Functional Classification of Protein Kinase Binding Sites Using Cavbase

Increasingly, drug‐discovery processes focus on complete gene families. Tools for analyzing similarities and differences across protein families are important for the understanding of key functional features of proteins. Herein we present a method for classifying protein families on the basis of the properties of their active sites. We have developed Cavbase, a method for describing and comparing protein binding pockets, and show its application to the functional classification of the binding pockets of the protein family of protein kinases. A diverse set of kinase cavities is mutually compared and analyzed in terms of recurring functional recognition patterns in the active sites. We are able to propose a relevant classification based on the binding motifs in the active sites. The obtained classification provides a novel perspective on functional properties across protein space. The classification of the MAP and the c‐Abl kinases is analyzed in detail, showing a clear separation of the respective kinase subfamilies. Remarkable cross‐relations among protein kinases are detected, in contrast to sequence‐based classifications, which are not able to detect these relations. Furthermore, our classification is able to highlight features important in the optimization of protein kinase inhibitors. Using small‐molecule inhibition data we could rationalize cross‐reactivities between unrelated kinases which become apparent in the structural comparison of their binding sites. This procedure helps in the identification of other possible kinase targets that behave similarly in “binding pocket space” to the kinase under consideration. A diverse set of 258 kinases has been analyzed and clustered based on the exposed physicochemical properties of their ATP binding sites using Cavbase. The resulting clustering provides a relevant grouping of the kinases. Furthermore, pairs of kinases are identified that show unexpected similarities in their binding sites independent of their distance in sequence space.