TRANSITION PATHWAY OF NITROGEN REGULATORY PROTEIN C (NTRC)

氮调节蛋白 C (NTRC) 的转变途径

• 批准号: 8171908
• 负责人: DOROTHEE KERN
• 金额: $ 0.11万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8171908
• 关键词: Accounting; Affect; Algorithms; Amino Acids; Behavior; Code; Computer Retrieval of Information on Scientific Projects Database; Equilibrium; Evolution; Free Energy; Funding; Goals; Grant; Institution; Measurement; Measures; Methodology; Methods; Molecular Conformation; Nature; Pathway interactions; Performance; Phosphorylation; Policies; Population; Process; Protein C; Proteins; Publishing; Relaxation Techniques; Research; Research Personnel; Resources; Rest; Running; Sampling; Scheme; Science; Signaling Protein; Solutions; Source; Techniques; Testing; Time; United States National Institutes of Health; Wolves; base; computer studies; conformational conversion; improved; insight; millisecond; molecular dynamics; mutant; nitrogen-regulated response proteins; protein folding; research study; response; statistics

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. Molecular dynamics simulations will be performed on a signaling protein NtrC (Nitrogen regulatory protein C) to elucidate the free energy landscapes between its active and inactive states, and to gain insight on the conformational transition mechanism. Previously, our lab has shown by experiments that both conformations exist in solution, even in absence of phosphorylation [1, 2]. The protein is thus able to freely interconvert between the two states, and the phosphorylation simply shifts the equilibrium between the two population. Preliminary computational studies in our lab, mainly relying on Targeted Molecular Dynamics, have suggested a possible pathway for the conformational change [3]. In particular it has been found that the helix 4, the one that undergoes the largest conformational change and have been suggested to unfold in computational studies by other groups, is almost stable throughout the transition. The transition occurs through a number of intermediate metastable states stabilized by transient interactions among a few specific amino-acids. These transitional interactions, not present in either of the two end states, keep the free energy barriers low, allowing an interconversion timescale of the order of the high microsecond to millisecond. A set of experiments have been carried out to verify the reliability of the computational findings. In particular measurements on the unfolding of the helix 4 have shown that the helix has the same stability as the rest of the protein and the unfolding occurs on time scales too slow to account for the conformational transition. We have also measured the interconversion rate in mutants in which the transient interactions have been removed. These experimental results show that the stability of active and inactive states is not affected, while the barrier separating them is significantly increased by the destabilization of the transient metastable states. This reverberates in a slowing down of the interconversion rate, confirming the extreme importance of the metastable states for the correct functionality of the protein [4]. Stimulated by these promising results, we aim at understanding the mechanism of interconversion in its completeness, sampling extensively the ensemble of the most probable transition pathways. We will pursue this task combining minimum free energy pathway calculation techniques (in particular the string method [5]), algorithms for enhancing the exploration of the free energy landscape ( bias exchange metadynamics [6]), and schemes for efficiently sampling the space of the transition paths (transition path sampling [7]). We will rely on the start-up Roaming account mainly to perform tests on the different machines available at Teragrid to accurately evaluate the amount of needed resources and to devise an optimal strategy to achieve our goal. The calculations with the string method will be performed with NAMD. After having tested various possible trajectory durations, we will evaluate the performance of the MD runs, and, based on the performances, queue waiting times, and policies of the different facilities, we will decide the best scheme to optimize the efficiency of the calculation. As the result of string method is known to be in great measure affected by the choice of the initial guess of the pathways, we will combine this with other methods, in order to have reasonable initial guesses of possible alternative pathways to analyze. The standard metadynamics algorithm is both implemented in NAMD and available in GROMACS as an external plugin [8]. The metadynamics bias exchange scheme has been completely implemented by the developers of the methodology within Gromacs 3.3, modifying the facility for the replica exchange already in the code. This implementation takes advantage of the parallelization of Gromacs to perform the swap among the replicas, while the dynamical evolution of the single replicas is a single core process. An alternative would be preparing a set of scripts for performing the exchange indipendently from the MD runs. This will allow us to use the internal parallelization of the code for running the single metadynamics runs faster, but would require a large number of short independent runs. The more efficient choice will depend on the features of the facilities we will have access to, including the the queue times and the amount of resource we will access to. We will therefore need to perform tests on the different machines to select the optimal strategy. Once we will have collected a set of possible patways connecting the two basins, we want to accurately sample the ensemble of transition pathways, and for this we will use Transition Path Sampling (TPS) [7], which performs Monte Carlo sampling in the space of the reactive trajectories. We have performed preliminary calculations in CHARMM, and we are preparing scripts to run the algorithm in NAMD. We can guess from preliminary analysis that the free energy profile connecting the two relevant states is populated by a number of metastable states, that we hope to identify with the metadynamics calculations. For this reason, we will make use of the recently published version of the algorithm proposed by Rogal et Al [9], which is particularly suitable when multiple metastable states are present. The most challenging task in applying TPS will be to determine a minimal but complete set of order parameters that identify the stable or metastable states. To do this we will perform test runs and carefully analyze the resulting trajectories. Once we have obtained a satisfactory definition of the basins, we will perform a large number of runs, in order to improve the statistics of the sampled reactive trajectories. REFERENCES: [1] B. F. Volkman, D. Lipson, D. E.Wemmer, D. Kern, Two-state allosteric behavior in a single-domain signaling protein, Science 291(5512) (2001). [2] A. Gardino, D. Kern, Functional dynamics of response regulators using NMR relaxation techniques, Methods Enzymol 423 (2007). [3] M. Lei, J. Velos, A. Gardino, A. Kivenson, M. Karplus and D. Kern, Segmented transition pathways of the signaling protein NtrC, J Mol Biol (2009) submitted. [4] A. Gardino, J. Velos, M. Lei, A. Kivenson, C. F Liu, P. Steindel, E. Z. Eisenmesser, W. Labeikovsky, M. Wolf-Watz and D. Kern, Native-state energy landscape reveals activation pathway in a signaling protein, Nature (2009) submitted. [5] A. C. Pan, D. Sezer, B. Roux, Finding transition pathways using the string method with swarms of trajectories, J Phys Chem B 112 (11) (2008) 3432. [6] S. Piana, A. Laio, A Bias-Exchange Approach to Protein Folding, J. Phys. Chem. B, 2007, 111 (17). [7] P. G. Bolhuis, D. Chandler, C. Dellago, P. L. Geissler, Transition path sampling: Throwing ropes over rough mountain passes, in the dark, Ann Rev Phys Chem 53 (1) (2002). [8] http://merlino.mi.infn.it/~plumed/PLUMED/Home.html [9] J. Rogal, and P. G. Bolhuis, Multiple state transition path sampling, J. Chem. Phys. 129, 224107 (2008).

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。子项目和 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。 将对信号蛋白 NtrC（氮调节蛋白 C）进行分子动力学模拟，以阐明其活性和非活性状态之间的自由能景观，并深入了解构象转变机制。此前，我们实验室通过实验表明，即使没有磷酸化，这两种构象也存在于溶液中 [1, 2]。因此，该蛋白质能够在两种状态之间自由地相互转换，并且磷酸化只是改变了两个群体之间的平衡。我们实验室主要依靠靶向分子动力学进行的初步计算研究提出了构象变化的可能途径[3]。特别是，我们发现，螺旋 4 是经历最大构象变化的螺旋，并且其他研究小组已建议在计算研究中展开螺旋 4，它在整个转变过程中几乎是稳定的。这种转变是通过一些中间亚稳态发生的，这些亚稳态通过一些特定氨基酸之间的瞬时相互作用来稳定。这些过渡相互作用在两个最终状态中都不存在，使自由能垒保持较低，从而允许高微秒到毫秒量级的相互转换时间尺度。已经进行了一组实验来验证计算结果的可靠性。特别是对螺旋 4 展开的测量表明，螺旋与蛋白质的其余部分具有相同的稳定性，并且展开发生的时间尺度太慢，无法解释构象转变。我们还测量了瞬时相互作用已被消除的突变体的相互转化率。这些实验结果表明，活性和非活性状态的稳定性不受影响，而分隔它们的势垒由于瞬态亚稳态的不稳定而显着增加。这会导致相互转化率减慢，证实亚稳态对于蛋白质的正确功能至关重要[4]。受到这些有希望的结果的刺激，我们的目标是全面了解相互转化的机制，对最可能的转换途径进行广泛采样。我们将结合最小自由能路径计算技术（特别是弦法[5]）、增强自由能景观探索的算法（偏差交换元动力学[6]）以及有效采样过渡路径空间的方案（过渡路径采样[7]）来完成这项任务。我们将主要依靠初创公司的Roaming帐户在Teragrid可用的不同机器上进行测试，以准确评估所需资源的数量并制定最佳策略来实现我们的目标。使用字符串方法的计算将使用 NAMD 执行。在测试了各种可能的轨迹持续时间后，我们将评估MD运行的性能，并根据不同设施的性能、队列等待时间和策略，决定优化计算效率的最佳方案。由于已知串法的结果在很大程度上受到路径初始猜测选择的影响，因此我们将其与其他方法结合起来，以便对可能的替代路径进行合理的初始猜测进行分析。标准元动力学算法既在 NAMD 中实现，又在 GROMACS 中作为外部插件使用 [8]。元动力学偏差交换方案已由 Gromacs 3.3 中方法的开发人员完全实现，修改了代码中已有的副本交换设施。该实现利用 Gromacs 的并行性来执行副本之间的交换，而单个副本的动态演化是一个单核进程。另一种方法是准备一组脚本，用于独立于 MD 运行执行交换。这将使我们能够使用代码的内部并行化来运行单个元动力学，运行速度更快，但需要大量短暂的独立运行。更有效的选择将取决于我们将访问的设施的功能，包括排队时间和我们将访问的资源量。因此，我们需要在不同的机器上进行测试以选择最佳策略。一旦我们收集了一组连接两个盆地的可能路径，我们想要准确地对过渡路径的集合进行采样，为此，我们将使用过渡路径采样（TPS）[7]，它在反应轨迹空间中执行蒙特卡罗采样。我们已经在CHARMM中进行了初步计算，我们正在准备在NAMD中运行算法的脚本。从初步分析中我们可以猜测，连接两个相关态的自由能剖面由许多亚稳态组成，我们希望通过元动力学计算来识别这些亚稳态。因此，我们将利用 Rogal 等人 [9] 提出的最近发布的算法版本，该算法特别适合存在多个亚稳态的情况。应用 TPS 中最具挑战性的任务是确定一组最小但完整的序参数，用于识别稳定或亚稳态。为此，我们将进行测试运行并仔细分析产生的轨迹。一旦我们获得了令人满意的盆地定义，我们将进行大量运行，以改进采样反应轨迹的统计数据。参考文献：[1] B. F. Volkman、D. Lipson、D. E.Wemmer、D. Kern，单域信号蛋白中的两种状态变构行为，Science 291(5512) (2001)。 [2] A. Gardino, D. Kern，使用 NMR 弛豫技术的响应调节器的功能动力学，Methods Enzymol 423 (2007)。 [3] M. Lei、J. Velos、A. Gardino、A. Kivenson、M. Karplus 和 D. Kern，信号蛋白 NtrC 的分段转换途径，J Mol Biol (2009) 提交。 [4] A. Gardino、J. Velos、M. Lei、A. Kivenson、C. F Liu、P. Steindel、E. Z. Eisenmesser、W. Labeikovsky、M. Wolf-Watz 和 D. Kern，天然状态能量景观揭示信号蛋白中的激活途径，Nature (2009) 提交。 [5] A. C. Pan, D. Sezer, B. Roux，使用轨迹群的弦法寻找转换路径，J Phys Chem B 112 (11) (2008) 3432。 [6] S. Piana, A. Laio，蛋白质折叠的偏差交换方法，J. Phys.化学。 B，2007，111（17）。 [7] P. G. Bolhuis、D. Chandler、C. Dellago、P. L. Geissler，过渡路径采样：在黑暗中在崎岖的山口上扔绳子，Ann Rev Phys Chem 53 (1) (2002)。 [8] http://merlino.mi.infn.it/~plumed/PLUMED/Home.html [9] J. Rogal 和 P. G. Bolhuis，多状态转换路径采样，J. Chem。物理。 129, 224107 (2008)。