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Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products
Published on: March 12, 2020
Automated genome mining for natural products
Michael H T Li1, Peter M U Ung, James Zajkowski
1Life Sciences Institute, University of Michigan, Ann Arbor, MI, USA. mikeleez@umich.edu
BMC Bioinformatics
|June 18, 2009
Summary
This study introduces a new computational method to predict natural product structures directly from bacterial genome sequences. This approach accelerates the discovery of novel therapeutic compounds from microbial natural products.
Area of Science:
- Computational chemistry
- Genomics
- Natural product discovery
Background:
- Traditional natural product discovery relies on screening and bioassays.
- Genomic and computational advancements enable a more rational approach.
Purpose of the Study:
- To develop a rapid computational method for predicting natural product structures from genome sequences.
- To bridge the gap between genomics and metabolomics for drug discovery.
Main Methods:
- Developed an open-source, web-based program to scan genome sequences.
- Utilizes hidden Markov models to identify substrate specificities in biosynthetic gene clusters.
- Assembles small molecules from defined building blocks for polyketides and nonribosomal peptides.
Main Results:
- Successfully predicted natural product structures from diverse bacteria using signature sequences.
- Generated output readily convertible to 2D and 3D structures.
- Demonstrated a method for direct DNA to metabolomic analysis.
Conclusions:
- The developed method accelerates the identification of potential therapeutic compounds.
- Facilitates rapid scanning of bacterial genomes for novel natural products.
- Enhances collaboration between chemists and biologists in drug discovery.
