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correlating graph-theoretical centrality indices with interface residue propensity

correlating graph-theoretical centrality indices with interface residue propensity. or: where do things stick together?. Stefan Maetschke Teasdale Group. …a bit more specific. Prediction of interface residues Protein-RNA interfaces Machine learning methods Structural information

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correlating graph-theoretical centrality indices with interface residue propensity

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  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 Protein-RNA complex Binding site Contact graph [JMol,1R3E_A] [Terribilini et al. 2006] [Jung Library]

  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) http://www.see.ed.ac.uk/~tduren/research/surface_area/ http://www.ysbl.york.ac.uk/~ccp4mg/ccp4mg_help/analysis.html

  7. physico-chemical properties • AAIndex database • approx. 400 indices • AUC over 144 protein chains4304 binding and 27932 non-bindingsequence similarity < 30% Hydrophobicity Inside/Outside Conformation Partition Coefficient

  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 v t http://en.wikipedia.org/wiki/Image:Graph_betweenness.svg

  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 http://en.wikipedia.org/wiki/Aminoacyl_tRNA_synthetase

  17. questions

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