Deciphering Complex Metabolite Mixtures by Unsupervised and Supervised Substructure Discovery and Semi-Automated Annotation from MS/MS Spectra
1:00pm - 2:00pm /
Monday 28th October 2019
/ Venue: LT1 Life Sciences Building
- Andy Jones
- Suitable for: Those interested in Genomes, Systems and Therapeutic Targeting
- Admission: Free
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Speaker: Simon Rogers (University of Glasgow)
Mass spectrometry (MS) is a widely used technique to obtain structural information on complex mixtures. We recently proposed a substructure-based strategy to enhance metabolite annotation for complex mixtures by considering metabolites as the sum of (bio)chemically relevant moieties that we can detect through mass spectrometry fragmentation approaches. Our MS2LDA tool allows us to discover - unsupervised - groups of mass fragments and/or neutral losses termed Mass2Motifs that often correspond to substructures. A drawback is that Mass2Motifs have to be manually annotated. I will describe how additional strategies, taking advantage of i) combinatorial in-silico matching of experimental mass features to substructures of candidate molecules, and ii) automated machine learning classification of molecules, can facilitate semi-automated annotation of substructures, and enhance structural characterisation of metabolites.
Mass spectrometry (MS) is a widely used technique to obtain structural information on complex mixtures. We recently proposed a substructure-based strategy to enhance metabolite annotation for complex mixtures by considering metabolites as the sum of (bio)chemically relevant moieties that we can detect through mass spectrometry fragmentation approaches. Our MS2LDA tool allows us to discover - unsupervised - groups of mass fragments and/or neutral losses termed Mass2Motifs that often correspond to substructures. A drawback is that Mass2Motifs have to be manually annotated. I will describe how additional strategies, taking advantage of i) combinatorial in-silico matching of experimental mass features to substructures of candidate molecules, and ii) automated machine learning classification of molecules, can facilitate semi-automated annotation of substructures, and enhance structural characterisation of metabolites.