MHC-I peptides are a group of important immunopeptides presented by major histocompatibility complex (MHC) on the cell surface for immune recognition. The majority of reported MHC-I peptides are derived from protein coding sequences, and noncanonical peptides translated from small open reading frames (sORF) are largely unknown due to the lack of accurate and sensitive detection methods. Herein we report an efficient approach that implements complementary bioinformatic strategies to improve the identification of noncanonical MHC-I peptides. In a database search strategy, noncanonical immunopeptides mapping was optimized by combining three complementary pipelines to construct predicted sORF databases from Ribo-seq data. In a de novo peptide sequencing strategy, MS data search results were filtered against sORF databases to pin down additional noncanonical immunopeptides. In total, 308 noncanonical immunopeptides were identified from two tumor cell lines with selected ones vigorously validated. Our approach is a handy solution to identify noncanonical MHC peptides with Ribo-seq and MS data. Meanwhile, the novel noncanonical immunopeptides identified with this method could shed insights on fundamental immunology as well as cancer immunotherapies.

中文翻译：

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一种发现由小型开放阅读框编码的非规范 MHC-I 肽的综合方法。

MHC-I肽是一组重要的免疫肽，由主要组织相容性复合物（MHC）呈递在细胞表面用于免疫识别。大多数报道的 MHC-I 肽来源于蛋白质编码序列，由于缺乏准确和灵敏的检测方法，从小开放阅读框 (sORF) 翻译的非规范肽在很大程度上是未知的。在这里，我们报告了一种有效的方法，该方法实施了互补的生物信息学策略，以改进非经典 MHC-I 肽的鉴定。在数据库搜索策略中，通过结合三个互补的管道来优化非经典免疫肽图谱，以从 Ribo-seq 数据构建预测的 sORF 数据库。在从头肽测序策略中，MS 数据搜索结果针对 sORF 数据库进行过滤，以确定其他非经典免疫肽。总共从两个肿瘤细胞系中鉴定出 308 种非经典免疫肽，其中选定的免疫肽得到了大力验证。我们的方法是使用 Ribo-seq 和 MS 数据识别非规范 MHC 肽的便捷解决方案。同时，用这种方法鉴定的新型非经典免疫肽可以为基础免疫学和癌症免疫疗法提供见解。