Automated genome mining for natural products

Michael H.T. Li, Peter M.U. Ung, James Zajkowski, Sylvie Garneau-Tsodikova, David H. Sherman

Research output: Contribution to journalArticlepeer-review

210 Scopus citations


Background: Discovery 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. Results: We 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. Conclusion: Using 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.

Original languageEnglish
Article number185
JournalBMC Bioinformatics
StatePublished - Jun 16 2009

Bibliographical note

Funding Information:
This work was supported by NIH grant GM076477 and the Hans W. Vahl-teich Professorship (to D.H.S), the Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, and an NIH Cellular Biotechnology Training Grant.

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics


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