Martin Steinegger (scientist)

Martin Steinegger
Steinegger receiving the Overton Prize at the Intelligent Systems for Molecular Biology (ISMB) conference in 2024
Alma materTechnical University of Munich (PhD)
LMU Munich (BSc, MSc)
Known forMMseqs2, Linclust, ColabFold, Foldseek
AwardsOverton Prize (2024)
Scientific career
FieldsBioinformatics
WorkplacesSeoul National University
Johns Hopkins University School of Medicine
Max Planck Institute for Biophysical Chemistry
ThesisUltrafast and sensitive sequence search and clustering methods in the era of next generation sequencing (2018)
Johannes Söding
Other academic advisors
Steven Salzberg

Martin Steinegger is a bioinformatician and associate professor in the School of Biological Sciences at Seoul National University.[1][2] His research focuses on computational methods for large-scale analysis of biological sequence and structure data.[3] He is known for developing the bioinformatics tools MMseqs2, Linclust, ColabFold, and Foldseek,[3][4] and contributed to the development of AlphaFold2.[5] In 2024, he received the Overton Prize from the International Society for Computational Biology.[6][3]

Education and career

Steinegger studied bioinformatics at the Technical University of Munich and LMU Munich. He completed a PhD in computer science at the Technical University of Munich in 2018 under the supervision of Johannes Söding, with research carried out at the Max Planck Institute for Biophysical Chemistry. He was then a postdoctoral fellow in the laboratory of Steven Salzberg at Johns Hopkins University School of Medicine. In 2020, he joined Seoul National University as assistant professor.[1]

Research

With Johannes Söding, Steinegger developed MMseqs2, a software suite for protein sequence searching and clustering,[7] and Linclust, which clusters protein sequence datasets in linear time.[8] These tools are used in resources including UniRef, NCBI ClusteredNR, and RCSB PDB sequence clusters.[9][10][11] His Conterminator software, built on MMseqs2, was used to identify over two million contaminated or mislabeled database entries.[12]

He later co-developed ColabFold, an open platform described as making AlphaFold2 protein structure prediction more accessible to experimental biologists.[13][14] He also co-developed Foldseek, a tool for protein structure search.[15] Nature Methods described Foldseek as a major acceleration in protein structure database searches, while Nature reported on its adoption as the search tool for the AlphaFold Protein Structure Database.[16][4][17] In 2026, Nature reported that Steinegger was part of a consortium with EMBL-EBI, Google DeepMind, and NVIDIA that expanded the AlphaFold database to include homodimer predictions.[18]

Steinegger co-authored the 2021 Nature paper on AlphaFold and contributed the BFD sequence database used in the system.[5][19]

Awards and honors

References

  1. 1 2 Forrester, Nikki (2021). "How new principal investigators tackled a tumultuous year". Nature. doi:10.1038/d41586-021-01311-5.
  2. ↑ "Millions of protein complexes added to AlphaFold Database shed light on how proteins interact". EMBL News. European Molecular Biology Laboratory. 17 March 2026. Retrieved 21 April 2026.
  3. 1 2 3 4 Wiper, Mallory L. (2024). "The 2024 ISCB Overton Prize Award—Dr Martin Steinegger". Bioinformatics. 40 (Suppl 1): i3–i4. doi:10.1093/bioinformatics/btae288.
  4. 1 2 Hutson, Matthew (29 June 2023). "Foldseek gives AlphaFold protein database a rapid search tool". Nature. doi:10.1038/d41586-023-02205-4. PMID 37386183.
  5. 1 2 Jumper, John; et al. (2021). "Highly accurate protein structure prediction with AlphaFold". Nature. 596 (7873): 583–589. doi:10.1038/s41586-021-03819-2. PMC 8371605.
  6. 1 2 "ISCB Congratulates 2024 Award Winners". International Society for Computational Biology. 2024.
  7. ↑ Steinegger, Martin; Söding, Johannes (2017). "MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets". Nature Biotechnology. 35 (11): 1026–1028. doi:10.1038/nbt.3988. hdl:11858/00-001M-0000-002E-1967-3.
  8. ↑ Steinegger, Martin; Söding, Johannes (2018). "Clustering huge protein sequence sets in linear time". Nature Communications. 9 (1): 2542. doi:10.1038/s41467-018-04964-5. PMC 6026198.
  9. ↑ "UniRef". UniProt. UniProt Consortium. Retrieved 21 April 2026.
  10. ↑ "New ClusteredNR database: faster searches and more informative BLAST results". NCBI Insights. National Center for Biotechnology Information. 2 May 2022. Retrieved 21 April 2026.
  11. ↑ "Developers: Switch to New Sequence Cluster Files by April 12". RCSB Protein Data Bank. 12 February 2022. Retrieved 21 April 2026.
  12. ↑ Tang, Lin (2020). "Contamination in sequence databases". Nature Methods. 17 (7): 654. doi:10.1038/s41592-020-0895-8.
  13. ↑ Mirdita, Milot; Schütze, Konstantin; Moriwaki, Yoshitaka; Heo, Lim; Ovchinnikov, Sergey; Steinegger, Martin (2022). "ColabFold: making protein folding accessible to all". Nature Methods. 19 (6): 679–682. doi:10.1038/s41592-022-01488-1. PMC 9184281.
  14. ↑ Eisenstein, Michael (23 November 2021). "Artificial intelligence powers protein-folding predictions". Nature. doi:10.1038/d41586-021-03499-y.
  15. ↑ van Kempen, Michel; Kim, Stephanie; Tumescheit, Charlotte; Mirdita, Milot; Lee, Jeong; Gilchrist, Cameron L. M.; Söding, Johannes; Steinegger, Martin (2023). "Fast and accurate protein structure search with Foldseek". Nature Biotechnology. 42: 243–246. doi:10.1038/s41587-023-01773-0.
  16. ↑ Singh, Arunima (2023). "Speedier protein structure search". Nature Methods. 20 (7): 983. doi:10.1038/s41592-023-01953-5. PMID 37434010.
  17. ↑ Oldach, Laurel (2022). "Advancing structural biology to blazing speed". ASBMB Today.
  18. ↑ Callaway, Ewen (17 March 2026). "AlphaFold database hits 'next level': the AI system now includes protein pairing". Nature. doi:10.1038/d41586-026-00787-3.
  19. ↑ Tran, Laura (21 September 2023). "Lasker Award for Revolutionizing Protein Structure Predictions". The Scientist. Retrieved 20 April 2026.
  20. ↑ Cho, Seong-ho (19 March 2026). "Asan Medical Award Honors Top Researchers in Basic, Clinical Medicine". The Chosun Daily.
  21. ↑ "마틴 스타이네거 교수, Clarivate 선정 "2024 세계에서 가장 많이 인용된 연구자(Highly Cited Researchers)" 명단에 이름 올려 (Prof. Martin Steinegger)". News - Resources - BIOLOGICAL SCIENCES, SNU. Seoul National University School of Biological Sciences. 21 November 2024. Retrieved 21 April 2026.
  22. ↑ "마틴 슈타이네거 교수 및 밀롯 미르디타 박사, 2025년 '세계에서 가장 많이 인용된 연구자' 선정 (Prof. Martin Steinegger)". News - Resources - BIOLOGICAL SCIENCES, SNU. Seoul National University School of Biological Sciences. 13 November 2025. Retrieved 21 April 2026.
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