Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.
Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA.
Mol Cell Proteomics. 2021;20:100018. doi: 10.1074/mcp.TIR120.002216. Epub 2020 Dec 11.
Open searching has proven to be an effective strategy for identifying both known and unknown modifications in shotgun proteomics experiments. Rather than being limited to a small set of user-specified modifications, open searches identify peptides with any mass shift that may correspond to a single modification or a combination of several modifications. Here we present PTM-Shepherd, a bioinformatics tool that automates characterization of post-translational modification profiles detected in open searches based on attributes, such as amino acid localization, fragmentation spectra similarity, retention time shifts, and relative modification rates. PTM-Shepherd can also perform multiexperiment comparisons for studying changes in modification profiles, e.g., in data generated in different laboratories or under different conditions. We demonstrate how PTM-Shepherd improves the analysis of data from formalin-fixed and paraffin-embedded samples, detects extreme underalkylation of cysteine in some data sets, discovers an artifactual modification introduced during peptide synthesis, and uncovers site-specific biases in sample preparation artifacts in a multicenter proteomics profiling study.
开放搜索已被证明是一种有效的策略,可以识别鸟枪法蛋白质组学实验中的已知和未知修饰。开放搜索不限于用户指定的一小部分修饰,而是可以识别具有任何质量偏移的肽,这些偏移可能对应于单个修饰或几个修饰的组合。在这里,我们介绍了 PTM-Shepherd,这是一种生物信息学工具,可以根据属性(例如氨基酸定位、片段谱相似性、保留时间偏移和相对修饰率)自动描述开放搜索中检测到的翻译后修饰谱特征。PTM-Shepherd 还可以进行多实验比较,以研究修饰谱的变化,例如,在不同实验室或不同条件下生成的数据。我们展示了 PTM-Shepherd 如何改进对福尔马林固定和石蜡包埋样本数据的分析,检测到一些数据集中文 cysteine 的极端低酰化,发现肽合成过程中引入的人为修饰,并揭示了多中心蛋白质组学分析研究中样品制备人工制品的特定部位偏倚。