Automated genome mining for natural products

Michael Ht Li,David H Sherman,Peter Mu Ung,Sylvie Garneau-Tsodikova,James Zajkowski

doi:10.1186/1471-2105-10-185

Michael Ht Li, David H Sherman + Show 3 more

Open Access

https://doi.org/10.1186/1471-2105-10-185

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Journal: BMC Bioinformatics	Publication Date: Jun 16, 2009
Citations: 261	License type: cc-by

Affiliation: University of Michigan–Ann Arbor

Abstract

BackgroundDiscovery of new medicinal agents from natural sources has largely been an adventitious process based on screening of plant and microbial extracts combined with bioassay-guided identification and natural product structure elucidation. Increasingly rapid and more cost-effective genome sequencing technologies coupled with advanced computational power have converged to transform this trend toward a more rational and predictive pursuit.ResultsWe have developed a rapid method of scanning genome sequences for multiple polyketide, nonribosomal peptide, and mixed combination natural products with output in a text format that can be readily converted to two and three dimensional structures using conventional software. Our open-source and web-based program can assemble various small molecules composed of twenty standard amino acids and twenty two other chain-elongation intermediates used in nonribosomal peptide systems, and four acyl-CoA extender units incorporated into polyketides by reading a hidden Markov model of DNA. This process evaluates and selects the substrate specificities along the assembly line of nonribosomal synthetases and modular polyketide synthases.ConclusionUsing this approach we have predicted the structures of natural products from a diverse range of bacteria based on a limited number of signature sequences. In accelerating direct DNA to metabolomic analysis, this method bridges the interface between chemists and biologists and enables rapid scanning for compounds with potential therapeutic value.

Highlights

Discovery of new medicinal agents from natural sources has largely been an adventitious process based on screening of plant and microbial extracts combined with bioassayguided identification and natural product structure elucidation
Genome scanning was subsequently recognized as a means of discovering useful secondary metabolites such as nonribosomal peptides (NRPs), polyketides (PKs), and terpenoids [4]
The breadth of recognition We examined 79 natural product gene clusters from the databases NORINE and ASMPKS that produced metabolites with accessible structural information

Summary

Introduction

Discovery of new medicinal agents from natural sources has largely been an adventitious process based on screening of plant and microbial extracts combined with bioassayguided identification and natural product structure elucidation. Scientists today still approach identification of medicinal compounds by screening chemicals for their ability to inhibit cellular growth This was a landmark step for science compared to using medicines passed down through collective human experience, but one from which we have not yet significantly advanced [2]. The arrival of a powerful graphic user interface (GUI) in the mid 1990s coincided with the first whole genome sequence of the microorganism Haemophilus influenza [3]. This synergy of computational technology and molecular biology was the beginning of whole genome analysis aiding drug discovery by searching for robust protein targets. Genome scanning was subsequently recognized as a means of discovering useful secondary metabolites such as nonribosomal peptides (NRPs), polyketides (PKs), and terpenoids [4]

Results

Discussion

Conclusion