1. 1 correlating graph-theoretical centrality indices with interface residue propensity or: where do things stick together? Stefan Maetschke Teasdale Group

2. 2 …a bit more specific  Prediction of interface residues  Protein-RNA interfaces  Machine learning methods  Structural information  Graph-topological features

3. 3 something for the visual cortex [Terribilini et al. 2006][JMol,1R3E_A][Jung Library] Protein-RNA complex Binding siteContact graph

4. 4 questions Most predictors are sequence based:   What impact has structural information on prediction accuracy?   What features are predictive for interface residues?

5. 5 obvious features  is on surface => Accessible surface area  has to bind=> Physico-chemical prop.  must be stabilized=> Contact graph topology  prefers flat surface=> not really  is conserved=> maybe not that much Interface residue…

6. 6 accessible surface area (ASA) http://www.see.ed.ac.uk/~tduren/research/surface_area/ http://www.ysbl.york.ac.uk/~ccp4mg/ccp4mg_help/analysis.html

7. 7 physico-chemical properties Hydrophobicity Inside/Outside Partition Coefficient Conformation  AAIndex database  approx. 400 indices  AUC over 144 protein chains 4304 binding and 27932 non-binding sequence similarity < 30%

8. 8 patch types

9. 9 patch type comparison  Naïve Bayes  PSI-BLAST Profiles  AUC  5-fold x-validation  RB144 data set

10. 10 features over patches

11. 11 betweenness-centrality (BC) http://en.wikipedia.org/wiki/Image:Graph_betweenness.svg s t v

12. 12 BC for contact graph  1FJG_K  AUC = 0.71  Red: interface residue  Size: betweenness centrality Histogram: binned BC over RB144

13. 13 combined features  WRC: distance-weighted retention coefficient  BC: betweenness centrality  ASA: accessible surface area  5-fold x–validation, RB144  Patch sizes: sequential->11, topological->19, spatial->19

14. 14 summary  Patch size is critical for sequential patches  Spatial/topological patches perform better  Structural information helps – but not much: +5%  Novelty: centrality indices as predictors  SVM superior to NB  Top prediction accuracy – as far as one can tell  Accuracy in general is still low (MCC < 0.4)

15. 15 what’s next…  Prediction of disease associated SNPs  Graph-spectral methods  Protein function prediction

16. 16 acknowledgments  Zheng Yuan – Data sets and much more …  Karin Kassahn – Aminoacyl-tRNA synthetases http://en.wikipedia.org/wiki/Aminoacyl_tRNA_synthetase

17. 17 questions