Peptide chromatography: comprehensive coverage of separation mechanisms for advanced proteomic applications

30年来，蛋白质组学的加速发展改变了许多科学领域的格局。由于样品制备、肽分离、质谱分析和蛋白质鉴定软件的改进，这些进步成为可能。液相色谱-质谱法（LC-MS）已成为蛋白质组学应用的首选方法。该技术依赖于LC分离和质谱分析多肽，蛋白质水解消化的产物。大多数开发重点都放在系统的MS组件上。肽分离的创新进展较慢，但许多研究人员已经认识到它的效用，通过计算肽保留特性的能力来扩展这种分析。我的实验室领导了这一科学挑战，打破了自我获得第一个国家科学研究委员会发现基金（2008）以来MS统治的现状。我的研究项目的广泛目标是将急需的预测工具带到实践肽分离的实验室：从蛋白质组学研究小组到工业纯化和质量控制设施。为了实现这一目标，我开发了第一个序列特异性肽保留预测模型，发现了分离机制的新特征，引入了最先进的二维和三维LC-MS协议，并将我们的建模方法扩展到包含翻译后修饰的肽和各种分离模式的适用性。挑战仍然存在：固定相、柱温和梯度条件的广泛变化都限制了实验室间的适用性和研究和工业LC社区对预测模型的采用。还迫切需要进一步将预测方法扩展到各种分离机制和洗脱条件，因为这将为运行分析和制备肽LC的实验室提供先进的方法开发工具。除此之外，交替的分离条件有可能驱动新的肽富集系统，通过简化分析的肽混合物，同时保留蛋白质含量的相关信息，可以帮助彻底改变高通量蛋白质组学。本研究旨在将我们的保留预测方法扩展到蛋白质组学中使用的标准LC条件之外，以确保其全面覆盖和适用性。它将：1)为蛋白质组学研究人员提供可靠的预测工具，这些工具受LC设置变化的影响较小；2)在各种固定相中创建新的肽保留时间预测模型，包括最具挑战性的混合模式肽分离；3)产生新的多肽分离/富集技术，以更快地进行蛋白质组学分析；4)为我实验室的11名成员（学生和研究助理）提供世界级的培训；5)进一步建立加拿大多肽分离科学前沿研究平台。

The accelerated development of proteomics over 30 years has changed the landscape in many fields of science. These advancements were possible thanks to improvements in sample preparation, peptide separation, mass-spectrometry, and protein identification software. Liquid chromatography - mass spectrometry (LC-MS) has become the method of choice for proteomic applications. This technique relies on LC separation and MS analysis of peptides, the products of proteolytic digestion. Most of the developmental emphasis has been on the MS component of the system. Peptide separation innovations have progressed slower, but many researchers have recognized its utility to extend this analysis through the capability to compute peptide retention properties. My laboratory has led this scientific challenge, breaking this MS domination status quo since my first NSERC Discovery Grant award (2008). The broad goal of my research program is to bring much-needed prediction tools to laboratories practicing peptide separation: from proteomics research groups to industrial purification and quality control facilities. To achieve this, I've developed the first sequence-specific peptide retention prediction model, discovered new features of separation mechanisms, introduced the most advanced two- and three-dimensional LC-MS protocols, and extended the applicability of our modeling approaches into peptides containing post-translational modifications and across various separation modes. Challenges still remain: the wide variability of stationary phases, column temperatures, and gradient conditions all limit inter-laboratory applicability and adoption of the prediction models by the research and industrial LC communities. Further expansion of prediction methods into a variety of separation mechanisms and eluent conditions is also urgently needed, as this will provide laboratories running analytical and preparative peptide LC with advanced tools for method development. In addition to it, alternate separation conditions have the potential to drive new peptide enrichment systems that could help revolutionize high-throughput proteomics by simplifying analysed peptide mixtures but keeping relevant information on protein content. The proposed research aims to expand our retention prediction approach beyond standard LC conditions used in proteomics to ensure its comprehensive coverage and applicability. It will: 1) provide proteomic researchers with reliable prediction tools that are less influenced by variations in their LC setups; 2) create new models for peptide retention time prediction across a wide variety of stationary phases, including the most challenging mixed mode peptide separations; 3) generate novel peptide fractionation/enrichment techniques for faster proteomic analysis; 4) provide world-class training for eleven members of my lab (students and research associates); 5) further establish Canadian based research platform at the forefront of peptide separation science.