Predicting MS/MS Fragmentation using Density Function Theory
Lead Research Organisation:
University of Southampton
Department Name: Sch of Chemistry
Abstract
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.
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.
People |
ORCID iD |
| Graham Langley (Primary Supervisor) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| BB/N504087/1 | 01/10/2015 | 30/09/2019 | |||
| 1647735 | Studentship | BB/N504087/1 | 01/10/2015 | 30/09/2019 |
| Description | A paper has been submitted but required some corrections after which it could be considered again for a publication. I had a viva examination and submitted corrected version of my thesis. |
| Exploitation Route | The findings might be used in industry for faster and improved metabolite or any other small molecule identification. |
| Sectors | Agriculture, Food and Drink,Chemicals,Pharmaceuticals and Medical Biotechnology |
| Description | iCASE PhD |
| Organisation | Syngenta International AG |
| Department | Syngenta Ltd (Bracknell) |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | During my placement I have used my knowledge and skills to answer some structure elucidation questions asked at Syngenta. Due to the confidentiality agreement I could not disclose the details of that contribution. I transfer my knowledge on the application of computational modelling for the prediction of tandem mass spectra to some employees of the company. |
| Collaborator Contribution | Syngenta provided me with access to instrumentation, software and very knowledgeable supervisors from the company. The guidance, help, support and knowledge which I gained from my placement and knowledge exchange with the supervisory team is immense. |
| Impact | - poster presentations - publications in preparation - knowledge transfer |
| Start Year | 2015 |
| Description | Twilight event at the University of Southampton, Open days at the University of Southampton |
| Form Of Engagement Activity | Participation in an open day or visit at my research institution |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Schools |
| Results and Impact | I had supporting my University at an open day and described my research project to potential students and the exciting opportunities its provides. I was involved in a Twilight events for school kids too. The students were involved in the analysis of compounds using analytical methods and I shared my experience with them on what is like to conduct research in mass spectrometry. |
| Year(s) Of Engagement Activity | 2016,2017,2018 |