液相色谱耦合质谱(LC-MS/MS)是获得疾病或患者特异性人类白细胞抗原(HLA)呈递的直接证据的主要方法。然而,与蛋白质组学中胰蛋白酶肽的分析相比,HLA肽的分析仍然存在计算和统计学挑战.最近,基于碎片离子强度的匹配分数评估预测和观察到的光谱之间的相似性被证明大大增加了确定的肽的数量,特别是在分析非胰蛋白酶肽的用例中。在这一章中,我们详细描述了如何从最先进的深度学习模型中受益来分析和验证单个光谱的三个过程,单次测量,以及基于质谱的免疫感受态学中的多种测量。为此,我们解释如何使用通用频谱浏览器(USE),网上啤酒节,离线啤酒节。对于基于强度的评分,啤酒节使用来自深度学习框架Prosit的碎片离子强度和保留时间预测,在ProteomeTools项目中生成的大量合成肽和串联质谱上训练的深度神经网络。显示的示例突出了深度学习辅助分析如何增加已识别的HLA肽的数量,促进自信地发现新表位,或提供协助评估隐藏肽的存在,如剪接的肽。
Liquid chromatography-coupled mass spectrometry (LC-MS/MS) is the primary method to obtain direct evidence for the presentation of disease- or patient-specific human leukocyte antigen (HLA). However, compared to the analysis of tryptic peptides in proteomics, the analysis of HLA peptides still poses computational and statistical challenges. Recently, fragment ion intensity-based matching scores assessing the similarity between predicted and observed spectra were shown to substantially increase the number of confidently identified peptides, particularly in use cases where non-tryptic peptides are analyzed. In this chapter, we describe in detail three procedures on how to benefit from state-of-the-art deep learning models to analyze and validate single spectra, single measurements, and multiple measurements in mass spectrometry-based immunopeptidomics. For this, we explain how to use the Universal Spectrum Explorer (USE), online Oktoberfest, and offline Oktoberfest. For intensity-based scoring, Oktoberfest uses fragment ion intensity and retention time predictions from the deep learning framework Prosit, a deep neural network trained on a very large number of synthetic peptides and tandem mass spectra generated within the ProteomeTools project. The examples shown highlight how deep learning-assisted analysis can increase the number of identified HLA peptides, facilitate the discovery of confidently identified neo-epitopes, or provide assistance in the assessment of the presence of cryptic peptides, such as spliced peptides.