COMPOUNDS FOR SELECTIVE KINASE INHIBITION

用于选择性激酶抑制的化合物

• 批准号: 7723276
• 负责人: Ravi Radhakrishnan
• 金额: $ 0.05万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7723276
• 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).

该子项目是利用该技术的众多研究子项目之一 资源由 NIH/NCRR 资助的中心拨款提供。子项目及 研究者 (PI) 可能已从 NIH 的另一个来源获得主要资金， 因此可以在其他 CRISP 条目中表示。列出的机构是 对于中心来说，它不一定是研究者的机构。 各种激酶之间激酶抑制剂的交叉反应性是设计对某些激酶具有特异性的抑制剂分子的一大障碍。直到最近，宾夕法尼亚大学才设计出对糖原合成酶激酶 3 (GSK-3) 具有特异性的钌有机金属配体抑制剂分子，但控制这种特异性的原因尚不清楚。一般来说，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课题，仍然需要研究。更好地理解这个问题很重要，因为激酶特异性抑制剂可用于破坏病理细胞中关键信号传导途径所涉及的激酶的活性，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，我们开发了一种使用隐式和显式协议来表征抑制剂结合模式和亲和力（自由能）的分层计算策略。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，因此能够捕获蛋白质的灵活性。使用这种多构象对接策略检查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灵活的蛋白质和配体系统的更严格的显式方法将用于细化先导结构的结合能量学。该策略预计将为我们正在研究的有机金属抑制剂类激酶特异性的起源提供重要的见解。我们将比较基于钌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，我们已经能够在纯水中对三种激酶（GSK-3、PIM-1 和 CDK-2）进行 10 ns MD 模拟（使用 NAMD）。我们还利用 AutoDock 在钌基有机金属抑制剂分子与三种激酶之间进行了模拟单框架对接。我们建议自动化钌基有机金属抑制剂与三种激酶之间的模拟对接，以通过我们内部的并行代码并行执行多构象对接。我们建议在 teragrid 上跨平台测试此并行代码，并为此请求 20,000 个 SU。每个单帧对接在 NCSA tungsten 或 SDSC datastar 上需要相当于 18 个 CPU 小时。对于每个激酶系统，我们将执行三个 32 处理器并行运行 18 小时，以处理从 MD 模拟中获取的 100 个快照。这相当于 [18 个 CPU 小时]*[32 个处理器]*[每个激酶 3 次运行]*[三个激酶系统]=5800 SU。对于所得的最低结合配置（对于两种激酶，即 PIM-1 和 GSK-3），我们建议进行伞式采样模拟以细化结合的结合自由能。这需要 [每 ns 每个处理器 24 个 CPU 小时]*[32 个处理器]*[使用 NAMD 每伞 1 ns MD]*[每个激酶 7 个伞]*[2 个激酶]=10753 SU 才能获取自由能数据。我们要求大约 4000 个 SU 来测试我们的内部并行以执行多构象对接。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。我们在 teragrid DAC 拨款下请求这些，因为这是我们第一次尝试运行跨平台模拟。 （我们的单一平台并行应用程序有一个 MRAC 续订提案正在等待中）。