COMPOUNDS FOR SELECTIVE KINASE INHIBITION

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 在设计对某些酶具有特异性的抑制剂分子时，不同的激酶之间的交叉反应是一个很大的障碍。直到最近，(宾夕法尼亚大学)设计出了对糖原合成酶激酶3(GSK-3)具有特异性的Ru有机金属配体抑制剂分子，但这种特异性的原因尚不清楚。总的来说，决定激酶抑制剂专一性的规则和分子机制是一个相对未知的课题，仍然需要研究。更好地了解这一问题是很重要的，因为激酶特异性抑制剂可以用来干扰参与病理细胞中关键信号通路的激酶的活性，即抑制与病变细胞相关的细胞信号通路可能具有潜在的治疗价值。在这里，我们开发了一种分层计算策略，使用隐式和显式协议来表征抑制剂的结合模式和亲和力(自由能)。我们的隐式方案基于使用来自分子动力学(MD)模拟的激酶大分子的多个快照，允许对不同构象进行采样，因此能够捕捉到蛋白质的灵活性。使用这种多构象对接策略研究了小分子酪氨酸激酶抑制剂与这些激酶的不同结合模式。然后，在显式溶剂伞采样自由能计算中，涉及完全柔性的蛋白质和配体系统的更严格的显式方法将被用于精炼铅结构的结合能。这一策略有望对我们正在研究的这类有机金属抑制剂中的激酶特异性的起源有重要的洞察力。我们将比较基于Ru的有机金属抑制剂与三种激酶，即GSK-3，PIM-1和CDK-2的结合特性。目前，我们已经能够在显性水中对三种激酶(GSK-3、PIM-1和CDK-2)进行10 ns MD模拟(使用NAMD)。我们还利用AutoDock模拟了基于Ru的有机金属抑制剂分子与这三种激酶之间的单帧对接。我们建议通过我们内部的并行代码来自动对接基于Ru的有机金属缓蚀剂与这三种激酶之间的模拟对接，以并行执行多构象对接。我们建议在TeraGrid上跨平台测试该并行代码，并为此请求20,000个SU。在NCSA钨或SDSC DataStar上，每个单帧对接需要相当于18个CPU小时。对于每个激酶系统，我们将在18小时内执行三次32处理器并行运行，以处理从我们的MD模拟中获得的100个快照。这相当于[18个CPU小时]*[32个处理器]*[每个激酶运行3次]*[三个激酶系统]=5800 Su。对于得到的最低结合构型(两种激酶，即PIM-1和GSK-3)，我们建议进行伞形采样模拟，以精炼结合自由能。这需要[每个处理器每ns 24个CPU小时]*[32个处理器]*[使用NAMD的每个伞1 ns MD]*[每个激酶7把伞]*[2个激酶]=10753 SU才能获得自由能数据。我们要求大约4000个SU来测试我们执行多构象对接的内部并行。我们总共请求5800个SU+10753个SU+4000个SU=20,554个SU，四舍五入为20,000个SU。我们根据TeraGrid DAC拨款申请这些服务，因为这是我们第一次尝试运行跨平台模拟。(我们正在为我们的单平台并行应用程序提交一份MRAC续订提案)。

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).