Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, United States.
Methods Mol Biol. 2021;2259:297-308. doi: 10.1007/978-1-0716-1178-4_19.
Shotgun proteomics is the inferential analysis of proteoforms using peptide proxies produced by enzyme-catalyzed hydrolysis of entire proteomes. Such peptides are usually identified by nanoflow liquid chromatography coupled to tandem mass spectrometry analysis (nLC-MS/MS). Traditionally, MS/MS analysis is performed in data-dependent acquisition (DDA) mode, which usually produces a pattern of fragment masses unique to a single peptide's fragmentation. Here, I describe a statistically rigorous qualitative and quantitative computational analysis for shotgun proteomics DDA analysis using free open-source software tools. MS/MS data are used to identify peptides, and the area of peptide mass/charge over chromatographic elution is used to quantify peptides. All peptides that uniquely map to a protein sequence predicted from the genome are combined into a single protein quantity, which can then be compared across experimental conditions. Statistically significant protein changes can be summarized using gene ontology or pathway term enrichment analysis.
Shotgun 蛋白质组学是使用酶促水解整个蛋白质组产生的肽代理物对蛋白质形式进行推断性分析。此类肽通常通过纳流液相色谱与串联质谱分析（nLC-MS/MS）进行鉴定。传统上，MS/MS 分析是在数据依赖型采集（DDA）模式下进行的，该模式通常会产生与单个肽片段唯一对应的片段质量模式。在这里，我使用免费的开源软件工具描述了用于 Shotgun 蛋白质组学 DDA 分析的严格统计学的定性和定量计算分析。MS/MS 数据用于鉴定肽，并且肽质荷比在色谱洗脱过程中的面积用于定量肽。唯一映射到从基因组预测的蛋白质序列的所有肽都组合成单个蛋白质量，然后可以在实验条件之间进行比较。可以使用基因本体论或途径术语富集分析来总结具有统计学意义的蛋白质变化。
