Protein kinases are a family of key enzymes that regulate cellular function under healthy conditions and misregulate cellular function in diseased conditions, such as cancer. Protein kinases are complex, highly-regulated, and dynamic enzymes whose primary function is not to turn over substrate but rather to integrate biological signals to make targeted post-translational modifications (phosphorylation) in specific substrates. Since the catalytic core of kinases is structurally conserved across the whole family, kinase structure is not sufficient to provide precise control over activity and substrate specificity. Drug design against kinases is challenging because this conserved active site structure can lead to off-target binding, which takes drug away from the desired target and causes unforeseen side effects. In order to design novel drugs that target non-catalytic sites in the kinase and drugs that might operate by kinetic rather than thermodynamic control, it is imperative to understand how dynamics regulate function in kinases at mechanistic level, and how this dynamic regulation evolved. To predict how novel drugs might modulate kinase function, it is important to develop computational tools to observe how these dynamics are altered by perturbations such as mutations or substrate binding. To study functional kinase dynamics in detail, NMR spectroscopy will be combined with advanced conformational sampling methods that take advantage of commodity graphics processors to study slow motions relevant to catalysis. Markov State Model methods will be improved to interpret NMR chemical shifts and relaxation-dispersion data at an atomic level. A covariance of mechanical stress approach will be developed along with transfer entropy analysis in internal coordinates to identify mechanistic cause and effect in active site opening, the rate- limiting step in catalysis. Residues implicated by these analyses will be mutated, and the kinase's slow dynamics studied by NMR. The role of dynamics in substrate specificity will be studied by monitoring the substrate's effects on kinase dynamics - locally, and at distal substrate docking sites, using molecular dynamics simulations with and without substrate peptides. Dynamical changes caused by substrate peptide binding will be compared across multiple kinases to identify the conservation of dynamic coupling between the active site and a distal C-lobe substrate docking site, and to potentially guide design of novel drugs that could potentially have fewer off-target effects or that might alter a kinase's substrate specificity.

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We will develop an approach that combines computational and experimental data to study how a signaling enzyme, protein kinase A, is regulated by dynamics. Our studies will help us understand how the kinase limits turnover under normal conditions and how mutations might cause pathogenic disregulation of kinase activity in disease. Mechanistic insights and tools developed in the course of this project are expected to be of general use in studying functionally-relevant conformational changes and dynamics and should be useful in guiding the discovery of novel small molecules targeting novel sites.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Postdoctoral Individual National Research Service Award (F32)
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Special Emphasis Panel (ZRG1-F04B-D (20))
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Flicker, Paula F
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University of California San Diego
Schools of Pharmacy
La Jolla
United States
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Meng, Hu; McClendon, Christopher L; Dai, Ziwei et al. (2016) Discovery of Novel 15-Lipoxygenase Activators To Shift the Human Arachidonic Acid Metabolic Network toward Inflammation Resolution. J Med Chem 59:4202-9
McClendon, Christopher L; Kornev, Alexandr P; Gilson, Michael K et al. (2014) Dynamic architecture of a protein kinase. Proc Natl Acad Sci U S A 111:E4623-31
Datta, Debajyoti; McClendon, Christopher L; Jacobson, Matthew P et al. (2013) Substrate and inhibitor-induced dimerization and cooperativity in caspase-1 but not caspase-3. J Biol Chem 288:9971-81
McClendon, Christopher L; Hua, Lan; Barreiro, Abriela et al. (2012) Comparing Conformational Ensembles Using the Kullback-Leibler Divergence Expansion. J Chem Theory Comput 8:2115-2126