COMPOUNDS FOR SELECTIVE KINASE INHIBITION

用于选择性激酶抑制的化合物

• 批准号: 7601539
• 负责人: Ravi Radhakrishnan
• 金额: $ 0.03万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2008-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7601539
• 关键词: Affinity; Binding; Cells; Characteristics; Class; Code; Computer Retrieval of Information on Scientific Projects Database; Data; Docking; Enzymes; Free Energy; Funding; Glycogen Synthase Kinase 3; Grant; Housing; Institution; Investigation; Lead; Ligands; Molecular; Molecular Conformation; Pennsylvania; Phosphotransferases; Pliability; Process; Proteins; Protocols documentation; Purpose; Research; Research Personnel; Resources; Running; Ruthenium; Sampling; Scheme; Signal Pathway; Simulate; Solvents; Source; Specificity; Structure; System; Testing; Therapeutic; Tungsten; Tyrosine Kinase Inhibitor; United States National Institutes of Health; Universities; Water; base; cross reactivity; design; inhibitor/antagonist; insight; kinase inhibitor; macromolecule; molecular dynamics; simulation; small molecule

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).

该副本是利用众多研究子项目之一 由NIH/NCRR资助的中心赠款提供的资源。子弹和 调查员（PI）可能已经从其他NIH来源获得了主要资金， 因此可以在其他清晰的条目中代表。列出的机构是 对于中心，这不一定是调查员的机构。 各种激酶之间激酶抑制剂的交叉反应性是抑制剂分子设计的一个大障碍，它将具有对某些激酶酶具有特异性。直到最近，在宾夕法尼亚大学设计了有机金属抑制剂分子（在宾夕法尼亚大学），对糖原合酶激酶3（GSK-3）具有特异性，但是管理这种特异性的原因尚不很好。通常，决定激酶抑制剂特异性的规则和分子机制是一个相对未知的受试者，仍然需要研究。对该问题的更好理解很重要，因为激酶特异性抑制剂可用于破坏病理细胞中与关键信号通路有关的激酶的活性，即，抑制与患病细胞相关的细胞信号传导途径可能具有治疗价值。在这里，我们使用隐式和显式协议制定了层次计算策略，以表征抑制剂结合模式和亲和力（自由能）。我们的隐式方案基于使用分子动力学（MD）模拟的激酶大分子的多个快照，允许对不同构型进行采样，因此能够捕获蛋白质的柔韧性。使用这种多种构象对接策略检查了小分子酪氨酸激酶抑制剂的不同结合模式。然后，更严格的明确方法涉及明确的溶剂伞采样自由能计算中的完全柔性蛋白和配体系统，以优化铅结构的结合能。预计该策略将对我们正在研究的有机抑制剂类别中激酶特异性的起源进行重大见解。我们将比较基于ruthenium的有机抑制剂与三种激酶的结合特性，即GSK-3，PIM-1和CDK-2。目前，我们能够在显式水中进行三种激酶（GSK-3，PIM-1和CDK-2）的10 ns MD模拟（使用NAMD）。我们还通过使用自动货架，与三个激酶之间的基于芳族的有机抑制剂分子之间进行了模拟的单帧对接。我们建议使用三种激酶自动化基于芳族的有机抑制剂之间的模拟对接，以通过我们的内部并行代码并行执行多构象对接。我们建议在TeraGRID上的平台上测试此并行代码，并为此目的要求20,000 SU。每个框架对接都需要在NCSAS钨上或SDSCS DataStar上等效于18个CPU HR。对于每个激酶系统，我们将执行3个32个处理器并行运行18小时，以处理从我们的MD模拟中获取的100个快照。这相当于[18 cpu hrs]*[32处理器]*[每个激酶]*[3个激酶系统] = 5800 SUS。对于所得的最低结合构型（对于两个激酶，即PIM-1和GSK-3），我们建议执行雨伞采样模拟，以完善结合的结合自由能。这需要[每个NS]*[32个处理器]*[使用NAMD]*[1 ns MD]*[每激酶每激酶7伞]*[2个激酶] = 10753 sus以获得自由能数据。我们要求大约4000 SUS测试我们的内部并行以执行多种构型对接。总共我们要求5800 SUS+10753 SUS+4000 SUS = 20,554 SUS舍入到20,000 SUS。我们在Teragrid DAC拨款下要求这些，因为这是我们首次尝试运行跨平台模拟的尝试。 （我们对单个平台并行应用程序有一个MRAC更新提案）。