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Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

Published on: March 12, 2020

Rationally minimizing natural product libraries using mass spectrometry.

Monica Ness^1,2, Thilini Peramuna¹, Karen L Wendt¹

¹Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma, USA.

|February 24, 2025

Summary

This study introduces a mass spectrometry method to reduce natural product libraries for drug discovery. This approach accelerates screening, cuts costs, and improves bioassay hit rates for novel therapeutics.

Keywords:

computational techniques drug discovery fungi mass spectrometry metabolomics specialized metabolites

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Area of Science:

Drug Discovery
Natural Products Chemistry
Mass Spectrometry

Background:

Natural products are vital for novel drug discovery, but large, redundant libraries hinder high-throughput screening (HTS).
Structural redundancy and re-discovery in natural product libraries increase screening time and costs.
Existing natural product libraries face challenges in efficient HTS implementation.

Purpose of the Study:

To develop a method for reducing natural product library size using mass spectrometry.
To minimize bioactive loss while decreasing library volume for cost-effective drug discovery.
To enhance bioassay hit rates and accelerate the identification of drug candidates.

Main Methods:

Leveraged liquid chromatography-tandem mass spectrometry (LC-MS/MS) for spectral similarity analysis.
Applied the method to a fungal extract collection to demonstrate library size reduction.
Validated the approach for its broad applicability across different assays and natural product sources.

Main Results:

Successfully reduced natural product library size with minimal loss of bioactive compounds.
Achieved increased bioassay hit rates against microbial targets.
Demonstrated broad applicability across various assays and natural product origins.

Conclusions:

The developed LC-MS/MS spectral similarity method significantly reduces natural product library size.
This strategy accelerates cost-effective natural product drug discovery.
The method offers a broadly applicable solution for improving HTS efficiency.