Dynamics of Inhibitor Binding and Regulation of Protein Tyrosine Kinases

PROJECT SUMMARY. Protein kinases are a large family of ubiquitous signaling enzymes in human cells. Their dysregulation often underlies diseases such as cancer, making them excellent therapeutic targets. However, the high structural and sequence conservation of the protein kinase catalytic domains has complicated the development of specific inhibitors. Many clinically-successful kinase inhibitors achieve specificity in part by binding only to distinct kinase conformations. While the analysis of thousands of X-ray crystal structures of protein kinases has shown that a single kinase domain can access different active and inactive conformations, little is known about how kinases interconvert between the conformations. The rationale of this proposal is that a quantitative understanding of the stability of these conformations and the dynamics of their interconversion are key to understanding kinase activity, regulation, ligand binding and drug resistance in health and disease states. The objective of this project is to obtain a comprehensive understanding of the role of conformational dynamics in kinase function, regulation and drug resistance through altered drug dissociation rates. This proposal is part of a continuum of research centered around three questions: Q1: How do regulatory domains, interacting proteins and disease-relevant mutations affect the conformational dynamics of tyrosine kinases? Our goal is to determine how the solution dynamics of Src kinase domain responds to these biologically important perturbations. Q2: What are the molecular and cellular determinants of ligand-binding kinetics? We will test our computational model for the drug binding process experimentally, both in vitro and in cells, by assessing drug affinities and binding and dissociation kinetics. Our goal is to determine quantitative parameters for the drug binding process and how the alteration of these parameters by mutation underlies drug resistance. Q3: How can phenotypic clustering of inhibition data be used to reveal hidden kinase features? Our goal is to mine kinome-wide inhibition datasets to group kinases by their inhibition phenotype, which reflects on the evolutionary pressure and adaption that other biological functions impose on kinases. We will use structural approaches (X-ray and NMR), cellular drug binding and activity assays, ligand binding kinetics, protein engineering, chemical biology and diverse computational methods. A network of productive collaborations supports this project. The impact of this project is to provide clinicians with the mechanism of resistance mutations, medicinal chemists with kinase inhibitor pharmacodynamics and pharmacokinetics and cell biologists with parameters to understand kinase signaling. The long-term goal is to lay the foundation for the design of safe, effective, and sufficiently specific inhibitors of disease-associated protein kinases.