Predicting MS/MS Fragmentation using Density Function Theory

使用密度函数理论预测 MS/MS 碎片

基本信息

批准号：
1647735
负责人：
金额：
--
依托单位：
University of Southampton
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2015
资助国家：
英国
起止时间：
2015 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1647735
关键词：
Predicting MS Fragmentation using Density

项目摘要

This project proposed developing DFT from a complicated process involving multiple steps and pieces of software to a validated work-flow useful to a non-specialist working in the field of bioanalysis. This is essential to cope with the large amounts of data generated by bioanalytical tandem MS studies. The main elements of the work will be:Developing software for a semi-automated DFT based approach to predict observe MS fragmentation of metabolites. This will expand DFT based interpretation of tandem MS data from the realm of experts in MS to biochemical researchers involved in metabolomics and agrochemical metabolism. Developing quantitative measures of the agreement between predicted and observed spectra will be an essential part of this.Validation of the DFT approach for metabolite identification. With a semi-automated system in place, it will be possible to apply it to a wider range of biological samples including ones from both metabolomics and agrochemical metabolism studies. This will allow a broad assessment of the scope of DFT for bioanalytical studies.Build knowledge of agrochemical metabolism. The rate-limiting step in metabolism studies is identification. Once the DFT approach is validated we will apply it to a wider range of compounds and biological systems, contributing to better understanding of environmental impact, and the design of more sustainable agrochemicals.Identifying unknown endogenous metabolites. Metabolite profiling and natural products discovery projects often highlight significant numbers of unidentified endogenous metabolites. The system developed during the project will be used in these studies to speed up the identification process.

该项目提出了从复杂的过程开发DFT，该过程涉及多个步骤和软件到对生物分析领域的非专家有用的经过验证的工作流。这对于应对生物分析串联MS研究产生的大量数据至关重要。这项工作的主要要素是：为半自动化量的基于DFT的方法开发软件，以预测观察代谢物的MS片段化。这将扩大基于DFT的串联MS数据的解释，从MS专家领域到参与代谢组学和农业化学代谢的生化研究人员。制定预测光谱和观察光谱之间一致性的定量度量将是其中的重要组成部分。有了半自动系统，可以将其应用于更广泛的生物样品，包括来自代谢组学和农业化学代谢研究的生物样品。这将允许对DFT的生物分析研究范围进行广泛的评估。建立农业代谢的知识。代谢研究的限制步骤是鉴定。一旦验证了DFT方法，我们将其应用于更广泛的化合物和生物系统，有助于更好地了解环境影响，并设计更可持续的农业化学。代谢物分析和天然产品发现项目通常会突出大量未识别的内源性代谢产物。这些研究将在项目期间开发的系统来加快识别过程。