Maximilian Stammnitz

Maximilian StammnitzMaximilian Stammnitz

Quantitative Cell Biology

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Quantitative Cell Biology
Independent Fellow
Maximilian Stammnitz

Quantitative Cell Biology

Independent Fellow
Maximilian Stammnitz

Biosketch

2027, Independent Fellow, CRG (Spain) and AR+I (Andorra)
2026, Staff Scientist, Ferruz Lab, CRG (Spain)
2022-2026, EMBO and Marie Skłodowska-Curie Postdoc Fellow, CRG (Spain)
2015-2022, PhD and Bridge Postdoc in Genomics, University of Cambridge (UK).
2014-2015, MPhil in Computational Biology, University of Cambridge (UK)
2011-2014, BSc in Biology, University of Freiburg (Germany)

Summary

How do proteins evolve to sense environmental chemistry with extreme specificity and sensitivity? Can we decode the molecular determinants by which these structures are tuned and engineered? In our group, we perform large-scale protein library screens to determine proteins’ small molecule interaction profiles (e.g. see Stammnitz & Lehner, Nature Communications 2026). We test thousands of hormone receptors from plants to humans, as well as anti-fungal and ‘molecular glue’ compounds which chemically modulate their protein targets. With the help of these functional genomics experiments, AI-enhanced evolutionary inference and protein structure modelling, our goal is to design a range of new biosensors for the scalable monitoring of invisible chemicals – this holds key applications for One Health.
Through this approach, we also investigate the role of repetitive DNA into the generation of structural variants, a variant class encompassing large genetic changes, including deletions, insertions, inversions or duplications of DNA sequences (Cosenza et al., 2022). In this context, we studied the activity of L1 retroelements in the largest cohort of cancer whole genomes available at that time (Rodriguez-Martin et al., 2020; PCAWG Consortium 2020), uncovering a novel mutational mechanism of structural variation formation in cancer caused by L1 retrotransposition. Together with colleagues from the Human Genome Structural Variation Consortium (HGSVC), we have also pioneered the use of genome assembly approaches to enhance the discovery of polymorphic structural variants and characterize mobile elements (Ebert et al., 2021). This includes inversions (Porubsky et al., 2022), which have been particularly challenging to detect using previous technologies, as they are typically flanked by large repeats known as segmental duplications.

We are a small and multidisciplinary team. Together with Hannah Benisty’s group and our partners at AR+I, we are the founding team of the new joint green biotechnology and environmental monitoring initiative between CRG and the Principality of Andorra.

Job Openings

We are recruiting PhD candidates via CRG’s international call. Please get in touch by email if you would like to hear more about our research opportunities, or if you are considering joining in another role (e.g. for a Master’s thesis). We encourage self-motivated candidates and are always happy to discuss your own project ideas in the context of chemical biology, protein engineering, experimental and computational genomics.

Group Leader

SELECTED PUBLICATIONS:

 

**Joint-first Author
§Corresponding Author

 

R. Stammnitz, B. Lehner§
The genetic architecture of an allosteric hormone receptor.
O. Nousias*, M. McCauley*, M. R. Stammnitz*, …, D. J. Duffy§,
Shotgun sequencing of airborne eDNA achieves rapid assessment of whole biomes, population genetics and genomic variation
M. R. Stammnitz§, A. Hartman Scholz, D. J. Duffy
Environmental DNA without borders
M. R. Stammnitz, K. Gori, Y. M. Kwon, …, E. P. Murchison§,
The evolution of two transmissible cancers in Tasmanian devils.
L. Urban*,§, A. Holzer*,§, J. J. Baronas, …, M. R. Stammnitz§,
Freshwater monitoring by nanopore sequencing.
M. R. Stammnitz, T. H. H. Coorens, K. Gori, …, E. P. Murchison§,
The origins and vulnerabilities of two transmissible cancers in Tasmanian devils.

Software:

nf-core/deepmutscan: a reproducible, scalable, and community-curated pipeline for analyzing deep mutational scanning (DMS) data using shotgun DNA sequencing