Please join us on Tuesday, December 11th, 2018 at Northwestern University, Ryan Hall 4003. Dinner starts at 6:30 PM with the seminar immediately following at 7:00 PM. Please be sure to register so our sponsors know how much food to order! We hope you can join us! To register click here
Speaker: Lloyd Sumner from the University of Missouri, Columbia
Title: Novel Computational Tools for Metabolite Identity Prediction coupled to Sophisticated Experimental Tools to Prove Metabolite Structure
Abstract: The number one grand challenge that the metabolomics community faces is the confident identification of all the peaks and spectra observed in non-targeted metabolomics experiments. Identification is foundational to translating the raw data into biochemical information and context. We are attacking this grand challenge using both computational approaches to predict metabolite identity and then prove metabolite identities with sophisticated instrumental ensembles. More specifically I will describe a machine learning tool entitled MetExpert for predicting metabolite identities based upon EI and CI GC-QTofMS data. I’ll also describe another computational tool entitled PlantMAT that we use for identifying plant metabolites in UHPLC-QTofMS/MS datasets. It is important to realize that the outcomes of both computational tools are predictions of metabolite identity. We then confirm these identifications or prove structures using a sophisticated instrumentation suite composed of UHPLC-MS-SPE for automated purification and concentration of target analytes which we then analyze via 1D and 2D NMR. Another grand challenge we face is increasing our metabolome depth of coverage. We are far from comprehensive, but are pushing our depth of coverage by using multi-dimensional separations composed of UHPLC coupled to trapped ion mobility spectrometry (TIMS). This highly complementary two-dimensional UHPLC-TIMS separation platform is further coupled to tandem QTofMS/MS for greater depth of coverage. The cumulative UHPLC-TIMS-QToFMS/MS system provides an additional dimension of information by determining a compounds collisional cross section measurement, relative to UHPLC-QTofMS/MS, that can be further used as orthogonal data for increasing our metabolite identification confidence.