蛋白质组学
细胞培养中氨基酸的稳定同位素标记
工作流程
定量蛋白质组学
计算机科学
计算生物学
磷酸蛋白质组学
脚本语言
样品(材料)
补语(音乐)
数据科学
生物
化学
蛋白质磷酸化
细胞生物学
数据库
生物化学
磷酸化
色谱法
程序设计语言
蛋白激酶A
基因
表型
互补
作者
Armel Nicolas,Dalila Bensaddek,Angus I. Lamond
出处
期刊:Methods in molecular biology
日期:2016-01-01
卷期号:: 263-276
标识
DOI:10.1007/978-1-4939-3792-9_21
摘要
With recent advances in experiment design, sample preparation, separation and instruments, mass spectrometry (MS)-based quantitative proteomics is becoming increasingly more popular. This has the potential to usher a new revolution in biology, in which the protein complement of cell populations can be described not only with increasing coverage, but also in all of its dimensions with unprecedented precision. Indeed, while earlier proteomics studies aimed solely at identifying as many as possible of the proteins present in the sample, newer, so-called Next Generation Proteomics studies add to this the aim of determining and quantifying the protein variants present in the sample, their mutual associations within complexes, their posttranslational modifications, their variation across the cell-cycle or in response to stimuli or perturbations, and their subcellular distribution. This has the potential to make MS proteomics much more useful for researchers, but will also mean that researchers with no background in MS will increasingly be confronted with the less-than trivial challenges of preparing samples for MS analysis, then processing and interpreting the results. In Chapter 20 , we described a workflow for isolating the protein contents of a specific SILAC-labeled organelle sample (the nucleolus) and processing it into peptides suitable for bottom-up MS analysis. Here, we complete this workflow by describing how to use the freely available MaxQuant software to convert the spectra stored in the Raw files into peptide- and protein-level information. We also briefly describe how to visualize the data using the free R scripting language.
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