Covalent Labeling Automated Data Analysis Platform for High Throughput in R (coADAPTr): A Proteome-Wide Data Analysis Platform for Covalent Labeling Experiments.

IF 3.1 2区 化学 Q2 BIOCHEMICAL RESEARCH METHODS Journal of the American Society for Mass Spectrometry Pub Date : 2024-12-04 Epub Date: 2024-10-02 DOI:10.1021/jasms.4c00196
Raquel L Shortt, Lindsay K Pino, Emily E Chea, Carolina Rojas Ramirez, Daniel A Polasky, Alexey I Nesvizhskii, Lisa M Jones
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Abstract

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.

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共价标记高通量 R 语言自动数据分析平台(coADAPTr):共价标记实验的全蛋白质组数据分析平台。
近年来出现了共价标记法与质谱联用的方法,用于研究蛋白质的高阶结构。量化蛋白质在多种状态(即无配体与配体结合)下的修饰程度可以提供蛋白质相互作用位点和构象变化区域的信息。虽然有几种软件平台可用于量化修饰程度,但这一过程仍然很耗时,尤其是对于全蛋白质组研究而言。coADAPTr 解决了共价标记质谱技术(如羟基自由基蛋白质足迹(HRPF))对更高效数据分析的需求。coADAPTr模仿了以前使用Microsoft Excel中PowerPivot的定量平台所使用的功能,但减少了步骤,从而简化了分析,并通过增强的图形解释提供了对整个蛋白质组的洞察力。与 PowerPivot 相比,该平台增加了多项功能,以提高保真度和吞吐量。这些功能包括去除重复数据的过滤器,以及使用算术平均值而不是几何平均值来量化修饰程度。验证研究证实了 coADAPTr 的准确性和效率,其处理数据的速度比传统方法快 200 倍。coADAPTr 标志着结构蛋白质组学的一次重大飞跃,为数据解读提供了一个多功能、高效的平台。它改变这一领域的潜力在于它能无缝处理整个蛋白质组的数据分析,为研究人员破解复杂的结构生物学数据提供了强大的工具。
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来源期刊
CiteScore
5.50
自引率
9.40%
发文量
257
审稿时长
1 months
期刊介绍: The Journal of the American Society for Mass Spectrometry presents research papers covering all aspects of mass spectrometry, incorporating coverage of fields of scientific inquiry in which mass spectrometry can play a role. Comprehensive in scope, the journal publishes papers on both fundamentals and applications of mass spectrometry. Fundamental subjects include instrumentation principles, design, and demonstration, structures and chemical properties of gas-phase ions, studies of thermodynamic properties, ion spectroscopy, chemical kinetics, mechanisms of ionization, theories of ion fragmentation, cluster ions, and potential energy surfaces. In addition to full papers, the journal offers Communications, Application Notes, and Accounts and Perspectives
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