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

点击翻译按钮获取中文摘要

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