Elucidation of characteristic structural features of ligand binding sites of protein kinases: a neural network approach

Abstract

Protein kinases play important roles in regulating cellular signal transduction and other biochemical processes, and they are attractive targets for drug discovery programs in many disease areas. Most kinase inhibitors under development as drugs act by directly competing with ATP at the ATP-binding site of the kinase. There are more than 500 protein kinases, and the ATP-binding site is highly conserved among them. Therefore selectivity is an essential requirement for clinically effective drugs, and understanding the structural characteristics of ATP-binding sites is of crucial importance. The objective of the present study was to elucidate the structural characteristics of the adenosine-binding site of four major kinase groups, AGC (PKA, PKG, and PKC families), CaMK (calcium/calmodulin-dependent protein kinases), CMGC (CDK, MAPK, GSK3, and CLK families), and TK (tyrosine kinases). To do this, we classified the kinases into groups by using feed-forward multilayer perceptron (MLP) neural networks and structural, electronic, and hydrophobic descriptors of the amino acids at the adenosine-binding site. A total of 275 kinases were classified in two ways: (1) kinases belonging to a certain group were distinguished from those not belonging to that group, and (2) all of the kinases were classified into four groups. More than 85% of the kinases were correctly classified by both methods. Trained neural networks clarified which amino acids and which properties characterize the adenosine-binding site of each group, and the results were visualized by molecular graphics. Comparison of the modeled neural networks and the distributions of amino acids provided more detailed information on the structural characteristics of each group. Application of the present results to drug development is also discussed.