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基于高通量质谱的蛋白质翻译后修饰分析的计算与统计方法

Computational and Statistical Methods for High-Throughput Mass Spectrometry-Based PTM Analysis.

作者信息

Schwämmle Veit, Vaudel Marc

机构信息

Protein Research Group, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, 5230, Odense, Denmark.

Proteomics Unit, Department of Biomedicine, University of Bergen, Jonas Lies vei 91, 5020, Bergen, Norway.

出版信息

Methods Mol Biol. 2017;1558:437-458. doi: 10.1007/978-1-4939-6783-4_21.

Abstract

Cell signaling and functions heavily rely on post-translational modifications (PTMs) of proteins. Their high-throughput characterization is thus of utmost interest for multiple biological and medical investigations. In combination with efficient enrichment methods, peptide mass spectrometry analysis allows the quantitative comparison of thousands of modified peptides over different conditions. However, the large and complex datasets produced pose multiple data interpretation challenges, ranging from spectral interpretation to statistical and multivariate analyses. Here, we present a typical workflow to interpret such data.

摘要

细胞信号传导和功能严重依赖于蛋白质的翻译后修饰(PTM)。因此,它们的高通量表征对于多种生物学和医学研究至关重要。结合高效的富集方法,肽质谱分析可以对不同条件下数千种修饰肽进行定量比较。然而,所产生的庞大而复杂的数据集带来了多个数据解释挑战,从光谱解释到统计和多变量分析。在这里,我们展示了一个解释此类数据的典型工作流程。

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