Automated analysis for multiplet identification from ultra-high resolution 2D- ¹H, ¹³C-HSQC NMR spectra.

ABSTRACT: 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 minimis

SUBMITTER: Ferrante L 

PROVIDER: S-EPMC10050905 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature