Innovative Approaches and Tool Development for Proteomics Data Analysis: Applications Across Diverse Biological Systems

Bottum up proteomics (BUP) is a powerful analytical technique that involves digesting complex protein mixtures into peptides and analyzing them with liquid chromatography and tandem mass spectrometry to identify and quantify many proteins simultaneously. This produces massive multidimensional datasets which require informatics tools to analyze. The landscape of software tools for BUP analysis is vast and complex, and often custom programs and scripts are required to answer biological questions of interest in any given experiment.

This dissertation introduces novel methods and tools for analyzing BUP experiments and applies those methods to new samples. First, PrIntMap-R, a custom application for intraprotein intensity mapping, is developed and validated. This application is the first open-source tool to allow for statistical comparisons of peptides within a protein sequence along with quantitative sequence coverage visualization. Next, innovative sample preparation techniques and informatics methods are applied to characterize MUC16, a key ovarian cancer biomarker. This includes the proteomic validation of a novel model of MUC16 differing from the dominant isoform reported in literature. Shifting to bacterial studies, custom differential expression workflows are employed to investigate the role of virulence lipids in mycobacterial protein secretion by analyzing mutant strains of mycobacteria. This work links lipid presence and virulence factor secretion for the first time. Building on these efforts, OnePotN??TA, a labeling technique enabling quantification of N-terminal acetylation in mycobacterial samples, introduced. This method is the first technique to simultaneously quantify protein and N-terminal acetylation abundance using bottom-up proteomics, advancing the field of post-translational modification quantification. This project resulted in the identification of 37 new putative substrates for an N-acetyltransferase, three of which have since been validated biochemically. These tools and methodologies are further applied to various biological research areas, including breast cancer drug characterization and insect saliva analysis to perform the first proteomic studies of their kind with these respective treatments and samples. Additionally, a project focused on teaching programming skills relevant to analytical chemistry is presented. Collectively, this work enhances the analytical capabilities of bottom-up proteomics, providing novel tools and methodologies that advance protein characterization, post-translational modification analysis, and biological discovery across diverse research areas.

自下而上蛋白质组学（bottom-up proteomics, BUP）是一种高效的分析技术，其核心流程为将复杂蛋白质混合物酶解为肽段，再通过液相色谱与串联质谱联用技术对肽段进行分析，从而实现多种蛋白质的同时鉴定与定量。该技术会产生海量多维数据集，需借助生物信息学工具完成分析。当前用于BUP分析的软件工具生态庞杂且多样，针对特定实验中感兴趣的生物学问题，往往需要定制化程序与脚本才能完成相关研究。

本学位论文提出了用于BUP实验分析的全新方法与工具，并将这些方法应用于新型样本的研究中。首先，本研究开发并验证了一款用于蛋白质内强度映射的定制化应用程序PrIntMap-R。该工具是首款支持对蛋白质序列内的肽段进行统计学比较，并可实现定量序列覆盖度可视化的开源工具。随后，研究团队通过创新性样本制备技术与生物信息学方法，对关键卵巢癌生物标志物MUC16进行了表征。其中包括对一种与文献报道的主流亚型不同的新型MUC16模型进行蛋白质组学验证。

转向细菌学研究后，本研究通过分析分枝杆菌突变菌株，采用定制化差异表达分析流程，探究了毒力脂质在分枝杆菌蛋白质分泌过程中的作用。本研究首次建立了脂质存在与毒力因子分泌之间的关联。基于上述研究，本研究提出了一种可对分枝杆菌样本中N端乙酰化水平进行定量的标记技术OnePotN??TA。该方法是首款利用自下而上蛋白质组学技术同时实现蛋白质与N端乙酰化丰度定量的技术，推动了翻译后修饰定量研究领域的发展。本项目共鉴定出37种N-乙酰转移酶（N-acetyltransferase）的新型潜在底物，其中3种已通过生化实验得到验证。

上述工具与方法还被进一步应用于多个生物学研究领域，包括乳腺癌药物表征与昆虫唾液分析，利用对应的处理方式与样本完成了同类研究中的首次蛋白质组学分析。此外，本论文还介绍了一项面向分析化学领域的编程技能教学项目。

综上，本研究提升了自下而上蛋白质组学的分析能力，提供了全新的工具与方法，推动了跨多个研究领域的蛋白质表征、翻译后修饰分析以及生物学发现的进展。