Göttingen Graduate Center for Neurosciences, Biophysics, and Molecular Biosciences

Soeding, Johannes, Dr.

Research Group Leader at the Max Planck Institute for Multidisciplinary Sciences


  • 1992 Diploma in physics at the University of Heidelberg
  • 1996 PhD in physics at the University of Heidelberg
  • 1996 – 1998 Post-doc with C. Cohen-Tannoudji and J. Dalibard at the École Normale Supérieure in Paris
  • 1999 – 2002 Strategy management consultant for the Boston Consulting Group in Frankfurt
  • 2002 – 2007 Staff scientist with Andrei Lupas at the Max-Planck-Institute for Developmental Biology in Tuebingen
  • 2007 – 2013 Group leader at the Gene Center and Department of Biochemistry, University of Munich (LMU)
  • since 2014 Group Leader of the Computational Biology Group at the Max Planck Institute for Multidisciplinary Sciences (former MPI for Biophysical Chemistry)




Major Research Interests

Computational Biology Research Group

The group works on two broad topics. First, we develop computational and statistical methods for predicting the structure and function of proteins from their amino acid sequences. Our software packages for fast protein sequence searches, HH-suite/HHpred and MMseqs2 are standard tools in their field, and Foldseek is for fast protein structure searches. We also develop a statistical methods and software for analyzing large biomedical datasets for better understanding the genetic architecture of complex human diseases.


Homepage Department / Research Group

https://www.mpinat.mpg.de/de/soeding



Selected Recent Publications


  • van Kempen M, Kim S, Tumescheid C, Mirdita M, Söding J∗, Steinegger M∗ (2024) Fast and accurate protein structure search with Foldseek. Nat Biotechnol, 42, 243–246.

  • Banerjee S, Simonetti FL, Detrois KE, Kaphle A, Mitra R, Nagial R, Söding J (2021) Reverse regression increases power for detecting trans-eQTLs. Genome Biol 22, 142

  • Söding J, Zwicker D, Sohrabi-Jahromi S, Boehning M, Kirschbaum J (2020) Mechanisms of active regulation of biomolecular condensates. Trends Cell Biol 30, 4–14

  • Levi Karin E, Mirdita M, Söding J (2020) MetaEuk – sensitive, high- throughput gene discovery and annotation for eukaryotic metagenomics. Microbiome 8(48)

  • Erijman A, Kozlowski L, Sohrabi-Jahromi S, Fishburn J, Warfield L, Schreiber J, Noble WS, Söding J*, Hahn S* (2020) A high-throughput screen for transcription activation domains reveals their sequence characteristics and permits reliable prediction by deep learning. Mol Cell 78: 890–902

  • Sohrabi-Jahromi S#, Hofmann KB#, Boltendahl A, Roth C, Gressel S, Baejen C, Söding J*, Cramer P* (2019) Transcriptome maps of general eukaryotic RNA degradation factors. eLife 8:e47040 (#Equal contributions *Corresponding authors)

  • Söding J, Zwicker D, Sohrabi-Jahromi S, Boehning M, Kirschbaum J (2019) Mechanisms of active regulation of biomolecular condensates. bioRxiv: doi: https://doi.org/10.1101/694406

  • Steinegger M, Mirdita M, and Söding J (2019) Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold. Nature Methods 16, 603–606. https://doi.org/10.1038/s41592-019-0437-4, bioRxiv: https://doi.org/10.1101/386110

  • Vorberg S, Seemayer S and Söding J (2018) Synthetic protein alignments by CCMgen quantify noise in residue-residue contact prediction. PLoS Comput. Biol. 14, e1006526. bioRxiv https://doi.org/10.1101/344333

  • Banerjee S, Zeng L, Schunkert H, Söding J (2018) Bayesian multiple logistic regression for meta-analyses of GWAS. PLoS Genet 14:e1087856

  • Steinegger M, Söding J (2018) Clustering huge protein sequence sets in linear time. Nature Commun 9:2542-2550

  • Söding J (2017) Big-data approaches to protein structure prediction. Science (perspective), 355, 248-249. https://doi.org/10.1126/science.aal4512