Graphlet Kernel

This web page contains supplementary information for the paper "Graphlet kernels for prediction of functional residues in protein structures", by Vladimir Vacic, Lilia Iakoucheva, Stefano Lonardi, and Predrag Radivojac, Journal of Computational Biology. 17(1):55-72. (2010)

Preliminary version of the manuscript can be downloaded here.

Supplementary information

Supplementary Table S1: A nonredundant subset of phosphorylated sites mined from PDB and results of the search for the structures of proteins that can be found both in the phosphorylated and the unphosphorylated states.

Supplementary Table S2: Summary of the set of sequences with experimentally annotated phosphorylation sites. NR: non-redundant, here defined as having less than 40% sequence identity in the 25 residue-long fragment centered at S, T or Y.

Supplementary Table S3: Performance of method/parameter combinations on the CSA dataset. Within each group of predictors, the one with the highest AUC was awarded a point. In the case of ties, the point would be evenly split between the methods which performed equally well.

Supplementary Table S4: Performance of method/parameter combinations on the PHOS dataset. Within each group of predictors, the one with the highest AUC was awarded a point.

Supplementary Table S5: Jaccard coefficients between sets of edges in residue interaction networks obtained using different methods. Jaccard similarity coefficient for two sets A and B is defined as J(A,B) = |A \ B|/|A [ B|. Values over 70% are in bold face and marked with an asterisk.

Supplementary Figure S1: Schematic representation of the BLOSUM50 matrix-based amino acid alphabet reduction.

Supplementary Figures S2-S24: Performance of all predictors on the CSA and PHOS datasets.

Software

Beta version of the graphlet kernel code is available for download.