Data-Driven Software to Automate Top-Down Mass Spectrometry of Large Molecules

用于自动化大分子自上而下质谱分析的数据驱动软件

• 批准号: 10761429
• 负责人: Kenneth Durbin
• 金额: $ 95.56万
• 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 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10761429
• 关键词: Academia; Algorithms; Antibodies; Automation; Biological Process; Biological Products; Biomedical Research; Computer software; Consumption; Data; Data Aggregation; Data Analyses; Data Collection; Dedications; Development; Disease; Ensure; Generations; Industry; Intelligence; Intervention; Laboratories; Machine Learning; Mass Spectrum Analysis; Measurement; Methods; Modeling; Modification; Molecular; Output; Pharmaceutical Preparations; Phase; Post-Translational Protein Processing; Process; Productivity; Proteins; Regression Analysis; Reporting; Running; Sampling; Scheme; Specificity; Speed; System; Technology; Therapeutic; Time; Update; Visual; Visualization; Voice; analytical tool; computerized data processing; cost; dashboard; data acquisition; data quality; design; equipment acquisition; experience; experimental study; feature detection; flexibility; improved; insight; instrument; interest; prevent; therapeutic development; therapeutic protein; timeline; tool

PROJECT SUMMARY Analysis of intact proteins by top-down mass spectrometry enables direct measurement of the specific sequences and posttranslational modifications that drive biological processes, disease, and protein therapeutic attributes. Despite the potential that top-down mass spectrometry has for protein analytics, acquiring high- quality data and then interpreting the data requires significant expertise and time, producing a sizable barrier to both experts and those new to the field. This project aims to develop the first commercial software to ease this acquisition burden, with workflows that automate the process of acquiring optimal top-down data, searching this data in real-time, and updating the instrument with new parameters to maximize protein characterization. In this project, new processes for detecting and identifying mass features will be implemented. Strategies will also be designed to direct the instrument in collecting high-quality fragmentation data with data-driven updates to fragmentation settings. Strategies will also be specially developed for biologics and other large molecules. Lastly, data will be presented to users via interactive visuals intended to simplify and ease data interpretation. Collectively, the new software platform will provide great improvements to how top-down mass spectrometry data can be acquired, leading to more efficient and effective protein characterization.

项目摘要 通过自上而下的质谱法分析完整蛋白质使得能够直接测量特定的蛋白质。 驱动生物过程、疾病和蛋白质治疗的序列和翻译后修饰 美德.先知-愿尽管自上而下的质谱分析法具有蛋白质分析的潜力，但获得高质量的质谱分析技术， 高质量的数据，然后解释数据，需要大量的专业知识和时间，产生了相当大的障碍， 包括专家和新进入该领域的人。该项目旨在开发第一个商业软件来缓解这一问题。 采集负担，工作流自动化采集最佳自上而下的数据，搜索 这些数据是实时的，并使用新参数更新仪器，以最大限度地提高蛋白质表征。 在该项目中，将实施用于检测和识别质量特征的新流程。战略将 还应旨在指导仪器收集高质量的碎片化数据，并进行数据驱动的更新 到碎片设置。还将专门为生物制剂和其他大分子开发策略。 最后，数据将通过交互式视觉呈现给用户，旨在简化和方便数据解释。 总的来说，新的软件平台将为自上而下的质谱分析提供巨大的改进。 可以获得数据，导致更有效和更有效的蛋白质表征。