COMPUTATIONAL TOOLS FOR PROTEOFORM IDENTIFICATION BY TOP-DOWNDATA INDEPENDENT ACQUISITION MASS SPECTROMETRY

通过自上而下数据独立采集质谱进行蛋白质形态鉴定的计算工具

• 批准号: 10406784
• 负责人: Xiaowen Liu
• 金额: $ 29.25万
• 依托单位: TULANE UNIVERSITY OF LOUISIANA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10406784
• 关键词: COMPUTATIONAL; TOOLS; PROTEOFORM; IDENTIFICATION; TOP

Summary Mass spectrometry-based top-down proteomics has become one of the most informative approaches in protein analysis because it provides the bird's-eye view of intact proteoforms (protein forms) generated from post-translational modifications and sequence variations. Data dependent acquisition and data independent acquisition are the two main methods in top-down mass spectrometry. The former has been the dominant one, but it has two main challenges in proteome-wide studies: low protein coverage: a regular experiment of human cells can identify only 200 – 400 proteins, and low reproducibility: a technical triplet shares only about one third of identified proteoforms. Top-down data independent acquisition mass spectrometry (TD-DIA-MS) has the potential to significantly increase protein coverage and improve reproducibility in proteome-wide studies. However, its application has been hampered by the complexity of the data and the lack of efficient software tools. To address this problem, we will propose new algorithms and machine learning models and develop the first software package for proteoform identification by TD-DIA-MS. The proposed research will be conducted by a group of researchers with complementary expertise. All the proposed algorithms will be implemented as user-friendly open source software tools.

摘要 基于质谱学的自上而下的蛋白质组学已经成为信息量最大的蛋白质组学之一 蛋白质分析的方法，因为它提供了完整的鸟瞰 翻译后修饰产生的蛋白质形式(蛋白质形式)和 序列变异。数据依赖采集和数据独立采集分为 自上而下质谱学中的两种主要方法。前者一直是 占主导地位，但在蛋白质组研究中有两个主要挑战：低蛋白质 报道：人类细胞的常规实验只能识别200-400种蛋白质，而且 再现性低：技术三胞胎仅分享已确定的约三分之一 蛋白质形式。自上而下数据独立采集质谱仪(TD-DIA-MS) 有可能显著增加蛋白质覆盖率并改善重复性 全蛋白质组研究。然而，由于其复杂性，其应用受到了阻碍 数据的缺乏和缺乏有效的软件工具。为了解决这个问题，我们将 提出新的算法和机器学习模型，并开发第一个软件 TD-DIA-MS鉴定蛋白质组拟议的研究将是 由一组具有互补专业知识的研究人员进行。所有建议的 算法将作为用户友好的开源软件工具实施。