Chemical cross‐linking analyzed by mass spectrometry (XL‐MS) has become an important tool in unravelling protein structure, dynamics, and complex formation. Because the analysis of cross‐linked proteins with mass spectrometry results in specific computational challenges, many computational tools have been developed to identify cross‐linked peptides from mass spectra and subsequently interpret the identified cross‐links within their structural context. In this review, we will provide an overview of the different tools that are currently available to tackle the computational part of an XL‐MS experiment. First, we give an introduction on the computational challenges encountered when processing data from a cross‐linking experiment. We then discuss available tools to identify peptides that are linked by intact or MS‐cleavable cross‐linkers, and we provide an overview of tools to interpret cross‐linked peptides in the context of protein structure. Finally, we give an outlook on data management and dissemination challenges and opportunities for cross‐linking experiments.

中文翻译：

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交联肽段识别：算法的计算林

质谱（XL-MS）分析的化学交联已成为揭示蛋白质结构，动力学和复合物形成的重要工具。由于使用质谱分析交联蛋白会导致特定的计算难题，因此开发了许多计算工具来从质谱图中鉴定交联肽，然后在其结构背景下解释鉴定出的交联。在本文中，我们将概述目前可用于解决XL-MS实验计算部分的各种工具。首先，我们介绍了在处理来自交叉链接实验的数据时遇到的计算难题。然后，我们讨论可用的工具来识别通过完整或MS可裂解的交联剂连接的肽，我们提供了在蛋白质结构背景下解释交联肽的工具的概述。最后，我们对数据管理和传播的挑战以及进行交叉链接实验的机会给出了展望。