Maximizing Immunopeptidomics-Based Bacterial Epitope Discovery by Multiple Search Engines and Rescoring

Mass spectrometry-based
discovery of bacterial immunopeptides presented
by infected cells allows untargeted discovery of bacterial antigens
that can serve as vaccine candidates. However, reliable identification
of bacterial epitopes is challenged by their extremely low abundance.
Here, we describe an optimized bioinformatic framework to enhance
the confident identification of bacterial immunopeptides. Immunopeptidomics
data of cell cultures infected with Listeria monocytogenes were searched by four different search engines, PEAKS, Comet, Sage
and MSFragger, followed by data-driven rescoring with MS2Rescore. Compared with individual search engine results, this integrated
workflow boosted immunopeptide identification by an average of 27%
and led to the high-confidence detection of 18 additional bacterial
peptides (+27%) matching 15 different Listeria proteins
(+36%). Despite the strong agreement between the search engines, a
small number of spectra (<1%) had ambiguous matches to multiple
peptides and were excluded to ensure high-confidence identifications.
Finally, we demonstrate our workflow with sensitive timsTOF SCP data
acquisition and find that rescoring, now with inclusion of ion mobility
features, identifies 76% more peptides compared to Q Exactive HF acquisition.
Together, our results demonstrate how integration of multiple search
engine results along with data-driven rescoring maximizes immunopeptide
identification, boosting the detection of high-confidence bacterial
epitopes for vaccine development.

基于质谱技术（mass spectrometry）的受感染细胞呈递细菌免疫肽段的研究，可实现对可作为疫苗候选靶点的细菌抗原的非靶向发掘。然而，细菌表位（epitope）丰度极低，其可靠鉴定仍面临极大挑战。本研究构建了一套优化的生物信息学框架，以提升细菌免疫肽段的高可信度鉴定效率。我们对单核细胞增生李斯特菌（Listeria monocytogenes）感染的细胞培养物开展免疫肽组学（immunopeptidomics）分析，采用PEAKS、Comet、Sage及MSFragger四款不同的搜索引擎进行数据库检索，随后结合MS2Rescore进行数据驱动重打分。相较于单一搜索引擎的鉴定结果，该整合分析流程平均提升了27%的免疫肽段鉴定数量，额外高可信度检测到18条匹配15种不同李斯特菌蛋白的细菌肽段（增幅达36%）。尽管各搜索引擎间的鉴定结果一致性较高，但仍有少量质谱谱图（spectra，<1%）存在与多条肽段匹配的歧义情况，我们将其剔除以确保鉴定结果的高可信度。最后，我们利用搭载离子迁移率特性的高灵敏度timsTOF SCP质谱采集的数据验证该流程，发现相较于Q Exactive HF质谱平台的采集数据，引入离子迁移率特征的重打分步骤可多鉴定出76%的肽段。综上，本研究结果证实，整合多搜索引擎的鉴定结果并结合数据驱动重打分策略，可最大化免疫肽段的鉴定效能，进而提升可用于疫苗开发的高可信度细菌表位的检测数量。