Harnessing chromatographic information in proteomics: comprehensive coverage of peptide post-translational and chemical modifications

利用蛋白质组学中的色谱信息：全面覆盖肽翻译后和化学修饰

• 批准号: RGPIN-2016-05963
• 负责人: Krokhin, Oleg
• 金额: $ 4.44万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=710083
• 关键词: Harnessing; chromatographic; information; proteomics; comprehensive

Rapid developments in bioanalytical chemistry have provided enormous benefits for many applied fields of modern science including human health, agriculture, energy, and environmental studies. Liquid chromatography mass spectrometry (LC-MS) has become the method of choice for proteomic applications, as it is the most powerful technique for protein/peptide analysis. This technique relies on LC separation and MS analysis of smaller peptides: products of protein proteolytic digestion. Most of the developmental emphasis in recent years has been on the MS-component of the system. At the same time many researchers have recognized the ability of the LC component to provide very valuable information, given the ability to compute the retention properties of analyzed molecules. Overall this makes LC-MS a “double-edged sword”, where the outcomes from both methods can be used to improve the analysis output and design of the methods themselves. Several research teams have been developing peptide retention prediction models. My laboratory has been at the forefront of these developments: our Sequence Specific Retention Calculator (SSRCalc) model has become a benchmark tool in the field, attracting attention both from the proteomic/chromatographic community and from major vendors of proteomics equipment and software. So far, the vast majority of predictive approaches target peptides, which consist of 20 natural, non-modified amino acids. It is known, however, that proteins may carry numerous post-translational modifications, which often control protein functions in complex biological systems. These modifications cannot be predicted based on genomic sequence, which make proteomics a key methodology for their study. These modifications change both molecular weight and hydrophobicity of analysed peptides. While determining the effect of a modification on the MS properties (mass shift) of a peptide is straightforward, predicting the alteration in chromatographic properties is difficult. The focus of the next cycle of my Discovery Grant is to extend the SSRCalc prediction methodology to peptides with post-translational and chemical modifications. This will 1) provide proteomic researchers with new tools to effectively monitor these modifications; 2) help us to develop comprehensive solutions to this fundamental problem in separation chemistry while also gaining new insights into underlying chemical and biological processes; 3) provide extensive training for the next generation of bioanalytical chemists in this highly relevant field; 4) further establish Canada's position as a world leader in proteomics and peptide chromatography research.

生物分析化学的快速发展为现代科学的许多应用领域提供了巨大的好处，包括人类健康、农业、能源和环境研究。液-质联用(LC-MS)是蛋白质/多肽分析中最有效的方法，已成为蛋白质组学研究的首选方法。这项技术依赖于对较小的多肽进行LC分离和MS分析，这些多肽是蛋白质蛋白分解消化的产物。近年来，大多数开发重点都集中在该系统的MS组件上。与此同时，许多研究人员已经认识到，考虑到计算被分析分子的保留性质的能力，LC组分能够提供非常有价值的信息。总体而言，这使得LC-MS成为一把“双刃剑”，两种方法的结果都可以用来改进分析输出和方法本身的设计。几个研究团队一直在开发多肽保留预测模型。我的实验室一直处于这些发展的前沿：我们的序列特异性保留计算器(SSRCalc)模型已成为该领域的基准工具，吸引了蛋白质组/色谱界和主要蛋白质组学设备和软件供应商的注意。 到目前为止，绝大多数预测方法都是针对多肽的，这些多肽由20个天然的、未修饰的氨基酸组成。然而，众所周知，蛋白质可能带有许多翻译后修饰，这些修饰通常控制复杂生物系统中的蛋白质功能。这些修饰不能基于基因组序列进行预测，这使得蛋白质组学成为他们研究的关键方法。这些修饰改变了分析多肽的相对分子质量和疏水性。虽然确定修饰对多肽的MS性质(质量位移)的影响是直截了当的，但预测层析性质的变化是困难的。我的发现基金的下一个周期的重点是将SSRCalc预测方法扩展到具有翻译后和化学修饰的多肽。这将1)为蛋白质组研究人员提供有效监测这些修饰的新工具；2)帮助我们为分离化学中的这一基本问题开发全面的解决方案，同时对潜在的化学和生物过程获得新的见解；3)为这一高度相关领域的下一代生物分析化学家提供广泛的培训；4)进一步确立加拿大在蛋白质组学和肽层析研究方面的世界领先地位。