COMPOUNDS FOR SELECTIVE KINASE INHIBITION

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Cross-reactivity of kinase inhibitors amongst various kinases is a large obstacle in the design of inhibitor molecules that would possess specificity towards certain kinase enzymes. Only recently, ruthenium organometallic ligand inhibitor molecules were designed (at the University of Pennsylvania) that possess specificity towards glycogen synthase kinase 3 (GSK-3), but the reasons governing such specificity are not well understood. In general, the rules and molecular mechanisms that dictate kinase inhibitor specificity is a relatively uncharted subject that still requires investigation. A better understanding in this problem is important because kinase specific inhibitors can be used to disrupt the activity of kinases involved in crucial signaling pathways in pathological cells, i.e., inhibition of cellular signaling pathways associated with diseased cells could potentially have a therapeutic value. Here, we developed a hierarchical computational strategy using implicit and explicit protocol to characterize the inhibitor binding modes and affinities (free energies). Our implicit scheme is based on using multiple snapshots of the kinase macromolecule from a molecular dynamics (MD) simulation, allows sampling of different conformations, and therefore is able to capture the flexibility of the protein. The different binding modes of small molecule tyrosine kinase inhibitors with these kinases are examined using this multiple conformation docking strategy. Then a more rigorous explicit approach involving fully flexible protein and ligand systems in explicit solvent umbrella sampling free energy calculations will be used to refine the binding energetics of lead structures. This strategy is expected to throw significant insight on the origin of kinase specificity in the class of organometalic inhibitors we are studying. We will compare the binding characteristics of a Ruthenium based organometalic inhibitor to three kinases, namely GSK-3, PIM-1, and CDK-2. Currently, we have been able to conduct 10 ns MD simulations (using NAMD) of the three kinases (GSK-3, PIM-1, and CDK-2) in explicit water. We have also performed simulated single frame docking between the ruthenium-based organometalic inhibitor molecules with the three kinases by employing AutoDock. We propose to automate the simulated docking between the ruthenium based organometalic inhibitor with the three kinases to perform the multiple conformation docking in parallel through our in-house parallel code. We propose to test this parallel code across platforms on the teragrid and request 20,000 SUs for this purpose. Each single frame docking requires an equivalent of 18 CPU hrs on NCSAs tungsten or on SDSCs datastar. For each kinase system we will perform three 32-processor parallel runs for 18 hrs to process 100 snapshots taken from our MD simulations. This amounts to [18 CPU hrs]*[32 processors]*[3 runs per kinase]*[three kinase systems]=5800 SUs. For the resulting lowest binding configurations (for two kinases, namely PIM-1 and GSK-3), we propose to perform umbrella sampling simulations to refine the binding free energies of binding. This requires [24 CPU hrs per processor per ns]*[32 processors]*[1 ns MD per umbrella using NAMD]*[7 umbrellas per kinase]*[2 kinases]=10753 SUs to obtain the free energy data. We request about 4000 SUs to test our in-house parallel for performing the multiple conformation docking. In total we request 5800 SUs+10753 SUs+4000 SUs=20,554 SUs rounded off to 20,000 SUs. We request these under a teragrid DAC grant because this is our first attempt to run cross platform simulations. (We have an MRAC renewal proposal pending for our single platform parallel applications).