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correlating graph-theoretical correlating graph-theoretical centrality indices with interface centrality indices with interface residue propensityresidue propensity

or: where do things stick together?

Stefan MaetschkeTeasdale Group

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…a bit more specific

Prediction of interface residues Protein-RNA interfaces Machine learning methods Structural information Graph-topological features

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something for the visual cortex

[Terribilini et al. 2006][JMol,1R3E_A] [Jung Library]

Protein-RNA complex Binding site Contact graph

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questions

Most predictors are sequence based:

What impact has structural information on prediction accuracy?

What features are predictive for interface residues?

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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…

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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

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physico-chemical properties

Hydrophobicity

Inside/Outside

Partition Coefficient

Conformation

AAIndex database approx. 400 indices AUC over 144 protein chains

4304 binding and 27932 non-bindingsequence similarity < 30%

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patch types

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patch type comparison

Naïve Bayes PSI-BLAST Profiles AUC 5-fold x-validation RB144 data set

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features over patches

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betweenness-centrality (BC)

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

s tv

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BC for contact graph

1FJG_K AUC = 0.71 Red: interface residue Size: betweenness centrality

Histogram: binned BC over RB144

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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

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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)

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

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acknowledgments

Zheng Yuan – Data sets and much more …

Karin Kassahn – Aminoacyl-tRNA synthetases

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

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questions