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探索免疫肽组学的动态格局:揭示翻译后修饰并跨越生物信息学领域。

Exploring the dynamic landscape of immunopeptidomics: Unravelling posttranslational modifications and navigating bioinformatics terrain.

作者信息

Flender Daniel, Vilenne Frédérique, Adams Charlotte, Boonen Kurt, Valkenborg Dirk, Baggerman Geert

机构信息

Centre for Proteomics, University of Antwerp, Antwerpen, Belgium.

Health Unit, VITO, Mol, Belgium.

出版信息

Mass Spectrom Rev. 2025 Jul-Aug;44(4):599-629. doi: 10.1002/mas.21905. Epub 2024 Aug 16.

DOI:10.1002/mas.21905
PMID:39152539
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12147530/
Abstract

Immunopeptidomics is becoming an increasingly important field of study. The capability to identify immunopeptides with pivotal roles in the human immune system is essential to shift the current curative medicine towards personalized medicine. Throughout the years, the field has matured, giving insight into the current pitfalls. Nowadays, it is commonly accepted that generalizing shotgun proteomics workflows is malpractice because immunopeptidomics faces numerous challenges. While many of these difficulties have been addressed, the road towards the ideal workflow remains complicated. Although the presence of Posttranslational modifications (PTMs) in the immunopeptidome has been demonstrated, their identification remains highly challenging despite their significance for immunotherapies. The large number of unpredictable modifications in the immunopeptidome plays a pivotal role in the functionality and these challenges. This review provides a comprehensive overview of the current advancements in immunopeptidomics. We delve into the challenges associated with identifying PTMs within the immunopeptidome, aiming to address the current state of the field.

摘要

免疫肽组学正成为一个日益重要的研究领域。识别在人类免疫系统中起关键作用的免疫肽的能力对于将当前的治疗医学转向个性化医学至关重要。多年来,该领域已经成熟,揭示了当前存在的缺陷。如今,人们普遍认为将鸟枪法蛋白质组学工作流程一概而论是不当做法,因为免疫肽组学面临众多挑战。虽然其中许多困难已经得到解决,但通往理想工作流程的道路仍然复杂。尽管已经证明免疫肽组中存在翻译后修饰(PTM),但尽管它们对免疫疗法具有重要意义,其鉴定仍然极具挑战性。免疫肽组中大量不可预测的修饰在功能以及这些挑战中起着关键作用。本综述全面概述了免疫肽组学的当前进展。我们深入探讨了与在免疫肽组中识别PTM相关的挑战,旨在阐述该领域的当前状况。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dee4/12147530/24dbaaaacd87/MAS-44-599-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dee4/12147530/71630132fb24/MAS-44-599-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dee4/12147530/e0965a1451e5/MAS-44-599-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dee4/12147530/43d8db1c6a4c/MAS-44-599-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dee4/12147530/24dbaaaacd87/MAS-44-599-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dee4/12147530/71630132fb24/MAS-44-599-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dee4/12147530/e0965a1451e5/MAS-44-599-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dee4/12147530/43d8db1c6a4c/MAS-44-599-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dee4/12147530/24dbaaaacd87/MAS-44-599-g001.jpg

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3
Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023.基于 DIA 的蛋白质组学数据的获取与分析:2023 年全面综述。
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4
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5
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7
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Oktoberfest: Open-source spectral library generation and rescoring pipeline based on Prosit.慕尼黑啤酒节:基于 Prosit 的开源光谱库生成和重评分管道。
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