The opportunity cost of automated glycopeptide analysis: case study profiling the SARS-CoV-2 S glycoprotein.
The opportunity cost of automated glycopeptide analysis: case study profiling the SARS-CoV-2 S glycoprotein.
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DOI：
10.1007/s00216-021-03621-z
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发表时间：
2021-12
影响因子：
4.3
通讯作者：
Desaire H
中科院分区：
文献类型：
--
作者：
Go EP;Zhang S;Ding H;Kappes JC;Sodroski J;Desaire H
病毒糖蛋白的糖基化分析对疫苗的设计和开发具有重要意义。除其他好处外，糖基化分析使疫苗开发人员能够评估构建设计或生产者细胞系选择对疫苗生产的影响，并且它是一项关键措施，通过该措施可以将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，因此糖肽分析是绘制聚糖图的首选方法，然而糖肽数据的分析可能很麻烦，并且需要有经验的分析人员的专业知识。近年来，商业软件产品Byonic已在几个实例中实施，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。首先采用基于专家的分析策略对重组表达的SARS-CoV-2病毒三聚体S糖蛋白的糖肽进行分析；随后，使用Byonic完成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的糖肽分配也包含比真阳性更多的假阳性。本文的工作为消除Byonic生成的虚假分配提供了路线图，并提供了依赖于病毒糖蛋白糖肽数据集自动分配的机会成本评估。在线版本包含补充材料，可在10.1007/s00216-021-03621-z获得。
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. The online version contains supplementary material available at 10.1007/s00216-021-03621-z.
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影响因子：
5.4
作者：
Go, Eden P.;Herschhorn, Alon;Desaire, Heather
通讯作者：
Desaire, Heather
影响因子：
--
作者：
Mechref, Yehia
通讯作者：
Mechref, Yehia
超越屏蔽：聚糖在SARS-COV-2尖峰蛋白中的作用。
影响因子：
18.2
作者：
Casalino L;Gaieb Z;Goldsmith JA;Hjorth CK;Dommer AC;Harbison AM;Fogarty CA;Barros EP;Taylor BC;McLellan JS;Fadda E;Amaro RE
通讯作者：
Amaro RE
影响因子：
16.6
作者：
Cao L;Pauthner M;Andrabi R;Rantalainen K;Berndsen Z;Diedrich JK;Menis S;Sok D;Bastidas R;Park SR;Delahunty CM;He L;Guenaga J;Wyatt RT;Schief WR;Ward AB;Yates JR 3rd;Burton DR;Paulson JC
通讯作者：
Paulson JC
影响因子：
4.4
作者：
Go, Eden P.;Chang, Qing;Liao, Hua-Xin;Sutherland, Laura L.;Alam, S. Munir;Haynes, Barton F.;Desaire, Heather
通讯作者：
Desaire, Heather