Combining Ion Mobility Mass Spectrometry and Advanced Predictive Tools for the Structure Determination and Isomer Differentiation.

结合离子淌度质谱和先进的预测工具进行结构测定和异构体区分。

• 批准号: 2885349
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2885349
• 关键词: Combining; Ion; Mobility; Mass; Spectrometry

Context of researchMass spectrometry (MS) has become an essential approach for analysis of different types of samples, from drugmetabolites to protein complexes and their structural characterization, in a wide range of fields from medicine to materialsscience. In the pharmaceutical industry the technique is key for quality control and identification of byproducts in drugmanufacturing, usually as a multidimensional approach with liquid chromatography (LC).Ion mobility (IM, a gas-phase separation technique) has transformed capabilities to separate and characterize molecules incomplex mixtures, particularly isomeric forms (with the same mass). It separates ions in relation to their roationallyaveraged three-dimensional structure (collision cross section, ccs), and therefore can be useful for the separation ofisomeric compounds. There is currently no stringent framework how such ccs values should be recorded across differentinstruments, documented and deposited in searchable databases, which severely limits the use of ion mobility data forcompound identification and data mining.Aims and objectivesThe objectives are:Here we develop a data standard for ion mobility of small molecules which will enable automated identification and AIapproaches to take advantage of the additional analytical dimension which ion mobility can provide. We will developmachine learning to interpret multi-dimensional data and predict molecular structures.We will use training sets of wellcharacterizedcompounds to systematically measure ion mobility properties such as the drift time, as a possible additionalidentifier for molecules in analytical workflows, and investigate possible correlations between drift times and retention timesin liquid chromatography.Potential applications and benefitsThe use of advance prediction tools may make interpreting large mass spectrometry data sets easier via learning patternsin different compounds, this can be used to potentially "fill-in-gaps" to identify specific fragment peaks and may be used inmetabolomics and proteomics to protein modifications. Advanced predictive tool may also predict other patterns includingfragmentation patterns and retention times of unknown substances.Research Areas: Ion Mobility Mass Spectrometry, Advanced Predictive Tools for the Structure Determination and IsomerDifferentiation.Qualification to be attained: PhD degree

研究背景质谱（MS）已成为从药物代谢物到蛋白质复合物及其结构表征等不同类型样品分析的重要方法，应用于从医学到材料科学的广泛领域。在制药工业中，该技术是药品生产过程中质量控制和副产品鉴定的关键，通常采用液相色谱（LC）的多维方法。离子迁移率（IM，一种气相分离技术）已经改变了分离和表征复杂混合物分子的能力，特别是同分异构体形式（具有相同质量）。它根据离子的平均三维结构（碰撞截面，ccs）来分离离子，因此可以用于分离异构体化合物。目前还没有严格的框架，如何在不同的仪器上记录这些ccs值，记录并存储在可搜索的数据库中，这严重限制了离子迁移率数据在化合物识别和数据挖掘中的使用。目标和目标：在这里，我们开发了小分子离子迁移率的数据标准，这将使自动识别和人工智能方法能够利用离子迁移率可以提供的额外分析维度。我们将开发机器学习来解释多维数据和预测分子结构。我们将使用表征良好的化合物训练集来系统地测量离子迁移特性，如漂移时间，作为分析工作流程中分子的可能附加标识符，并研究液相色谱中漂移时间和保留时间之间可能的相关性。潜在的应用和好处：使用先进的预测工具可以通过学习不同化合物的模式来解释大量的质谱数据集，这可以用来潜在地“填补空白”，以识别特定的片段峰，并可以用于代谢组学和蛋白质组学，以蛋白质修饰。先进的预测工具还可以预测其他模式，包括未知物质的破碎模式和保留时间。研究领域：离子迁移率质谱法，结构测定和异构体分化的先进预测工具。资格要求：博士学位