Binding site similarity analysis for the functional classification of the protein kinase family.

Methods for analyzing complete gene families are becoming of increasing importance to the drug discovery process, because similarities and differences within a family are often the key to understanding functional differences that can be exploited in drug design. We undertake a large-scale structural comparison of protein kinase ATP-binding sites using a geometric hashing method. Subsequently, we propose a relevant classification of the protein kinase family based on the structural similarity of its binding sites. Our classification is not only able to reveal the great diversity of different protein kinases and therefore their different potential for inhibitor selectivity but it is also able to distinguish subtle differences within binding site conformation reflecting the protein activation state. Furthermore, using experimental inhibition profiling, we demonstrate that our classification can be used to identify protein kinase binding sites that are known experimentally to bind the same drug, demonstrating that it has potential as an inverse (protein) virtual screening tool, by identifying which other sites have the potential to bind a given drug. In this way the cross-reactivities of the anticancer drugs Tarceva and Gleevec are rationalized.