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

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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.