Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, United States.
Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana, United States; Department of Biostatistics and Epidemiology, Rutgers School of Public Health, Piscataway, New Jersey, United States.
Mol Cell Proteomics. 2023 Sep;22(9):100630. doi: 10.1016/j.mcpro.2023.100630. Epub 2023 Aug 9.
Thermal proteome profiling (TPP) is an invaluable tool for functional proteomics studies that has been shown to discover changes associated with protein-ligand, protein-protein, and protein-RNA interaction dynamics along with changes in protein stability resulting from cellular signaling. The increasing number of reports employing this assay has not been met concomitantly with new approaches leading to advancements in the quality and sensitivity of the corresponding data analysis. The gap between data acquisition and data analysis tools is important to fill as TPP findings have reported subtle melt shift changes related to signaling events such as protein posttranslational modifications. In this study, we have improved the Inflect data analysis pipeline (now referred to as InflectSSP, available at https://CRAN.R-project.org/package=InflectSSP) to increase the sensitivity of detection for both large and subtle changes in the proteome as measured by TPP. Specifically, InflectSSP now has integrated statistical and bioinformatic functions to improve objective functional proteomics findings from the quantitative results obtained from TPP studies through increasing both the sensitivity and specificity of the data analysis pipeline. InflectSSP incorporates calculation of a "melt coefficient" into the pipeline with production of average melt curves for biological replicate studies to aid in identification of proteins with significant melts. To benchmark InflectSSP, we have reanalyzed two previously reported datasets to demonstrate the performance of our publicly available R-based program for TPP data analysis. We report new findings following temporal treatment of human cells with the small molecule thapsigargin that induces the unfolded protein response as a consequence of inhibition of sarcoplasmic/endoplasmic reticulum calcium ATPase 2A. InflectSSP analysis of our unfolded protein response study revealed highly reproducible and statistically significant target engagement over a time course of treatment while simultaneously providing new insights into the possible mechanisms of action of the small molecule thapsigargin.
热蛋白质组分析（TPP）是功能蛋白质组学研究的一种非常有价值的工具，它已被证明可发现与蛋白质-配体、蛋白质-蛋白质和蛋白质-RNA 相互作用动态以及细胞信号导致的蛋白质稳定性变化相关的变化。越来越多的报告采用这种检测方法，但与之同时出现的新方法并没有提高相应数据分析的质量和灵敏度。数据采集和数据分析工具之间的差距很重要，因为 TPP 研究报告了与信号事件相关的微小熔融位移变化，如蛋白质翻译后修饰。在这项研究中，我们改进了 Inflect 数据分析管道（现在称为 InflectSSP，可在 https://CRAN.R-project.org/package=InflectSSP 上获得），以提高 TPP 测量的蛋白质组中较大和较小变化的检测灵敏度。具体来说，InflectSSP 现在集成了统计和生物信息学功能，通过提高数据分析管道的灵敏度和特异性，从 TPP 研究中获得的定量结果中提高客观的功能蛋白质组学发现。InflectSSP 将“熔融系数”的计算纳入管道，为生物重复研究生成平均熔融曲线，以帮助识别具有显著熔融的蛋白质。为了对 InflectSSP 进行基准测试，我们重新分析了两个以前报告的数据集，以证明我们公开提供的用于 TPP 数据分析的基于 R 的程序的性能。我们报告了在使用小分子 thapsigargin 处理人类细胞后的新发现，小分子 thapsigargin 抑制肌浆/内质网钙 ATP 酶 2A 会导致未折叠蛋白反应。我们的未折叠蛋白反应研究的 InflectSSP 分析显示，在治疗过程中时间过程具有高度可重复性和统计学意义的靶标结合，同时为小分子 thapsigargin 的可能作用机制提供了新的见解。
