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Protein Interaction Networks

Protein Interaction Networks. Feb. 21, 2013. Aalt-Jan van Dijk Applied Bioinformatics, PRI, Wageningen UR & Mathematical and Statistical Methods, Biometris, Wageningen University aaltjan.vandijk@wur.nl. My research. Protein complex structures Protein-protein docking Correlated mutations

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Protein Interaction Networks

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  1. Protein Interaction Networks Feb. 21, 2013 Aalt-Jan van Dijk Applied Bioinformatics, PRI, Wageningen UR & Mathematical and Statistical Methods, Biometris, Wageningen University aaltjan.vandijk@wur.nl

  2. My research • Protein complex structures • Protein-protein docking • Correlated mutations • Interaction site prediction/analysis • Protein-protein interactions • Enzyme active sites • Protein-DNA interactions • Network modelling • Gene regulatory networks • Flowering related

  3. Overview • Introduction: protein interaction networks • Sequences & networks: predicting interaction sites • Predicting protein interactions • Sequence and network evolution • Interaction network alignment

  4. Protein Interaction Networks hemoglobin Obligatory

  5. Protein Interaction Networks hemoglobin Mitochondrial Cu transporters Obligatory Transient

  6. Experimental approaches (1) Yeast two-hybrid (Y2H)

  7. Experimental approaches (2) Affinity Purification + mass spectrometry (AP-MS)

  8. Interaction Databases • STRINGhttp://string.embl.de/

  9. Interaction Databases

  10. Interaction Databases • STRING http://string.embl.de/ • HPRD http://www.hprd.org/

  11. Interaction Databases

  12. Interaction Databases • STRING http://string.embl.de/ • HPRD http://www.hprd.org/ • MINT http://mint.bio.uniroma2.it/mint/

  13. Interaction Databases

  14. Interaction Databases • STRING http://string.embl.de/ • HPRD http://www.hprd.org/ • MINT http://mint.bio.uniroma2.it/mint/ • INTACT http://www.ebi.ac.uk/intact/

  15. Interaction Databases

  16. Interaction Databases • STRING http://string.embl.de/ • HPRD http://www.hprd.org/ • MINT http://mint.bio.uniroma2.it/mint/ • INTACT http://www.ebi.ac.uk/intact/ • BIOGRID http://thebiogrid.org/

  17. Interaction Databases

  18. Some numbers Organism Number of known interactions H. Sapiens 113,217 S. Cerevisiae75,529 D. Melanogaster 35,028 A. Thaliana13,842 M. Musculus 11,616 Biogrid (physical interactions)

  19. Overview • Introduction: protein interaction networks • Sequences & networks: predicting interaction sites • Predicting protein interactions • Sequence and network evolution • Interaction network alignment

  20. Binding site

  21. Binding site prediction Applications:

  22. Binding site prediction Applications: • Understanding network evolution • Understanding changes in protein function • Predict protein interactions • Manipulate protein interactions

  23. Binding site prediction Applications: • Understanding network evolution • Understanding changes in protein function • Predict protein interactions • Manipulate protein interactions Input data: • Interaction network • Sequences (possibly structures)

  24. Sequence-based predictions

  25. Sequences and networks • Goal: predict interaction sites and/or motifs

  26. Sequences and networks • Goal: predict interaction sites and/or motifs • Data: interaction networks, sequences

  27. Sequences and networks • Goal: predict interaction sites and/or motifs • Data: interaction networks, sequences • Validation: structure data, “motif databases”

  28. Motif search in groups of proteins • Group proteins which have same interaction partner • Use motif search, e.g. find PWMs Neduva Plos Biol 2005

  29. Correlated Motifs

  30. Correlated Motifs • Motif model • Search • Scoring

  31. Predefined motifs

  32. Predefined motifs

  33. Predefined motifs

  34. Predefined motifs

  35. Predefined motifs

  36. Correlated Motif Mining Find motifs in one set of proteins which interact with (almost) all proteins with another motif

  37. Correlated Motif Mining • Find motifs in one set of proteins which interact with • (almost) all proteins with another motif • Motif-models: • PWM – so far not applied • (l,d) with l=length, d=number of wildcards • Score: overrepresentation, e.g. χ2

  38. Correlated Motif Mining • Find motifs in one set of proteins which interact with • (almost) all proteins with another motif • Search: • Interaction driven • Motif driven

  39. Interaction driven approaches Mine for (quasi-)bicliques  most-versus-most interaction Then derive motif pair from sequences

  40. Motif driven approaches Starting from candidate motif pairs, evaluate their support in the network (and improve them)

  41. D-MOTIF Tan BMC Bioinformatics 2006

  42. IMSS: application of D-MOTIF protein X protein Y Test error Number of selected motif pairs Van Dijk et al., Bioinformatics 2008 Van Dijk et al., Plos Comp Biol 2010

  43. Experimental validation protein X protein Y Test error Number of selected motif pairs Van Dijk et al., Bioinformatics 2008 Van Dijk et al., Plos Comp Biol 2010

  44. Experimental validation protein X protein Y Test error Number of selected motif pairs Van Dijk et al., Bioinformatics 2008 Van Dijk et al., Plos Comp Biol 2010

  45. Experimental validation protein X protein Y Test error Number of selected motif pairs Van Dijk et al., Bioinformatics 2008 Van Dijk et al., Plos Comp Biol 2010

  46. SLIDER Boyen et al. Trans Comp Biol Bioinf 2011

  47. SLIDER • Faster approach, enabling genome wide search • Scoring: Chi2 • Search: steepest ascent

  48. Validation • Performance assessment on simulated data • Performance assessment using using protein structures

  49. Extensions of SLIDER • Extension I: better coverage of network Boyen et al. Trans Comp Biol Bioinf 2013

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