Publication in Science, January 20, 2017

Söding, J.
Big-data approaches to protein structure prediction.

Research Group Quantitative and Computational Biology

Publications

Journal Article (84)

  1. 1.
    Bäjen, C.; Andreani, J.; Torkler, P.; Battaglia, S.; Schwalb, B.; Lidschreiber, M.; Maier, K. C.; Boltendahl, A.; Rus, P.; Esslinger, S. et al.; Söding, J.; Cramer, P.: Genome-wide analysis of RNA polymerase II termination at protein-coding genes. Molecular Cell 66 (1), pp. 38 - 49 (2017)
  2. 2.
    Galiez, C.; Siebert, M.; Enault, F.; Vincent, J.; Söding, J.: WIsH: Who is the host? Predicting prokaryotic hosts from metagenomic phage contigs. Bioinformatics 33 (19), pp. 3113 - 3114 (2017)
  3. 3.
    Mirdita, M.; von den Driesch, L.; Galiez, C.; Martin, M. J.; Söding, J.; Steinegger, M.: Uniclust databases of clustered and deeply annotated protein sequences and alignments. Nucleic Acids Research 45 (D1), pp. D170 - D176 (2017)
  4. 4.
    Prytuliak, R.; Volkmer, M.; Meier, M.; Habermann , B. H.: HH-MOTiF: De novo detection of short linear motifs in proteins by Hidden Markov Model comparisons. Nucleic Acids Research 45 (W1), pp. W470 - W477 (2017)
  5. 5.
    Steinegger, M.; Söding, J.: MMseqs2: sensitive protein sequence searching for the analysis of massive data sets. Nature biotechnology (2017)
  6. 6.
    Steinegger, M.; Söding, J.: MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biotechnology (2017)
  7. 7.
    Söding, J.: Big-data approaches to protein structure prediction. Science (2017)
  8. 8.
    Alva, V.; Nam, S. Z.; Söding, J.; Lupas, A. N.: The MPI bioinformatics Toolkit as an integrative platform for advanced protein sequence and structure analysis. Nucleic Acids Research 44 (W1), pp. W410 - W415 (2016)
  9. 9.
    Hauser, M.; Steinegger, M.; Söding, J.: MMseqs software suite for fast and deep clustering and searching of large protein sequence sets. Bioinformatics 32 (9), pp. 1323 - 1330 (2016)
  10. 10.
    Siebert, M.; Söding, J.: Bayesian Markov models consistently outperform PWMs at predicting motifs in nucleotide sequences. Nucleic Acids Research 44 (13), pp. 6055 - 6069 (2016)
  11. 11.
    Stützer, A.; Liokatis, S.; Kiesel, A.; Schwarzer, D.; Sprangers, R.; Söding, J.; Selenko, P.; Fischle, W.: Modulations of DNA contacts by linker histones and post-translational modifications determine the mobility and modifiability of nucleosomal H3 tails. Molecular Cell 61 (2), pp. 247 - 259 (2016)
  12. 12.
    Alva, V.; Söding, J.; Lupas, A. N.: A vocabulary of ancient peptides at the origin of folded proteins. eLife (2015)
  13. 13.
    Andreani, J.; Söding, J.: bbcontacts: Prediction of β-strand pairing from direct coupling patterns. Bioinformatics (2015)
  14. 14.
    Meier, A.; Söding, J.: Context similarity scoring improves protein sequence alignments in the midnight zone. Bioinformatics 31 (5), pp. 674 - 681 (2015)
  15. 15.
    Meier, A.; Söding, J.: Automatic prediction of protein 3D Structures by probabilistic multi-template homology modeling. PLoS Computational Biology (2015)
  16. 16.
    Mühlbacher, W.; Mayer, A.; Sun, M.; Remmert, M.; Cheung, A. C. M.; Niesser, J.; Söding, J.; Cramer, P.: Structure of Ctk3, a subunit of the RNA polymerase II CTD kinase complex, reveals a non-canonical CTD-interacting domain fold. Proteins: Structure, Function, and Bioinformatics 83 (10), pp. 1849 - 1858 (2015)
  17. 17.
    Bäjen, C.; Torkler, P.; Gressel, S.; Essig, K.; Söding, J.; Cramer, P.: Transcriptome maps of mRNP biogenesis factors define pre-mRNA recognition. Molecular Cell 55 (5), pp. 745 - 757 (2014)
  18. 18.
    Runge, S.; Sparrer, K. M. J.; Lässig, C.; Hembach, K.; Baum, A.; Garcia-Sastre, A.; Söding, J.; Conzelmann, K. K.; Hopfner, K. P.: In vivo ligands of MDA5 and RIG-I in measles virus-infected cells. PLoS Pathogens (2014)
  19. 19.
    Saponaro, M.; Mitter, R.; Kantidakis, T.; Kelly, G. P.; Heron, M.; Williams, H.; Söding, J.; Stewart, A.; Svejstrup, J. Q.: RECQL5 controls transcript elongation and suppresses genome instability associated with transcription stress. Cell 157 (5), pp. 1037 - 1049 (2014)
  20. 20.
    Seemayer, S.; Gruber, M.; Söding, J.: CCMpred-fast and precise prediction of protein residue-residue contacts from correlated mutations. Bioinformatics 30 (21), pp. 3128 - 3130 (2014)
 
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