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Prof. Dr. Nadine Ziemert

CMFI Executive Board Member | Equal Opportunities Officer


University of Tübingen
Interfaculty Institute of Microbiology and Infection Medicine Tübingen
Translational Genome Mining for Natural Products

Tel.: +49 7071 29-78841

Research Interest

Nadine Ziemert's research interests include the evolution of antibiotics as well as the genomics of their producers and the development of bioinformatics tools. In her research group "Translational Genome Mining for Natural Products" at the Interfaculty Institute for Microbiology and Infection Medicine at the University of Tübingen, she focuses on the evolution and distribution of bacterial secondary metabolites. She is particularly interested in secondary metabolites with antibacterial properties. Using the bioinformatic tools developed in her research group she was able to show that there are still unexplored genera within the bacterial kingdom with a high genomic potential to produce specialized metabolites, and that a large part of this potential lies in bacteria that cannot yet be cultivated.

The research group follows the guiding principle of Open Access and makes its data, protocols and computer-aided tools freely available.

Tools Website Research Group Ziemert


About Nadine Ziemert

Nadine Ziemert studied biology at Humboldt University in Berlin, where she received her Ph.D. in 2009. She then spent several years as a postdoctoral researcher at the Scripps Institute of Oceanography in California. She also worked there as a project scientist in 2013-14. In 2014, she returned to Germany and accepted a professorship in Translational Natural Product Genomics at the University of Tübingen. Since 2019, she is a member of the CMFI Board of Directors and the Cluster's Equal Opportunities Officer.


Selected Publications

Sehnal L, Lo Presti L, Ziemert N. (2024) Discovering cryptic natural products by substrate manipulation. Nat Chem. doi: 10.1038/s41557-023-01433-5.

Hansen MH, Adamek M, Iftime D, Petras D, Schuseil F, Grond S, Stegmann E, Cryle MJ, Ziemert N. (2023) Resurrecting ancestral antibiotics: unveiling the origins of modern lipid II targeting glycopeptides. Nat Commun. 14(1):7842. doi: 10.1038/s41467-023-43451-4.

Mullowney MW, {...}, Ziemert N, Goss RJM, Guyomard P, Volkamer A, Gerwick WH, Kim HU, Müller R, van Wezel GP, van Westen GJP, Hirsch AKH, Linington RG, Robinson SL, Medema MH. (2023) Artificial intelligence for natural product drug discovery. Nat Rev Drug Discov. 22:895–916. doi: 10.1038/s41573-023-00774-7.

Yilmaz TM, Mungan MD, Berasategui A, Ziemert N. (2023) FunARTS, the Fungal bioActive compound Resistant Target Seeker, an exploration engine for target-directed genome mining in fungi. Nucleic Acids Res. gkad386. doi: 10.1093/nar/gkad386.

Bär D, Konetschny B, Kulik A, Xu H, Paccagnella D, Beller P, Ziemert N, Dickschat JS, Gust B. (2022) Origin of the 3-methylglutaryl moiety in caprazamycin biosynthesis. Microb Cell Fact. 21(1): 232. doi: 10.1186/s12934-022-01955-6.

Mungan M, Harbig T, Perez Naybel, Edenhart  S, Stegmann E, Nieselt K, Ziemert N. (2022) Secondary Metabolite Transcriptomic Pipeline (SeMa-Trap), an expression-based exploration tool for increased secondary metabolite production in bacteria. NAR 50(W1): 682–689. doi: 10.1093/nar/gkac371.

Berasategui A, Breitenbach N, García-Lozano M, Pons I, Sailer B, Lanz C, Rodriguez V, Hipp K, Ziemert N, Windsor D, Salem H. (2022) The leaf beetle Chelymorpha alternans propagates a plant pathogen in exchange for pupal protection. Current Biology. 32(19): 4114–4127. doi:

Gavriilidou A, Kautsar SA, Zaburannyi N, Krug D, Müller R, Medema MH, Ziemert N. (2022) Compendium of specialized metabolite biosynthetic diversity encoded in bacterial genomes. Nat Microbiol. 7(5):726-735. doi: 10.1038/s41564-022-01110-2.

Chevrette MG, Gavrilidou A, Mantri S, Selem-Mojica N, Ziemert N, Barona-Gómez F. The confluence of big data and evolutionary genome mining for the discovery of natural products. Nat. Prod. Rep. 38: 2024–2040. (2021) doi: 10.1039/d1np00013f.

Mungan MD, Blin K, Ziemert N. ARTS-DB: a database for antibiotic resistant targets. Nucleic Acids Res. 50(D1): D736–D740. (2021) doi: 10.1093/nar/gkab940


You can find a complete list of publications here