Open AccessDevelopment of an NMR-Based Platform for the Direct Structural Annotation of Complex Natural Products Mixtures
Author(s) -
Joseph M. Egan,
Jeffrey A. van Santen,
Dennis Y. Liu,
Roger G. Linington
Publication year - 2021
Publication title -
journal of natural products
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.976
H-Index - 139
eISSN - 1520-6025
pISSN - 0163-3864
DOI - 10.1021/acs.jnatprod.0c01076
Subject(s) - metabolomics , similarity (geometry) , prioritization , computer science , nmr spectra database , nuclear magnetic resonance spectroscopy , biological system , chemistry , biochemical engineering , computational biology , artificial intelligence , physics , stereochemistry , engineering , biology , spectral line , chromatography , management science , astronomy , image (mathematics)
The development of new "omics" platforms is having a significant impact on the landscape of natural products discovery. However, despite the advantages that such platforms bring to the field, there remains no straightforward method for characterizing the chemical landscape of natural products libraries using two-dimensional nuclear magnetic resonance (2D-NMR) experiments. NMR analysis provides a powerful complement to mass spectrometric approaches, given the universal coverage of NMR experiments. However, the high degree of signal overlap, particularly in one-dimensional NMR spectra, has limited applications of this approach. To address this issue, we have developed a new data analysis platform for complex mixture analysis, termed MADByTE (Metabolomics and Dereplication by Two-Dimensional Experiments). This platform employs a combination of TOCSY and HSQC spectra to identify spin system features within complex mixtures and then matches spin system features between samples to create a chemical similarity network for a given sample set. In this report we describe the design and construction of the MADByTE platform and demonstrate the application of chemical similarity networks for both the dereplication of known compound scaffolds and the prioritization of bioactive metabolites from a bacterial prefractionated extract library.