequence-based predictors perform poorly at recognizing the binding potential of these structurally stable designs, indicating allele-specific limitations invisible in conventional evaluations. Our geometry-aware design pipeline yields peptides with high predicted structural integrity and higher residue diversity than existing datasets, representing a key resource for unbiased model training and evaluation. Our code, and data are available at: https://github.com/sermare/struct-mhc-dev.