Associate Professor
Martin Steinegger
Research
Bioinformatics
Leveraging the power of big data and machine learning algorithms, our lab builds computational methods to provide unprecedented insights into complex genomic and proteomic data. Our mission is to accelerate biological science through the development of fast and easy-to-use computational methods. Our tools, including the protein structure prediction methods AlphaFold2 and ColabFold, sequence analysis methods Linclust, Plass, and MMseqs2, and structure analysis method Foldseek, which have been installed over 800,000 times and used over 10 million times through our web services. Our methods are not just tools but catalysts for advancements in biological sciences worldwide.
Education/Career/Awards
Education
- - 2014 - 2018 Ph.D. in Computer Science at the Technical University Munich
- - 2013 - 2014 Master of Science in Computer Science at the Ludwig Maximilian University
- - 2010 - 2013 Bachelor of Science in Bioinformatics at TU Munich / Ludwig Maximilian University
Career
- - since 2024 Associate Professor(tenured), School of Biological Sciences, Seoul National University
- - 2020 - 2024 Assistant Professor, School of Biological Sciences, Seoul National University
- - 2018 - 2020 Post-doctoral Researcher, Johns Hopkins University School of Medicine
- - 2014 - 2018 Research Fellow, Max-Planck Institute for Biophysical Chemistry
Publications
- Barrio-Hernandez, Inigo, Yeo, J.; Jänes, J., Mirdita M., Gilchrist C. L.M., Wein, T., Varadi, M.; Velankar, S., Beltrao, P.*, Steinegger, M.* "Clustering predicted structures at the scale of the known protein universe", Nature, doi: 10.1038/s41586-023-06510-w
- van Kempen, M., Kim, S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist C. L.M., Söding, J.*, and Steinegger, M.* (2023) "Fast and accurate protein structure search with Foldseek", Nature Biotechnology, doi: 10.1101/2022.02.07.479398
- Mirdita M.*, Schütze K., Moriwaki Y., Heo L.,Ovchinnikov S.*, and Steinegger M.* (2022), "ColabFold: Making protein folding accessible to all", Nature Methods, doi: 10.1038/s41592-022-01488-1
- Elnaggar, A., Heinzinger, M., Dallago, C., and others (2021), "ProtTrans: Towards Cracking the Language of Lifes Code Through Self-Supervised Deep Learning and High Performance Computing", IEEE Transactions on Pattern Analysis and Machine Intelligence, doi: 10.1109/TPAMI.2021.3095381
- Jumper J., Evans R., Pritzel A., Green T. and others (2021), "Highly accurate protein structure prediction with AlphaFold", Nature, doi: 10.1038/s41586-021-03819-2
- Steinegger, M.*, Salzberg L S. (2020) "Terminating contamination: large-scale search identifies more than 2,000,000 contaminated entries in GenBank", Genome Biology , doi: 10.1186/s13059-020-02023-1 (∗Corresponding author)
- Steinegger, M.∗, Milot Mirdita, and Söding, J.∗ (2019) "Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold", Nature Methods, 16, 603–606, doi: 10.1038/s41592-019-0437-4 (∗Corresponding authors)
- Steinegger, M.∗, and Söding, J.∗ (2018) "Clustering huge protein sequence sets in linear time" Nature Communications doi: 10.1038/s41467-018-04964-5 (∗Corresponding authors)
- Steinegger, M., and Söding, J.* (2017) "MMseqs2: Sensitive protein sequence searching for the analysis of massive data sets", Nature Biotechnology, 35, 1026–1028, doi: 10.1038/nbt.3988