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Ultra-high resolution band-selective HSQC for nanomole-scale identification of chlorine-substituted 13 C in natural products drug discovery.

ABSTRACT: Ultra-high resolution band-selective HSQC (bsHSQC) has been employed for detection of 35 Cl-37 Cl isotope shifted 13 C NMR signals for assignment of regioisomerism in bromo-chloro natural products. Optimum pulse sequence and instrumental parameters for maximization of detection of the isotope shifts were explored. The chlorine isotope shifts (??) were detected within crosspeaks and were shown to vary with hybridization of 13 C, substitution of 13 C, presence of ?-chloro substituents, and their relative configuration. Deconvolution of Cl-substituted CH bsHSQC crosspeaks may provide other useful information, including a potentially MS-independent method for quantitating 37 Cl/35 C isotopic fractionation during the biosynthesis of halogenated natural products. Copyright © 2016 John Wiley & Sons, Ltd.

SUBMITTER: Wang X

PROVIDER: S-EPMC6411301 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Ultra-high resolution band-selective HSQC for nanomole-scale identification of chlorine-substituted 13 C in natural products drug discovery.

Wang Xiao X Duggan Brendan M BM Molinski Tadeusz F TF

Magnetic resonance in chemistry : MRC 20160307 4

Ultra-high resolution band-selective HSQC (bsHSQC) has been employed for detection of 35 Cl-37 Cl isotope shifted 13 C NMR signals for assignment of regioisomerism in bromo-chloro natural products. Optimum pulse sequence and instrumental parameters for maximization of detection of the isotope shifts were explored. The chlorine isotope shifts (Δδ) were detected within crosspeaks and were shown to vary with hybridization of 13 C, substitution of 13< ...[more]

PMID: 26951950

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Project description:Background: Metabolism is essential for cell survival and proliferation. A deep understanding of the metabolic network and its regulatory processes is often vital to understand and overcome disease. Stable isotope tracing of metabolism using nuclear magnetic resonance (NMR) and mass spectrometry (MS) is a powerful tool to derive mechanistic information of metabolic network activity. However, to retrieve meaningful information, automated tools are urgently needed to analyse these complex spectra and eliminate the bias introduced by manual analysis. Here, we present a data-driven algorithm to automatically annotate and analyse NMR signal multiplets in 2D- 1H, 13C-HSQC NMR spectra arising from 13C - 13C scalar couplings. The algorithm minimises the need for user input to guide the analysis of 2D- 1H, 13C-HSQC NMR spectra by performing automated peak picking and multiplet analysis. This enables non-NMR specialists to use this technology. The algorithm has been integrated into the existing MetaboLab software package. Methods: To evaluate the algorithm performance two criteria are tested: is the peak correctly annotated and secondly how confident is the algorithm with its analysis. For the latter a coefficient of determination is introduced. Three datasets were used for testing. The first was to test reproducibility with three biological replicates, the second tested the robustness of the algorithm for different amounts of scaling of the apparent J-coupling constants and the third focused on different sampling amounts. Results: The algorithm annotated overall >90% of NMR signals correctly with average coefficient of determination ρ of 94.06 ± 5.08%, 95.47 ± 7.20% and 80.47 ± 20.98% respectively. Conclusions: Our results indicate that the proposed algorithm accurately identifies and analyses NMR signal multiplets in ultra-high resolution 2D- 1H, 13C-HSQC NMR spectra. It is robust to signal splitting enhancement and up to 25% of non-uniform sampling.

Dataset Information

Ultra-high resolution band-selective HSQC for nanomole-scale identification of chlorine-substituted 13 C in natural products drug discovery.

Publications

Ultra-high resolution band-selective HSQC for nanomole-scale identification of chlorine-substituted <sup>13</sup> C in natural products drug discovery.

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