主要组织相容性复合物 (MHC) 分子通过在细胞表面呈递肽以供 T 细胞识别，在免疫系统中发挥着关键作用。肿瘤细胞经常产生带有氨基酸突变的MHC肽，称为新抗原，它可以逃避T细胞识别，导致肿瘤快速生长。在 TCR-T 和 CAR-T 等免疫疗法中，识别这些突变的 MHC 肽序列至关重要。目前基于质谱的肽鉴定方法主要依赖于数据库搜索，无法检测人类数据库中不存在的突变肽。在本文中，我们提出了一种称为 NeoMS 的新颖工作流程，旨在从质谱数据中有效识别非突变和突变的 MHC-I 肽。 NeoMS 利用标记算法生成扩展的序列数据库，其中包括每个样本的潜在突变蛋白质。此外，它对每个肽谱匹配 (PSM) 采用基于机器学习的评分函数，以最大限度地提高搜索灵敏度。最后，采用严格的目标诱饵方法来分别控制有突变和无突变的肽的错误发现率（FDR）。常规肽的实验结果表明 NeoMS 优于四种基准方法。对于突变肽，NeoMS 成功鉴定了黑色素瘤相关样本中的数百种高质量突变肽，其有效性经过进一步研究证实。

Major Histocompatibility Complex (MHC) molecules play a critical role in the immune system by presenting peptides on the cell surface for recognition by T-cells. Tumor cells often produce MHC peptides with amino acid mutations, known as neoantigens, which evade T-cell recognition, leading to rapid tumor growth. In immunotherapies such as TCR-T and CAR-T, identifying these mutated MHC peptide sequences is crucial. Current mass spectrometry-based peptide identification methods primarily rely on database searching, which fails to detect mutated peptides not present in human databases. In this paper, we propose a novel workflow called NeoMS, designed to efficiently identify both non-mutated and mutated MHC-I peptides from mass spectrometry data. NeoMS utilizes a tagging algorithm to generate an expanded sequence database that includes potential mutated proteins for each sample. Furthermore, it employs a machine learning-based scoring function for each peptide-spectrum match (PSM) to maximize search sensitivity. Finally, a rigorous target-decoy approach is implemented to control the false discovery rates (FDR) of the peptides with and without mutations separately. Experimental results for regular peptides demonstrate that NeoMS outperforms four benchmark methods. For mutated peptides, NeoMS successfully identifies hundreds of high-quality mutated peptides in a melanoma-associated sample, with their validity confirmed by further studies.