Inhibitor binding to mutants of protein kinase A with GGGxxG and GxGxxA glycine-rich loop motifs.

The conserved GxGxxG motif of protein kinases forms a beta turn at the tip of the flexible glycine-rich loop and creates much of the ATP pocket binding surface. Notable exceptions to this sequence include GGGxxG in ABL kinase and GxGxxA in protein kinase C isoforms. We constructed the corresponding mutants of PKA, T51G, and G55A, and tested quinazoline inhibitors that were designed to bind via glycine-rich loop interactions, testing also staurosporine for comparison. The quinazoline inhibitors have significantly reduced binding strengths in both mutants. In striking contrast to these results, the binding of the "pan-kinome" inhibitor staurosporine is strengthened in the mutants. Surface plasmon resonance (SPR) shows that the tightened binding of staurosporine arises from increased kon rates, changes not offset by more moderately increased koff rates. The SPR results fit best to a two step binding process for staurosporine in wild type PKA, but not the mutants.

Addressing the Glycine-Rich Loop of Protein Kinases by a Multi-Facetted Interaction Network: Inhibition of PKA and a PKB Mimic.

Protein kinases continue to be hot targets in drug discovery research, as they are involved in many essential cellular processes and their deregulation can lead to a variety of diseases. A series of 32 enantiomerically pure inhibitors was synthesized and tested towards protein kinase A (PKA) and protein kinase B mimic PKAB3 (PKA triple mutant). The ligands bind to the hinge region, ribose pocket, and glycine-rich loop at the ATP site. Biological assays showed high potency against PKA, with Ki values in the low nanomolar range. The investigation demonstrates the significance of targeting the often neglected glycine-rich loop for gaining high binding potency. X-ray co-crystal structures revealed a multi-facetted network of ligand-loop interactions for the tightest binders, involving orthogonal dipolar contacts, sulfur and other dispersive contacts, amide-π stacking, and H-bonding to organofluorine, besides efficient water replacement. The network was analyzed in a computational approach.

Assessing protein kinase target similarity: Comparing sequence, structure, and cheminformatics approaches.

In just over two decades, structure based protein kinase inhibitor discovery has grown from trial and error approaches, using individual target structures, to structure and data driven approaches that may aim to optimize inhibition properties across several targets. This is increasingly enabled by the growing availability of potent compounds and kinome-wide binding data. Assessing the prospects for adapting known compounds to new therapeutic uses is thus a key priority for current drug discovery efforts. Tools that can successfully link the diverse information regarding target sequence, structure, and ligand binding properties now accompany a transformation of protein kinase inhibitor research, away from single, block-buster drug models, and toward "personalized medicine" with niche applications and highly specialized research groups. Major hurdles for the transformation to data driven drug discovery include mismatches in data types, and disparities of methods and molecules used; at the core remains the problem that ligand binding energies cannot be predicted precisely from individual structures. However, there is a growing body of experimental data for increasingly successful focussing of efforts: focussed chemical libraries, drug repurposing, polypharmacological design, to name a few. Protein kinase target similarity is easily quantified by sequence, and its relevance to ligand design includes broad classification by key binding sites, evaluation of resistance mutations, and the use of surrogate proteins. Although structural evaluation offers more information, the flexibility of protein kinases, and differences between the crystal and physiological environments may make the use of crystal structures misleading when structures are considered individually. Cheminformatics may enable the "calibration" of sequence and crystal structure information, with statistical methods able to identify key correlates to activity but also here, "the devil is in the details." Examples from specific repurposing and polypharmacology applications illustrate these points. This article is part of a Special Issue entitled: Inhibitors of Protein Kinases.