Mass spectrometry has become the method of choice for the qualitative and quantitative characterization of protein mixtures isolated from all kinds of living organisms. The raw data in these studies are MS/MS spectra, usually of peptides produced by proteolytic digestion of a protein. These spectra are "translated" into peptide sequences, normally with the help of various search engines. Data acquisition and interpretation have both been automated, and most researchers look only at the summary of the identifications without ever viewing the underlying raw data used for assignments. Automated analysis of data is essential due to the volume produced. However, being familiar with the finer intricacies of peptide fragmentation processes, and experiencing the difficulties of manual data interpretation allow a researcher to be able to more critically evaluate key results, particularly because there are many known rules of peptide fragmentation that are not incorporated into search engine scoring. Since the most commonly used MS/MS activation method is collision-induced dissociation (CID), in this article we present a brief review of the history of peptide CID analysis. Next, we provide a detailed tutorial on how to determine peptide sequences from CID data. Although the focus of the tutorial is de novo sequencing, the lessons learned and resources supplied are useful for data interpretation in general.

中文翻译：

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串联质谱从头进行肽测序的经验。

质谱已成为定性和定量表征从各种活生物体中分离的蛋白质混合物的一种选择方法。这些研究中的原始数据是MS / MS谱图，通常是通过蛋白水解消化蛋白质而产生的肽。通常在各种搜索引擎的帮助下，这些光谱被“翻译”成肽序列。数据获取和解释都已经自动化，并且大多数研究人员仅查看标识的摘要，而从未查看用于分配的基础原始数据。由于生成的数据量大，因此自动分析数据至关重要。但是，由于熟悉肽片段化过程的复杂性，经历手动数据解释的困难使研究人员能够更严格地评估关键结果，尤其是因为有许多已知的肽片段化规则并未纳入搜索引擎评分中。由于最常用的MS / MS激活方法是碰撞诱导解离（CID），因此在本文中，我们将简要介绍肽CID分析的历史。接下来，我们提供有关如何从CID数据确定肽序列的详细教程。尽管本教程的重点是从头开始排序，但是从中获得的经验教训和所提供的资源通常可用于数据解释。由于最常用的MS / MS激活方法是碰撞诱导解离（CID），因此在本文中，我们将简要介绍肽CID分析的历史。接下来，我们提供有关如何从CID数据确定肽序列的详细教程。尽管本教程的重点是从头开始排序，但是从中获得的经验教训和所提供的资源通常可用于数据解释。由于最常用的MS / MS激活方法是碰撞诱导解离（CID），因此在本文中，我们将简要介绍肽CID分析的历史。接下来，我们提供有关如何从CID数据确定肽序列的详细教程。尽管本教程的重点是从头开始排序，但是从中获得的经验教训和所提供的资源通常可用于数据解释。