CardiOmics Program, Center for Heart and Vascular Research and Department of Cellular and Integrative Physiology, University of Nebraska Medical Center, Omaha, Nebraska 68198, United States.
Department of Biochemistry, Medical College of Wisconsin, Milwaukee, Wisconsin 53226, United States.
J Proteome Res. 2024 Aug 2;23(8):3235-3248. doi: 10.1021/acs.jproteome.3c00800. Epub 2024 Feb 27.
Currently, no consensus exists regarding criteria required to designate a protein within a proteomic data set as a cell surface protein. Most published proteomic studies rely on varied ontology annotations or computational predictions instead of experimental evidence when attributing protein localization. Consequently, standardized approaches for analyzing and reporting cell surface proteome data sets would increase confidence in localization claims and promote data use by other researchers. Recently, we developed Veneer, a web-based bioinformatic tool that analyzes results from cell surface -glycocapture workflows─the most popular cell surface proteomics method used to date that generates experimental evidence of subcellular location. Veneer assigns protein localization based on defined experimental and bioinformatic evidence. In this study, we updated the criteria and process for assigning protein localization and added new functionality to Veneer. Results of Veneer analysis of 587 cell surface -glycocapture data sets from 32 published studies demonstrate the importance of applying defined criteria when analyzing cell surface proteomics data sets and exemplify how Veneer can be used to assess experimental quality and facilitate data extraction for informing future biological studies and annotating public repositories.
目前，对于在蛋白质组学数据集中指定蛋白质为细胞表面蛋白所需的标准，尚未达成共识。大多数已发表的蛋白质组学研究在归因于蛋白质定位时，依赖于不同的本体论注释或计算预测，而不是实验证据。因此，分析和报告细胞表面蛋白质组数据集的标准化方法将提高定位声明的可信度，并促进其他研究人员对数据的使用。最近，我们开发了一个名为 Veneer 的基于网络的生物信息学工具，该工具可分析细胞表面糖捕获工作流程的结果，这是迄今为止最流行的细胞表面蛋白质组学方法，可提供亚细胞位置的实验证据。Veneer 根据定义的实验和生物信息学证据来分配蛋白质的定位。在这项研究中，我们更新了分配蛋白质定位的标准和流程，并为 Veneer 添加了新功能。Veneer 对 32 项已发表研究的 587 个细胞表面糖捕获数据集的分析结果表明，在分析细胞表面蛋白质组学数据集时应用定义标准的重要性，并举例说明了如何使用 Veneer 评估实验质量，促进数据提取，为未来的生物学研究提供信息，并注释公共存储库。
