Protein function prediction via graph kernels

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1 Protein function prediction via graph kernels Karsten Borgwardt Joint work with Cheng oon Ong and.v.n. Vishwanathan, tefan chönauer, Hans-Peter Kriegel and Alex mola appeared in IMB

2 Content Introduction The problem: protein function prediction The method: upport Vector Machines (VM) Our approach to function prediction Protein graph model Protein graph kernel Experimental evaluation Technique to analyze our graph model Hyperkernels Discussion 2

3 Current approaches to protein function prediction similar structures similar phylogenetic profiles similar motifs similar interaction partners similar function similar sequences similar chemical properties similar surface clefts 3

4 Current approaches to protein function prediction similar structures similar phylogenetic profiles similar motifs similar function similar sequences similar interaction partners similar surface clefts similar chemical properties 4

5 upport Vector Machines Are new data points (x) red or black? The blue decision boundary allows to predict class membership of new data points. 5

6 Kernel trick input space feature space mapping Ф kernel function The kernel trick allows to introduce a separating hyperplane in feature space. 6

$(2004), Dobson and Doig (2003) hydrophobicity polarity polarizability van der Waals volume fraction of amino acid types fraction of surface area$

7 Feature vectors for function prediction protein structure and/or protein sequence e.g. Cai et al. (2004), Dobson and Doig (2003) hydrophobicity polarity polarizability van der Waals volume fraction of amino acid types fraction of surface area disulphide bonds size of largest surface pocket 7

8 Our approach equence + tructure + Chemical properties Graph model VMs + Graph models Protein function 8

9 Protein graph model protein secondary structure sequence structure 9

10 Protein graph model Node attributes hydrophobicity polarity polarizability van der Waals volume length helix, sheet, loop Edge attributes type (sequence, structure) length 10

11 Protein graph kernel (Kashima et al. (2003) and Gärtner et al. (2003)) compares walks of identical length l k walk ((v 1,...,v l ),(w 1,...,w l )) = l 1 k step ((v i,v i+1 ),(w i,w i+1 )) i=1 Walks are similar, if along both walks types of secondary structure elements (Es) are the same distances between Es are similar chemical properties of Es are similar 11

12 Example: Protein kernel Protein A Protein B imilar (H,10,,1,,3,H) (H,9,,1,,3,H) 12

13 Example: Protein kernel Protein A Protein B Dissimilar (H,10,,1,) (,3,H,5,) 13

14 Evaluation: enzymes vs. non-enzymes 10-fold cross-validation on 1128 proteins from dataset by Dobson and Doig (2003); 59 % are enzymes. 14

15 Attribute selection Which structural or chemical attribute is most important for correct classification? For this purpose, we employ hyperkernels (Ong et. al, 2003). Hyperkernels find an optimal linear combination of input kernel matrices : n i=1µ i K i minimizing training error and fulfilling regularization constraints 15

16 Attribute selection Our approach: Calculate kernel matrix for 600 proteins on graph model with only ONE single attribute! Repeat this for all attributes Normalize these kernel matrices Determine hyperkernel combination Weights then reflect contribution of individual attributes to correct classification 16

17 Attribute selection Attribute EC 1 EC 2 EC 3 EC 4 EC 5 EC 6 Amino acid length bin van der Waals 3-bin Hydrophobicity 3-bin Polarity bin Polarizability d length 0.40 Total van der Waals Total Hydrophobicity Total Polarity Total Polarizability

18 Discussion Novel combined approach to protein function prediction integrating sequence, structure and chemical information Reaches state-of-the-art classification accuracy on less information; higher accuracy levels on same amount of information Hyperkernels for finding most interesting protein characteristics 18

19 Discussion More detailed graph models (amino acids, atoms) might be more interesting, yet raise computational difficulties (graphs too large!) Two directions of future research: Efficient, yet expressive graph kernels for structure Integrating more proteomic information, e.g. surface pockets, into our graph model 19

BIOINFORMATICS. Protein function prediction via graph kernels

BIOINFORMATICS. Protein function prediction via graph kernels BIOINFORMATICS Vol. 00 no. 00 2005 Pages 1 9 Protein function prediction via graph kernels Karsten M. Borgwardt a, Cheng Soon Ong b, Stefan Schönauer a, S.V.N. Vishwanathan b, Alex J. Smola b, Hans-Peter