Natural Products Genomics

Explorative Natural product Genomics


Bacteria produce a wealth of small organic molecules to interact with their local environment. Many of these natural products have promising bioactivities and can be exploited in human and veterinary medicine for the treatment of a wide variety of ailments such as infectious diseases and cancer. Traditionally, natural products have been identified through extensive bioactivity-guided screening efforts focusing on few talented producer phyla that are frequently found in soil samples. These restrictions have resulted in the recurring discovery of already known natural products. As a result, the discovery pace of truly novel natural product scaffolds has slowed down significantly in the past decades. To circumvent the time-consuming rediscovery of known metabolites and to identify truly novel natural products in the post-genomics era, we employ and develop genomics-based workflows for the identification of novel bioactive natural products – a strategy called genome mining. 

Genome mining is an in-silico natural products discovery strategy in which sequenced genomes are analyzed for the presence of biosynthetic gene clusters responsible for the production of specialized metabolites. Despite the development of highly sophisticated bioinformatic tools, many natural product pathways remain unrecognized by state-of-the-art bioinformatic platforms. Moreover, for several natural product classes, no biosynthetic rules have been implemented that allow structural predictions based on genome sequence information. Our goal is to identify novel families of natural product biosynthetic gene clusters in the wealth of publicly available bacterial genomes and to characterize the associated products. We study the biosynthesis of these natural products and decipher the rules that govern their biosynthesis. These universal rules are then used for the development of bioinformatic tools for the identification of other members of the natural product family and the structural prediction of the associated metabolites.

TBG group members

  • Prof. Dr. Eric Helfrich (PI)
  • Sirinthra Thiengmag
  • Bin Tan
  • Yuya Kakumu
  • Milena Breitenbach
  • Friederike Biermann
  • Thao Ngoc Phan
  • Ayesha Ahmed


  • Dr. Pakjira Nanudorn
  • Dr. Sebastian Wenski

Group expertise / Methods

  • Genome Mining
  • Synthetic Biology
  • Heterologous Expression
  • (Imaging-) Mass Spectrometry
  • Chemical Ecology


Symbiotic bacteria
uncultured bacteria

TBG-related Publications

T. H. Fukuda, E. J. N. Helfrich, E. Mevers, W. G. P. Melo, E. B. Van Arnam, D. R. Andes, C. R. Currie, M. T. Pupo, J. Clardy. (2021). Specialized Metabolites Reveal Evolutionary History and Geographic Dispersion of a Multilateral Symbiosis. ACS Central Science 

Group webpage