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

在从药物到材料科学的各种领域中，研究量谱法（MS）的背景已成为分析不同类型样品的基本方法，从药物实物化到蛋白质复合物及其结构表征。在制药行业中，该技术是药物制造中副产品的质量控制和鉴定的关键，通常是一种具有液相色谱的多维方法（LC）。IOM迁移率（IM，一种气相分离技术）具有将分离和表征分子的量不相同的混合物（尤其是同样的群众）的能力，以使其转化为分离和表征分子的能力。它与离子相对于其静脉曲张的三维结构（Collision Cross截面，CCS）分开，因此对于异构化合物的分离可能是有用的。 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机动性可以提供。我们将开发摩碱学习来解释多维数据并预测分子结构。我们将使用良好的表达式功能的培训集，以系统地测量离子迁移率，例如漂移时间，作为分子分子的额外识别剂，用于分析工作流程中的额外识别剂，并在整个液体液体液体上的使用量和保留量的相关性。通过学习模式在不同的化合物中，光谱数据集更容易，这可用于潜在地“填充间隙”以识别特定的片段峰，并且可以使用非代谢组学和蛋白质组学将其用于蛋白质修饰。先进的预测工具还可以预测其他模式，包括抛弃模式和未知物质的保留时间。研究区域：离子迁移率质谱法，结构确定和异构分辨率的高级预测工具。