Small Molecule Accurate Recognition Technology (SMART) to Enhance Natural Products Research

TitleSmall Molecule Accurate Recognition Technology (SMART) to Enhance Natural Products Research
Publication TypeJournal Article
Year of Publication2017
AuthorsZhang C., Idelbayev Y., Roberts N., Tao Y.W, Nannapaneni Y., Duggan BM, Min J., Lin E.C, Gerwick E.C, Cottrell G.W, Gerwick WH
JournalScientific Reports
Volume7
Date Published2017/10
Type of ArticleArticle
ISBN Number2045-2322
Accession NumberWOS:000413907000066
Keywordscyanobacterium lyngbya-majuscula; cyclic; depsipeptides; derivatives; gene-cluster; marine; multidimensional nmr; neural-networks; saponins; spectra; spectroscopy
Abstract

Various algorithms comparing 2D NMR spectra have been explored for their ability to dereplicate natural products as well as determine molecular structures. However, spectroscopic artefacts, solvent effects, and the interactive effect of functional group(s) on chemical shifts combine to hinder their effectiveness. Here, we leveraged Non-Uniform Sampling (NUS) 2D NMR techniques and deep Convolutional Neural Networks (CNNs) to create a tool, SMART, that can assist in natural products discovery efforts. First, an NUS heteronuclear single quantum coherence (HSQC) NMR pulse sequence was adapted to a state-of-the-art nuclear magnetic resonance (NMR) instrument, and data reconstruction methods were optimized, and second, a deep CNN with contrastive loss was trained on a database containing over 2,054 HSQC spectra as the training set. To demonstrate the utility of SMART, several newly isolated compounds were automatically located with their known analogues in the embedded clustering space, thereby streamlining the discovery pipeline for new natural products.

DOI10.1038/s41598-017-13923-x
Short TitleSci Rep
Student Publication: 
No
Research Topics: 
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