The identification of major histocompatibility complex (MHC)‐binding peptides in mass spectrometry (MS)‐based immunopeptideomics relies largely on database search engines developed for proteomics data analysis. However, because immunopeptidomics experiments do not involve enzymatic digestion at specific residues, an inflated search space leads to a high false positive rate and low sensitivity in peptide identification. In order to improve the sensitivity and reliability of peptide identification, a post‐processing tool named DeepRescore is developed. DeepRescore combines peptide features derived from deep learning predictions, namely accurate retention timeand MS/MS spectra predictions, with previously used features to rescore peptide‐spectrum matches. Using two public immunopeptidomics datasets, it is shown that rescoring by DeepRescore increases both the sensitivity and reliability of MHC‐binding peptide and neoantigen identifications compared to existing methods. It is also shown that the performance improvement is, to a large extent, driven by the deep learning‐derived features. DeepRescore is developed using NextFlow and Docker and is available at https://github.com/bzhanglab/DeepRescore.

中文翻译：

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DeepRescore：利用深度学习改进免疫肽组学中的肽鉴定。

基于质谱 (MS) 的免疫肽组学中主要组织相容性复合体 (MHC) 结合肽的鉴定很大程度上依赖于为蛋白质组学数据分析而开发的数据库搜索引擎。然而，由于免疫肽组学实验不涉及特定残基的酶消化，搜索空间膨胀导致肽鉴定的假阳性率高、灵敏度低。为了提高肽鉴定的灵敏度和可靠性，开发了一种名为DeepRescore的后处理工具。DeepRescore 将源自深度学习预测的肽特征（即准确的保留时间和 MS/MS 谱图预测）与之前使用的用于重新评分肽谱匹配的特征相结合。使用两个公共免疫肽组学数据集表明，与现有方法相比，DeepRescore 重新评分提高了 MHC 结合肽和新抗原鉴定的灵敏度和可靠性。研究还表明，性能改进在很大程度上是由深度学习衍生的特征驱动的。DeepRescore 使用 NextFlow 和 Docker 开发，可从 https://github.com/bzhanglab/DeepRescore 获取。