Shortt Raquel L, Pino Lindsay K, Chea Emily E, Ramirez Carolina Rojas, Polasky Daniel A, Nesvizhskii Alexey I, Jones Lisa M
Department of Pharmaceutical Sciences, University of Maryland, Baltimore, Maryland 21201, United States.
Talus Bio, Seattle, Washington 98122, United States.
J Am Soc Mass Spectrom. 2024 Dec 4;35(12):3301-3307. doi: 10.1021/jasms.4c00196. Epub 2024 Oct 2.
Covalent labeling methods coupled to mass spectrometry have emerged in recent years for studying the higher order structure of proteins. Quantifying the extent of modification of proteins in multiple states (i.e., ligand free vs ligand-bound) can provide information on protein interaction sites and regions of conformational change. Though there are several software platforms that are used to quantify the extent of modification, the process can still be time-consuming, particularly for proteome-wide studies. Here, we present an open-source software for quantitation called Covalent labeling Automated Data Analysis Platform for high Throughput in R (coADAPTr). coADAPTr tackles the need for more efficient data analysis in covalent labeling mass spectrometry for techniques such as hydroxyl radical protein footprinting (HRPF). Traditional methods like Excel's Power Pivot (PP) are cumbersome and time-intensive, posing challenges for large-scale analyses. coADAPTr simplifies analysis by mimicking the functions used in the previous quantitation platform using PowerPivot in Microsoft Excel but with fewer steps, offering proteome-wide insights with enhanced graphical interpretations. Several features have been added to improve the fidelity and throughput compared to those of PowerPivot. These include filters to remove any duplicate data and the use of the arithmetic mean rather than the geometric mean for quantitation of the extent of modification. Validation studies confirm coADAPTr's accuracy and efficiency while processing data up to 200 times faster than conventional methods. Its open-source design and user-friendly interface make it accessible for researchers exploring intricate biological phenomena via HRPF and other covalent labeling MS methods. coADAPTr marks a significant leap in structural proteomics, providing a versatile and efficient platform for data interpretation. Its potential to transform the field lies in its seamless handling of proteome-wide data analyses, empowering researchers with a robust tool for deciphering complex structural biology data.
近年来，共价标记方法与质谱联用已出现，用于研究蛋白质的高级结构。量化蛋白质在多种状态下（即无配体状态与配体结合状态）的修饰程度，可以提供有关蛋白质相互作用位点和构象变化区域的信息。尽管有几种软件平台可用于量化修饰程度，但该过程仍然可能很耗时，尤其是对于全蛋白质组研究而言。在此，我们展示了一款名为“用于R语言高通量分析的共价标记自动化数据分析平台（coADAPTr）”的开源定量软件。coADAPTr满足了共价标记质谱中对更高效数据分析的需求，适用于诸如羟基自由基蛋白质足迹法（HRPF）等技术。像Excel的Power Pivot（PP）这样的传统方法既繁琐又耗时，给大规模分析带来了挑战。coADAPTr通过模仿先前在Microsoft Excel中使用PowerPivot的定量平台所使用的功能来简化分析，但步骤更少，通过增强的图形解释提供全蛋白质组的见解。与PowerPivot相比，还添加了几个功能来提高保真度和通量。这些功能包括用于去除任何重复数据的过滤器，以及在修饰程度定量中使用算术平均值而非几何平均值。验证研究证实了coADAPTr在处理数据时的准确性和效率，其速度比传统方法快200倍。其开源设计和用户友好界面使研究人员能够通过HRPF和其他共价标记质谱方法探索复杂的生物学现象。coADAPTr标志着结构蛋白质组学的重大飞跃，为数据解释提供了一个通用且高效的平台。它改变该领域的潜力在于其对全蛋白质组数据分析的无缝处理，为研究人员提供了一个强大的工具来解读复杂的结构生物学数据。
