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

研究背景从医学到材料科学，质谱学已经成为分析从药物代谢物到蛋白质络合物及其结构表征的不同类型样品的基本方法。在制药工业中，离子迁移率(IM，一种气相分离技术)转变了分离和表征复杂混合物中分子的能力，特别是同分异构体(具有相同质量)，在制药工业中，这项技术是药品生产中质量控制和副产品鉴定的关键。它根据离子的旋转平均三维结构(碰撞截面，CCS)来分离离子，因此可用于同分异构体的分离。目前还没有一个严格的框架应该如何在不同的仪器上记录这些CCS值，将其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用于化合物鉴定和数据挖掘的使用。目的和目标是：在这里，我们开发一个小分子离子迁移率的数据标准，它将使自动识别和AI方法能够利用离子迁移率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的训练集来系统地测量离子迁移率属性，如漂移时间，作为分析工作流程中分子的可能附加识别符，并调查漂移时间和液相色谱中保留时间之间的可能相关性。潜在的应用和好处高级预测工具的使用可能使通过学习不同化合物的模式来解释大型质谱学数据集变得更容易，这可以用于潜在的“填补缺口”来识别特定的片段峰，并可能用于代谢组学和蛋白质组学对蛋白质的修饰。先进的预测工具还可以预测其他模式，包括未知物质的碎裂模式和保留时间。研究领域：离子迁移质谱学，用于结构确定和异构体区分的先进预测工具。需要获得的资格：博士学位