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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=632610
• 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.

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。液相色谱-质谱（LC-MS）已成为蛋白质组学应用的首选方法，因为它是蛋白质/肽分析的最强大技术。该技术依赖于较小肽的LC分离和MS分析：蛋白质蛋白水解消化的产物。近年来，大多数开发重点都放在系统的MS组件上。与此同时，许多研究人员已经认识到LC组分提供非常有价值的信息的能力，因为它能够计算被分析分子的保留特性。总的来说，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善分析输出和方法本身的设计。几个研究小组一直在开发肽保留预测模型。我的实验室一直处于这些发展的最前沿：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，吸引了蛋白质组学/色谱界以及蛋白质组学设备和软件主要供应商的关注。