MixONat, a Software for the Dereplication of Mixtures Based on ¹³C NMR Spectroscopy

Abstract

Whether chemists or biologists, researchers dealing with metabolomics require tools to decipher complex mixtures. As a part of metabolomics and initially dedicated to identifying bioactive natural products, dereplication aims at reducing the usual timeconsuming process of known compounds isolation. Mass spectrometry and nuclear magnetic resonance are the most commonly reported analytical tools during dereplication analysis. Though it has low sensitivity, 13 C NMR has many advantages for such a study. Notably, it is nonspecific allowing simultaneous high-resolution analysis of any organic compounds including stereoisomers. Since NMR spectrometers nowadays provide useful data sets in a reasonable time frame, we have embarked upon writing software dedicated to 13 C NMR dereplication. The present study describes the development of a freely distributed algorithm, namely MixONat and its ability to help researchers decipher complex mixtures. Based on Python 3.5, MixONat analyses a { 1 H}-13 C NMR spectrum optionally combined with DEPT-135 and 90 datato distinguish carbon types (i.e., CH 3 , CH 2 , CH, and C)as well as a MW filtering. The software requires predicted or experimental carbon chemical shifts (δc) databases and displays results that can be refined based on user interactions. As a proof of concept, this 13

Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches

“…The latest developments in nuclear magnetic resonance (NMR) spectroscopy experimentation and instrumentation have led to a signicant increase in the use of NMR-based metabolomics for the dereplication of natural products. 14 Recently, several innovative 1D NMR-based tools have emerged such as Mix-ONat 74 and CARAMEL, 75 just to name a few; however, the identication of compounds within complex mixtures using NMR spectroscopy is still challenging mainly due to NMR signal overlap that masks lower abundance signals and distorts signal patterns and their quantitative areas. [74][75][76] As a way to overcome these resolution issues, deconvolution of signals has been considered to be an essential step for NMR-based identication of metabolites in complex mixtures.…”

“…14 Recently, several innovative 1D NMR-based tools have emerged such as Mix-ONat 74 and CARAMEL, 75 just to name a few; however, the identication of compounds within complex mixtures using NMR spectroscopy is still challenging mainly due to NMR signal overlap that masks lower abundance signals and distorts signal patterns and their quantitative areas. [74][75][76] As a way to overcome these resolution issues, deconvolution of signals has been considered to be an essential step for NMR-based identication of metabolites in complex mixtures. As such, Diffusion Ordered SpectroscopY (DOSY) 77 and Statistical TOtal Correlation Spec-troscopY (STOCSY) 78 are key methods for spectral deconvolution in NMR-based metabolomics.…”

13C NMR Dereplication Using MixONat Software: A Practical Guide to Decipher Natural Products Mixtures

“…The { 1 H}- 13 C NMR spectrum (1024 scans) of peppermint EO (90 mg) was recorded in CDCl 3 (0.6 mL) using a routine 400 MHz NMR spectrometer. Though not mandatory for the software, DEPT-135 and DEPT-90 spectra were also recorded since carbon multiplicity was previously shown as a powerful discriminant filter [11]. From there, the present 13 C NMR-based dereplication process requires 30 to 60 min only until completion.…”

Synthetic biology approaches to actinomycete strain improvement

“…Lately, 13 C NMR-based tools have also been developed to dereplicate compounds in mixtures (MixONat; Bruguière et al . 2020 ) or the prediction of natural product classes (Martínez-Treviño et al . 2020 ).…”