The study and prediction of kin
ase function (kinomics) is of major importance for proteome research dueto the widespread distribution of kin
ases. However, theprediction of protein function b
ased on the similaritybetween a functionally annotated 3D template and a querystructure may fail, for instance, if a similar proteinstructure cannot be identified. Alternatively, function canbe
assigned using 3D-structural empirical parameters. Inprevious studies, we introduced parameters b
ased onelectrostatic entropy (
Proteins 2004,
56, 715) and molecular vibration entropy (
Bioinformatics 2003,
19, 2079) butignored other important factors such
as van der Waals(vdw) interactions. In the work described here, we define3D-vdw entropies (
![](/images/entities/deg.gif)
k) and use them for the first time toderive a cl
assifier for protein kin
ases. The model cl
assifiescorrectly 88.0% of proteins in training and more than85.0% of proteins in validation studies. Principal components analysis of heterogeneous proteins demonstratedthat
![](/images/entities/deg.gif)
k codify information that is different to that described by other bulk or folding parameters. In additionalvalidation experiments, the model recognized 129 out of142 kin
ases (90.8%) and 592 out of 677 non-kin
ases(87.4%) not used above. This study provides a b
asis forfurther consideration of
![](/images/entities/deg.gif)
k as parameters for the empirical search for structure-function relationships.