Resources for biological research

Proteomics and biomarkersWhere the data analysis comes from, and what it was used on

• Journal publication Tsai W-Y, Giesbertz P, Breimann S et al. Multi-platform integration of brain and CSF proteomes reveals biomarker panels for Alzheimer's disease. Briefings in Bioinformatics 27, bbag012 (2026). 10.1093/bib/bbag012Several proteomics platforms compared and combined into one analysis.
• Journal publication Kardell O, Breimann S, Hauck SM. mpwR: an R package for comparing performance of mass spectrometry-based proteomic workflows. Bioinformatics 39, btad358 (2023). 10.1093/bioinformatics/btad358An R package for comparing proteomics workflows.
• Journal publication Bernhardt AM et al. A unified classification approach rating clinical utility of protein biomarkers across neurologic diseases. EBioMedicine 89, 104456 (2023). 10.1016/j.ebiom.2023.104456A scheme for rating how far a protein biomarker is clinically validated.

Membrane proteins and proteasesThe protein class the methods were built for

• Journal publication Steiner H, Breimann S, Kamp F, Frishman D. From mechanism to substratome: unravelling mysteries of γ-secretase. Journal of Biological Chemistry 302, 113287 (2026). 10.1016/j.jbc.2026.113287How the protease works, and which membrane proteins it cuts.
• Journal publication Jocher G, Grass V, Tschirner SK, Riepler L, Breimann S et al. ADAM10 and ADAM17 promote SARS-CoV-2 cell entry and spike protein-mediated lung cell fusion. EMBO Reports 23, e54305 (2022). 10.15252/embr.202154305Two membrane proteases cut the viral spike protein and let lung cells fuse.
• Journal publication Hönigschmid P, Breimann S et al. AllesTM: predicting multiple structural features of transmembrane proteins. BMC Bioinformatics 21, 242 (2020). 10.1186/s12859-020-03581-8Predicts depth in the membrane, topology and other structural features.

With our advisorsResearch co-authored by our advisors, grouped by their scientific expertise

Protein language models

• Journal publication With our advisor, Dr Michael HeinzingerElnaggar A, Heinzinger M et al. ProtTrans: toward understanding the language of life through self-supervised learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 7112 (2022). 10.1109/TPAMI.2021.3095381
• Journal publication With our advisor, Dr Michael HeinzingerSchmirler R, Heinzinger M et al. Fine-tuning protein language models boosts predictions across diverse tasks. Nature Communications 15, 7407 (2024). 10.1038/s41467-024-51844-2
• Journal publication With our advisor, Dr Michael HeinzingerBordin N, Dallago C, Heinzinger M et al. Novel machine learning approaches revolutionize protein knowledge. Trends in Biochemical Sciences 48, 345 (2023). 10.1016/j.tibs.2022.11.001

Protein structure prediction and interaction data

• Journal publication With our advisor, Prof. Dmitrij FrishmanHeinig M, Frishman D. STRIDE: a web server for secondary structure assignment from known atomic coordinates of proteins. Nucleic Acids Research 32, W500 (2004). 10.1093/nar/gkh429
• Journal publication With our advisor, Prof. Dmitrij FrishmanPagel P et al. The MIPS mammalian protein-protein interaction database. Bioinformatics 21, 832 (2005). 10.1093/bioinformatics/bti115
• Journal publication With our advisor, Prof. Dmitrij FrishmanFrishman D, Argos P. Knowledge-based protein secondary structure assignment. Proteins 23, 566 (1995). 10.1002/prot.340230412

Agentic AI and knowledge graphs

• Journal publication With our advisor, Dr Sebastian LobentanzerLobentanzer S et al. A platform for the biomedical application of large language models. Nature Biotechnology 43, 166 (2025). 10.1038/s41587-024-02534-3
• Journal publication With our advisor, Dr Sebastian LobentanzerLobentanzer S, Rodriguez-Mier P, Bauer S, Saez-Rodriguez J. Molecular causality in the advent of foundation models. Molecular Systems Biology 20, 848 (2024). 10.1038/s44320-024-00041-wWhat a foundation model does and does not settle about cause.
• Journal publication With our advisor, Dr Sebastian LobentanzerLobentanzer S et al. Democratizing knowledge representation with BioCypher. Nature Biotechnology 41, 1056 (2023). 10.1038/s41587-023-01848-y

Protein design, and seeing one molecule at a time

• Journal publication With our advisor, Prof. Thomas SchlichthärleMuratspahić E et al. De novo design of miniproteins targeting GPCRs. Nature 656, 1044 (2026). 10.1038/s41586-026-10656-8
• Journal publication With our advisor, Prof. Thomas SchlichthärleYang W et al. The past, present and future of de novo protein design. Nature 652, 1139 (2026). 10.1038/s41586-026-10328-7
• Journal publication With our advisor, Prof. Thomas SchlichthärleSchlichthärle T, Lindner C, Jungmann R. Super-resolved visualization of single DNA-based tension sensors in cell adhesion. Nature Communications 12, 2510 (2021). 10.1038/s41467-021-22606-1Single force sensors, resolved one molecule at a time where a cell grips.