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

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

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

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

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 accessible surface area (ASA)

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

8 patch types

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

10 features over patches

11 betweenness-centrality (BC) s t v

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

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

16 acknowledgments  Zheng Yuan – Data sets and much more …  Karin Kassahn – Aminoacyl-tRNA synthetases

17 questions