An Automated Proteoform Characterization Control System

自动化蛋白质形态表征控制系统

• 批准号: 10010454
• 负责人: Kenneth Durbin
• 金额: $ 20.2万
• 依托单位: PROTEINACEOUS, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10010454
• 关键词: Amino Acid Sequence; Biological; Biotechnology; Charge; Collection; Computer software; Consumption; Custom; Data; Data Analyses; Data Set; Databases; Decision Making; Disease; Escherichia coli; Family; Feedback; Generations; Genes; Grant; Immunoprecipitation; Industry; Intelligence; Knowledge; Laboratories; Manuals; Mass Spectrum Analysis; Modeling; Molecular Weight; Names; Output; Peptides; Phase; Post-Translational Protein Processing; Probability; Procedures; Property; Proteins; Proteomics; Reporting; Research; Research Personnel; Resolution; Running; Sampling; Scanning; Scientist; Small Business Innovation Research Grant; Speed; Sum; Surveys; System; Technology; Testing; Time; Universities; Update; Validation; Variant; Visualization software; base; computerized data processing; data acquisition; data visualization; drug development; experimental study; flexibility; follow-up; insight; instrument; interest; mass spectrometer; novel; software systems; stem; success; therapeutic protein; tool

ABSTRACT Current mass spectrometers have benefited from a technological revolution over recent years, with great increases to speed and resolution as well as significant improvements in fragmentation capabilities for biomolecule analyses. Proteomics results that were previously unthinkable only a few years ago are now routine and being performed by hundreds of laboratories across the world. However, despite these advancements and successes, the software systems guiding mass spectrometry data acquisition have not seen technology improvements on the same scale. As such, these new mass spectrometers have not yet reached their full potential, particularly for intact protein characterization where analytes are more varied than peptides and acquisition parameters are tougher to generalize. A software named AutoProt is being developed to automate the targeted characterization of proteoforms from a proteoform family. AutoProt controls the acquisition of mass spectrometry data, initiating a data-driven fragmentation routine when proteoforms-of-interest from a proteoform family are detected in survey scans. As each fragmentation data scan is collected, AutoProt matches experimental data to theoretical proteoform fragments and uses the corresponding results to make an informed decision on how to further characterize the proteoform. By using instant feedback, fragmentation settings are customized specifically and in real-time for each proteoform being analyzed, leading to better characterization results in less overall time. In Phase I, AutoProt will be further developed to handle the characterization of multiple proteoforms such as modified and truncated forms from the same proteoform family in the same acquisition run, enabling a wider assessment of the proteoforms present in a sample (e.g., all the proteoforms from the same UniProt gene accession after immunoprecipitation). A database will be integrated into AutoProt to store run data and allow sample characterization to be resumed for additional acquisition runs. Furthermore, the characterization routine will be made more robust in this grant through the collection of a large amount of fragmentation data on many proteoforms and then the distillation of that data into a general fragmentation model. AutoProt will then be able to broadly characterize proteoforms with different properties (e.g., molecular weight and fragmentation propensities). Lastly, AutoProt will be outfitted with report generation to automatically output experimental report files compatible with the proteoform data visualization tool, TDCollider. With these features in place, AutoProt will be a first-of-its-kind automated characterization software with first-class proteoform characterization abilities and acquisition-to-report capabilities, eliminating time- consuming set up of acquisition and processing for proteoform characterization data.

抽象的 当前的质谱仪受益于近年来的技术革命，取得了巨大的进步 速度和分辨率的提高以及碎片能力的显着改进 生物分子分析。蛋白质组学结果在几年前还是不可想象的，现在已成为家常便饭 并由世界各地数百个实验室进行。然而，尽管有这些进步和 成功，指导质谱数据采集的软件系统还没有看到技术 同等规模的改进。因此，这些新型质谱仪尚未充分发挥其潜力， 特别是对于完整蛋白质的表征，其中分析物比肽和采集更加多样化 参数更难概括。正在开发一个名为 AutoProt 的软件来自动化目标 蛋白质型家族中蛋白质型的表征。 AutoProt 控制质量的采集 光谱数据，当感兴趣的蛋白质形式来自 在调查扫描中检测到蛋白质型家族。当收集每个碎片数据扫描时，AutoProt 会匹配 将实验数据转化为理论蛋白质片段，并使用相应的结果来做出明智的决定 决定如何进一步表征蛋白质组。通过使用即时反馈，碎片设置 针对正在分析的每种蛋白质型进行专门定制和实时定制，从而实现更好的表征 导致总时间更少。在第一阶段，AutoProt 将得到进一步开发，以处理 多种蛋白质形式，例如来自同一蛋白质形式家族的修饰和截短形式 采集运行，能够对样品中存在的蛋白质形式进行更广泛的评估（例如，所有蛋白质形式 来自免疫沉淀后的相同 UniProt 基因登录）。数据库将集成到 AutoProt 中 存储运行数据并允许恢复样品表征以进行额外的采集运行。此外， 通过收集大量的资料，本次拨款将使得表征程序更加稳健 许多蛋白质形式的碎片数据，然后将该数据蒸馏成一般碎片 模型。然后，AutoProt 将能够广泛地表征具有不同特性（例如，分子 重量和碎片倾向）。最后，AutoProt 将配备报告生成功能 自动输出与proteoform数据可视化工具兼容的实验报告文件， TDCollider。有了这些功能，AutoProt 将成为同类首个自动化表征软件 具有一流的蛋白质组表征能力和采集到报告的能力，消除了时间 消耗蛋白质型表征数据的采集和处理设置。