Soeding, Johannes, Dr.

Research Group Leader at the Max Planck Institute for Biophysical Chemistry

  • 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 of Biophysical Chemistry

Major Research Interests

Computational Biology Research Group

Our group develops statistical and computational methods for analyzing data from high-throughput biological experiments. Our work is focussed on protein function and structure prediction, sequence search and assembly in metagenomics, transcription regulation, protein-RNA interactions, gene regulatory networks, and systems medicine.

Homepage Department / Research Group

Selected Recent Publications

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

  • Steinegger M, Mirdita M, and Söding J (2019) Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold. Nature Methods 16, 603–606., bioRxiv:

  • 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

  • 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.

  • Steinegger M, Söding J (2017) MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biotechnol. 35:1026-1028

  • Baejen C,# Andreani J,# Torkler P, Battaglia S, Schwalb B, Lidschreiber M, Maier KC, Boltendahl A, Rus P, Esslinger S, Söding J*, and Cramer P* (2017) Genome-wide analysis of RNA polymerase II termination at protein-coding genes. Molecular Cell 66, 38-49. e6. (#Equal contributions. *Corresponding authors.)

  • Siebert M, Söding J (2016) Bayesian Markov models consistently outperform PWMs at predicting motifs in nucleotide sequences. Nucleic Acids Res 44:6055-6069