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Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

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A Structure-Guided Molecular Network Strategy for Global Untargeted Metabolomics Data Annotation.

Xinxin Wang^1,2,3, Chao Li^1,4,2,3, Zaifang Li^1,2,3

¹CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P.R. China.

Analytical Chemistry

|July 26, 2023

Summary

This summary is machine-generated.

A new structure-guided molecular network strategy (SGMNS) enhances metabolite annotation in untargeted metabolomics. This method improves deep annotation accuracy by leveraging chemical structure similarity for spectral prediction.

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Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

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Published on: January 7, 2019

Area of Science:

Metabolomics
Bioinformatics
Analytical Chemistry

Background:

Large-scale metabolite annotation is a significant challenge in untargeted metabolomics.
Current network-based methods face limitations due to insufficient reference MS/MS spectra.

Purpose of the Study:

To develop a structure-guided molecular network strategy (SGMNS) for deep annotation of untargeted ultra-performance liquid chromatography-high resolution mass spectrometry (MS) metabolomics data.
To overcome the limitations of existing annotation methods by utilizing chemical structure information.

Main Methods:

Constructed a global connectivity molecular network (GCMN) based on molecular fingerprint similarity of chemical structures.
Employed network annotation propagation using known metabolites as seeds, assigning 'pseudo' spectra to neighbors.
Iteratively propagated annotations by searching predicted retention times, MS1, and 'pseudo' spectra against experimental data.

Main Results:

SGMNS demonstrated unique advantages for metabolome annotation.
Successfully annotated a large number of metabolites across diverse biological samples (cells, feces, plasma, tissue, urine) with high accuracy (>83%) and low variability (RSD <2%).
Annotated 701 (cell), 1557 (feces), 1147 (plasma), 1095 (tissue), 1237 (urine), and 2041 (pooled) metabolites.

Conclusions:

SGMNS effectively exploits the chemical space of existing metabolomes for deep metabolite annotation.
The strategy overcomes the bottleneck of insufficient reference MS/MS spectra in untargeted metabolomics.
This approach significantly advances the capability for comprehensive metabolite identification.