SugarPy facilitates the universal, discovery-driven analysis of intact glycopeptides

Protein glycosylation is a complex post-translational modification with crucial cellular functions in all domains of life. Currently, large-scale glycoproteomics approaches rely on glycan database dependent algorithms and are thus unsuitable for discovery-driven analyses of glycoproteomes. Therefore, we devised SugarPy, a glycan database independent Python module, and validated it on the glycoproteome of human breast milk. We further demonstrated its applicability by analyzing glycoproteomes with uncommon glycans stemming from the green algae <em>Chlamydomonas reinhardtii </em>and the archaeon <em>Haloferax volcanii</em>. SugarPy also facilitated the novel characterization of glycoproteins from the red alga <em>Cyanidioschyzon merolae</em>. Provided here are, for each species: input files (mzML) SugarPy result files In addition, for <em>Homo sapiens</em> and <em>Chlamydomonas reinhardtii</em>, the following is included: SugarQb result files pGlyco result files MSFragger-Glyco result files Furthermore, a SugarPy example_data folder is provided that can be used with the SugarPy example scripts. The source code for SugarPy can be found on GitHub: https://github.com/SugarPy/SugarPy蛋白质糖基化（Protein glycosylation）是一类复杂的翻译后修饰（post-translational modification），在所有生命域中均发挥关键的细胞功能。当前，大规模糖蛋白质组学（glycoproteomics）研究方法依赖于糖链数据库（glycan database）驱动的算法，因此不适用于糖蛋白质组（glycoproteome）的发现驱动型分析。为此，我们开发了SugarPy——一款不依赖糖链数据库的Python模块，并在人母乳的糖蛋白质组上对其进行了验证。此外，我们通过分析源自绿藻莱茵衣藻（*Chlamydomonas reinhardtii*）与古菌沃氏嗜盐古菌（*Haloferax volcanii*）的携带罕见糖链的糖蛋白质组，进一步证实了其应用潜力。SugarPy还助力实现了对红藻*Cyanidioschyzon merolae*糖蛋白的全新表征。本数据集提供了各物种的以下数据：mzML格式的输入文件、SugarPy分析结果文件。此外，针对智人（*Homo sapiens*）与莱茵衣藻（*Chlamydomonas reinhardtii*），本数据集还包含以下数据：SugarQb分析结果文件、pGlyco分析结果文件以及MSFragger-Glyco分析结果文件。此外，本数据集还提供了SugarPy示例数据文件夹，可配合SugarPy示例脚本使用。SugarPy的源代码可在GitHub上获取：https://github.com/SugarPy/SugarPy