Analytical and Bioanalytical Chemistry ( IF 3.8 ) Pub Date : 2021-08-27 , DOI: 10.1007/s00216-021-03621-z Eden P Go 1 , Shijian Zhang 2, 3 , Haitao Ding 4 , John C Kappes 4, 5 , Joseph Sodroski 2, 3, 6 , Heather Desaire 1
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Glycosylation analysis of viral glycoproteins contributes significantly to vaccine design and development. Among other benefits, glycosylation analysis allows vaccine developers to assess the impact of construct design or producer cell line choices for vaccine production, and it is a key measure by which glycoproteins that are produced for use in vaccination can be compared to their native viral forms. Because many viral glycoproteins are multiply glycosylated, glycopeptide analysis is a preferrable approach for mapping the glycans, yet the analysis of glycopeptide data can be cumbersome and requires the expertise of an experienced analyst. In recent years, a commercial software product, Byonic, has been implemented in several instances to facilitate glycopeptide analysis on viral glycoproteins and other glycoproteomics data sets, and the purpose of the study herein is to determine the strengths and limitations of using this software, particularly in cases relevant to vaccine development. The glycopeptides from a recombinantly expressed trimeric S glycoprotein of the SARS-CoV-2 virus were first analyzed using an expert-based analysis strategy; subsequently, analysis of the same data set was completed using Byonic. Careful assessment of instances where the two methods produced different results revealed that the glycopeptide assignments from Byonic contained more false positives than true positives, even when the data were assessed using a 1% false discovery rate. The work herein provides a roadmap for removing the spurious assignments that Byonic generates, and it provides an assessment of the opportunity cost for relying on automated assignments for glycopeptide data sets from viral glycoproteins.
Graphical abstract
中文翻译：
自动糖肽分析的机会成本：分析 SARS-CoV-2 S 糖蛋白的案例研究
病毒糖蛋白的糖基化分析有助于疫苗的设计和开发。除其他好处外，糖基化分析允许疫苗开发人员评估构建体设计或生产细胞系选择对疫苗生产的影响，这是一项关键措施，可以将生产用于疫苗接种的糖蛋白与其天然病毒形式进行比较。由于许多病毒糖蛋白是多重糖基化的，糖肽分析是绘制聚糖的首选方法，但糖肽数据的分析可能很麻烦，并且需要经验丰富的分析师的专业知识。近年来，已在多个实例中实施了商业软件产品 Byonic，以促进对病毒糖蛋白和其他糖蛋白组学数据集的糖肽分析，本文研究的目的是确定使用该软件的优势和局限性，特别是在与疫苗开发相关的情况下。首先使用基于专家的分析策略分析来自重组表达的 SARS-CoV-2 病毒三聚体 S 糖蛋白的糖肽；随后，使用 Byonic 完成了对同一数据集的分析。仔细评估两种方法产生不同结果的实例表明，即使使用 1% 的错误发现率评估数据，Byonic 的糖肽分配包含的假阳性多于真阳性。本文的工作提供了去除 Byonic 生成的虚假分配的路线图，
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