Software Tool for Visualization and Validation of Protein Turnover Rates Using Heavy Water Metabolic Labeling and LC-MS.
Software Tool for Visualization and Validation of Protein Turnover Rates Using Heavy Water Metabolic Labeling and LC-MS.
复制标题
DOI：
10.3390/ijms232314620
复制
发表时间：
2022-11-23
影响因子：
5.6
通讯作者：
中科院分区：
文献类型：
--
作者：
代谢稳定同位素标记后，液相色谱-质谱联用（LC-MS）是一个强大的工具，在体内蛋白质周转研究的个别蛋白质在大规模和高通量。来自数十个时间过程实验的数千种蛋白质的周转率由数据处理工具确定，这些工具是周转率自动提取工作流程的重要组成部分。发展先进的算法估计蛋白质周转一直强调。然而，时间序列数据的可视化和注释同样重要。可视化工具有助于验证模型拟合的质量、拟合优度特性、肽的质谱特征以及肽鉴定的一致性等。在这里，我们描述了一个图形用户界面（GUI）可视化的结果，从蛋白质周转率分析工具，d2 ome，它确定蛋白质周转率代谢D2 O标记，然后通过LC-MS。我们强调的具体功能的时间序列数据和他们的可视化在GUI中。通过GUI可视化的时间序列数据可以以JPEG格式保存，以便存储和进一步传播。
Metabolic stable isotope labeling followed by liquid chromatography coupled with mass spectrometry (LC-MS) is a powerful tool for in vivo protein turnover studies of individual proteins on a large scale and with high throughput. Turnover rates of thousands of proteins from dozens of time course experiments are determined by data processing tools, which are essential components of the workflows for automated extraction of turnover rates. The development of sophisticated algorithms for estimating protein turnover has been emphasized. However, the visualization and annotation of the time series data are no less important. The visualization tools help to validate the quality of the model fits, their goodness-of-fit characteristics, mass spectral features of peptides, and consistency of peptide identifications, among others. Here, we describe a graphical user interface (GUI) to visualize the results from the protein turnover analysis tool, d2ome, which determines protein turnover rates from metabolic D2O labeling followed by LC-MS. We emphasize the specific features of the time series data and their visualization in the GUI. The time series data visualized by the GUI can be saved in JPEG format for storage and further dissemination.
登录
查看更多内容
影响因子：
4.4
作者：
Sadygov VR;Zhang W;Sadygov RG
通讯作者：
Sadygov RG
DOI：
10.1016/j.bbagen.2005.12.023
发表时间：
2006-05-01
影响因子：
3
作者：
Busch, Robert;Kim, Yoo-Kyeong;Hellerstein, Marc K.
通讯作者：
Hellerstein, Marc K.
影响因子：
7.4
作者：
Sadygov, Rovshan G.;Maroto, Fernando Martin;Huehmer, Andreas F. R.
通讯作者：
Huehmer, Andreas F. R.
影响因子：
4.4
作者：
Basisty, Nathan;Shulman, Nicholas;Schilling, Birgit
通讯作者：
Schilling, Birgit
影响因子：
4.4
作者：
Lim, Matthew Y.;Paulo, Joao A.;Gygi, Steven P.
通讯作者：
Gygi, Steven P.