Natural products are often biosynthesized as mixtures of structurally similar compounds, rather than a single compound. Due to their common structural features, many compounds within the same class undergo similar MS/MS fragmentation and have several identical product ions and/or neutral losses. The purpose of diagnostic fragmentation filtering (DFF) is to efficiently detect all compounds of a given class in a complex extract by screening non-targeted LC-MS/MS datasets for MS/MS spectra that contain class specific product ions and/or neutral losses. This method is based on a DFF module implemented within the open-source MZmine platform that requires sample extracts be analyzed by data-dependent acquisition on a high-resolution mass spectrometer such as quadrupole Orbitrap or quadrupole time-of-flight mass analyzers. The main limitation of this approach is the analyst must first define which product ions and/or neutral losses are specific for the targeted class of natural products. DFF allows for the subsequent discovery of all related natural products within a complex sample, including new compounds. In this work, we demonstrate the effectiveness of DFF by screening extracts of Microcystis aeruginosa, a prominent harmful algal bloom causing cyanobacteria, for the production of microcystins.

Additional Metadata
Persistent URL dx.doi.org/10.3791/59712
Journal Journal of visualized experiments
Citation
McMullin, D, Hoogstra, S. (Shawn), McDonald, K.P. (Kimberlynn P.), Sumarah, M.W. (Mark W.), & Renaud, J.B. (Justin B.). (2019). Natural Product Discovery with LC-MS/MS Diagnostic Fragmentation Filtering: Application for Microcystin Analysis. Journal of visualized experiments, (147). doi:10.3791/59712