Dereplication of Fungal Metabolites by NMR-Based Compound Networking Using MADByTE

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Nicholas Oberlies, Patricia A. Sullivan Distinguished Professor of Chemistry (Creator)
Huzefa A. Raja, Research Scientist (Creator)
The University of North Carolina at Greensboro (UNCG )
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Abstract: Strategies for natural product dereplication are continually evolving, essentially in lock step with advances in MS and NMR techniques. MADByTE is a new platform designed to identify common structural features between samples in complex extract libraries using two-dimensional NMR spectra. This study evaluated the performance of MADByTE for compound dereplication by examining two classes of fungal metabolites, the resorcylic acid lactones (RALs) and spirobisnaphthalenes. First, a pure compound database was created using the HSQC and TOCSY data from 19 RALs and 10 spirobisnaphthalenes. Second, this database was used to assess the accuracy of compound class clustering through the generation of a spin system feature network. Seven fungal extracts were dereplicated using this approach, leading to the correct prediction of members of both families from the extract set. Finally, NMR-guided isolation led to the discovery of three new palmarumycins (20–22). Together these results demonstrate that MADByTE is effective for the detection of specific compound classes in complex mixtures and that this detection is possible for both known and new natural products.

Additional Information

Journal of Natural Products 2022 85 (3), 614-624. DOI: 10.1021/acs.jnatprod.1c00841
Language: English
Date: 2022
isolation, quantum mechanics, chemical structure, nuclear magnetic resonance spectroscopy, metabolism

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