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

总结 基于质谱的自上而下的蛋白质组学已经成为信息量最大的 蛋白质分析的方法，因为它提供了完整的鸟瞰图 由翻译后修饰产生的蛋白质形式（蛋白质形式）， 序列变异数据相关采集和数据独立采集是 这是自上而下质谱法中的两种主要方法。前者一直是 占主导地位的一个，但它在蛋白质组研究中有两个主要挑战：低蛋白 覆盖范围：人类细胞的常规实验只能识别200 - 400种蛋白质， 低重现性：一个技术三联体仅占鉴定的三分之一。 蛋白质型自顶向下数据独立采集质谱法（TD-DIA-MS） 具有显著增加蛋白质覆盖率和改善再现性的潜力， 蛋白质组研究然而，其应用受到了复杂性的阻碍， 缺乏数据和有效的软件工具。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 软件包的蛋白质型鉴定的TD-DIA-MS。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。所有拟议 算法将作为用户友好的开放源码软件工具实施。

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.