Project 1/Computational Core

项目1/计算核心

• 批准号: 7449170
• 负责人: TERRY P LYBRAND
• 金额: $ 10.65万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-05-01 至 2013-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7449170
• 关键词: Accounting; Address; Agreement; Algorithms; Amyloid beta-Protein; Aprotinin; Back; Behavior; Belief; Cell Nucleus; Cereals; Classification; Collection; Commit; Communities; Complex; Computational Technique; Computer software; Coupled; Crystallography; Data; Data Analyses; Data Quality; Data Set; Deer; Dependency; Depth; Descriptor; Development; Electron Spin Resonance Spectroscopy; Electrostatics; Environment; Equilibrium; Evaluation; Exhibits; Facility Construction Funding Category; Fluorescence Resonance Energy Transfer; Generations; Goals; Homology Modeling; In Situ; Individual; Label; Laboratories; Ligand Binding Domain; Magnetism; Measurement; Measures; Methodology; Methods; Metric; Modeling; Modification; Muramidase; NMR Spectroscopy; Nature; Numbers; Occupations; Positioning Attribute; Procedures; Process; Protein Dynamics; Proteins; Protocols documentation; Protons; Publishing; Range; Recommendation; Relaxation; Reporting; Research; Research Design; Research Project Grants; Resolution; Running; Sample Size; Sampling; Score; Series; Severities; Shapes; Side; Signal Transduction; Site; Solutions; Solvents; Spectrum Analysis; Spin Labels; Staging; Structural Models; Structural Protein; Structure; Suggestion; System; Techniques; Testing; Text; Time; Torsion; Validation; Vertebral column; Work; base; caN protocol; comparison group; computerized tools; concept; design; extracellular; globular protein; improved; models and simulation; mutant; nitroxyl; programs; protein structure; protein structure prediction; research study; secretin receptor; simulation; three dimensional structure; three-dimensional modeling; tool; tool development; trend; user-friendly

The Computational Core research plan has been revised to address specific questions and concerns raised by the reviewers, and to emphasize the principle focus of this core. The primary goals of the Computational Core are 1) to develop a set of computational tools and protocols to facilitate the analysis and interpretation of EPR spectral data, including distance measurements obtained from DEER experiments for doubly spin-labeled proteins, and 2) provide basic computational support for the individual research projects. For Project 1, basic computational support entails a series of equilibrium MD simulations to support EPR spectral calculations. In Project 2, this computational support includes detailed equilibrium MD simulations for CDB3 to explore possible conformational changes triggered by the P327R point mutant, and preliminary results are described above in the Project 2 Research Plan. Basic computational support for Project 3 includes routine structure refinement calculations for conventional 2D-NMR experiments and paramagnetic resonance enhancement NMR experiments, as well as MD simulations to explore conformational trends for spin labels introduced in the amyloid-beta peptides. This conformational analysis will be important to address distance dependencies on spin label side chain conformational behavior in both EPR experiments and paramagnetic resonance enhancement NMR studies. The development of practical computational tools and protocols to facilitate EPR data analysis depends crucially on data obtained in Project 1, and requires several discreet steps. First, it is important to establish that we can use conventional equilibrium MD simulations that describe spin label side chain dynamics and protein backbone dynamics, coupled with Brownian dynamics calculations that model global protein tumbling, to compute EPR spectra directly for singly labeled proteins. As the reviewers noted, previous published attempts to exploit this type of strategy have not been completely satisfactory or convincing. However, these previous studies were based on rather limited MD simulations, and possibly suffered from some other issues that we address in more detail in the Research Plan below. It is essential to establish that a simulation strategy can be used to compute EPR spectra, in order to establish that we can capture the important features and behavior of spin-labeled proteins that give rise to unique EPR spectra for different samples (e.g., the sharp, distinct spectral signal typical of a completely mobile spin label versus the broader, more complex signals representative of partially immobilized spin labels). As discussed in the Project 1 Research Plan, we now have preliminary results that indicate we can compute EPR spectra more accurately and reliably than has been reported previously. There is still need for improvement, and we present detailed analysis of current MD-based EPR spectral simulations below that highlight possible inadequacies in the current methodology, and discuss specific strategies and tests to address these problems. Only after we have established convincingly that we can calculate EPR spectra directly with the combined MD/Brownian dynamics simulation protocol can we address seriously the calculation of spin label pair distances obtained in EPR DEER experiments, or pursue development of simpler computational strategies that do not require multiple, lengthy MD simulations with explicit solvent to estimate these distances. A number of issues impact the reliable MD simulation of spin label pair distances, including several raised by the reviewers for Project 1 (E.g., potential function parameters, electrostatics treatment, periodic boundary effects, etc.) We present preliminary data in the revised Research Plan below that addresses these issues and other important factors, and the strategies to achieve improved EPR spectral calculations and DEER distance estimates are presented in the context of a new Specific Aim 1. Aim 1 in the original proposal (now renumbered Specific Aim 2) contained a detailed discussion of previous studies designed to explore the impact of (limited) long-range distance constraints on 3D structural model generation. Reviewer #1 noted that the general strategy outlined in this Aim was reasonable, but rather timeconsuming. We note below some specific efficiency improvements for certain steps that reduce the overall computational expense for this protocol (although this is still a non-trivial computational task). Reviewer #1 also noted several specific concerns or suggestions related to this aim. Alternate metrics, such as backbone torsion angles rather than protein backbone RMSD values, were suggested for structural comparisons and clustering. This is certainly a reasonable recommendation, and we have explored some simple alternative comparison metrics. Backbone torsion angle comparisons, or other simple quantitative assessments such as volumetric or shape descriptors are intrinsically appealing, although those metrics are somewhat less "intuitive" for structural comparison (at least for us at this stage). We discuss below the use of backbone torsion angles as a potentially quite useful and efficient comparison metric in new work proposed. We have also discussed this issue with several colleagues who focus on protein structure prediction and thus perform these types of calculations routinely. Interestingly, we were referred back to the SUPPOSE algorithm for backbone RMSD comparisons by these groups (this program has clearly become more popular than we realized). Reviewer #1 also recommended that we consider alternate programs for the actual clustering process, and this is most reasonable. Nothing in our protocol commits us to use Jeff Barton's "OC" program, and it is straightforward to integrate alternate clustering algorithms in our job control scripts, so we will explore other algorithms after we have established the applicability and scope of our protocol. Reviewer #1 also suggested that we consider strategies to enhance the structural "diversity" in our relatively small 3D model datasets; this suggestion is closely coupled to the concern raised by reviewer #2 that 10,000-20,000 trial structures per run will be inadequate to sample 3D structural space adequately. It is our belief that an appropriate set of long-range distance constraints will limit the feasible 3D structural solution space sufficiently to reduce the severity of this problem. Our previous results, as well as those of several other research groups, have shown clearly that a small number of long-range distance constraints can dramatically reduce the 3D conformational search space for protein model construction, although there is no guarantee that any arbitrary set of long-range distance constraints will achieve this goal, and we must perform additional tests outlined in Specific Aim 2 to better understand how effective a relatively small collection of long-range distance constraints might be in reducing the search space. We also describe a new strategy to improve the structural "diversity" of the trial structures, which utilizes 3D model generation techniques incorporated in Rosetta (Wollacott, et al., 2007; Rohl, et al., 2004). Both reviewers expressed concerns regarding the scoring functions used to "rank" structural solutions. There is no easy or obvious answer here, and we can only pursue the strategies outlined in the Research Plan below. Our real solution to this problem is to use an iterative process of model generation and additional DEER distance measurements to systematically reduce the number of acceptable structural models. We now provide a more detailed discussion of the strategy we use for selection of additional labeling sites to illustrate more clearly how we expect this process will work, as requested by Reviewer #1. We also provide a more detailed explanation for how we have coupled the 3D model generation protocol with motif identification and homology modeling techniques for the test systems we have studied to date. Finally, we discuss in the revised Aims 2 and 3 ways to include additional EPR experimental data beyond inter-residue distances in the model generation and refinement procedures. We should reemphasize that the goal for calculations outlined in Specific Aim 2 is generation of low- to intermediate-resolution 3D models. It is inappropriate at this stage to talk about true 3D structure refinement from EPR DEER distance measurements in the same context as, for example, conventional NMR or x-ray structure refinement procedures. A more realistic goal at this point is structural motif identification for previously uncharacterized proteins, and our previous studies for test systems presented below suggest that this is feasible. Aim 2 in the original proposal (now Specific Aim 3) focused primarily on development of tools for analysis of inter-residue distances obtained from DEER measurements. This section has been modified significantly to better describe the tight integration of this work with Project 1, as well as to address various concerns raised by the reviewers. More methodological detail is provided for various strategies, and the planned implementation of coarse-grained models is discussed in greater detail. We discuss issues related to the adequacy of conformational sampling in depth, and criteria for "validation" of computed results such as distance distributions. Reviewer #1 also raised a question regarding constraint quality, and this is a rather tricky issue with EPR distance measurements. In some contexts, a measured distance that exhibits a large distance distribution might be classified as a lesser-quality data point (at least in the context of 3D model construction or refinement). However, many in the EPR field would take exception to such a characterization, arguing correctly that a large distance distribution is itself an important and informative piece of data. We discuss this issue in more detail in the new Specific Aim 3. Original Aim 3 (now Aim 4) entails primarily "toolkit" design and application to specific tasks in Projects 1-3, followed by packaging for wider dissemination to the general user community. These goals are unmodified from the original proposal. Major revisions in the Research Plan are demarcated by bold square brackets [] around the relevant text.

计算核心研究计划已进行了修订，以解决特定的问题和疑虑 由审稿人提出，并强调该核心的主要重点。主要目标 计算核心是1）开发一组计算工具和协议，以促进分析和 EPR光谱数据的解释，包括从鹿实验获得的距离测量值 双重标记的蛋白质和2）为单个研究项目提供基本的计算支持。 对于项目1，基本计算支持需要一系列平衡MD模拟来支持EPR 光谱计算。在项目2中，此计算支持包括详细的平衡MD模拟 CDB3探索p327R点突变体触发的可能的构象变化，并初步 结果2在项目2研究计划中描述。项目3的基本计算支持包括 常规2D-NMR实验和顺磁共振的常规结构细化计算 增强NMR实验以及MD模拟，以探索自旋标签的构象趋势 在淀粉样β肽中引入。这种构象分析对于解决距离很重要 EPR实验和顺磁性的自旋标签侧链构象行为的依赖性 共振增强NMR研究。 开发实用计算工具和协议以促进EPR数据分析取决于 至关重要的是在项目1中获得的数据，并且需要几个谨慎的步骤。首先，必须确定 我们可以使用传统的平衡MD模拟来描述自旋标签侧链动力学和蛋白质 主链动力学，加上对全球蛋白质翻滚的布朗动力学计算， 直接计算EPR光谱，以用于单个标记的蛋白质。正如审稿人指出的那样，先前发表的尝试 利用这种策略并不是完全令人满意或令人信服。但是，这些先前 研究是基于相当有限的MD模拟，并且可能遇到了我们的其他一些问题 在下面的研究计划中更详细地解决。必须确定模拟策略可以是 用于计算EPR光谱，以确定我们可以捕获的重要特征和行为 自旋标记的蛋白质可为不同样品产生独特的EPR光谱（例如，清晰，独特的 与更广泛，更复杂的信号的光谱信号典型的光谱信号 代表部分固定的自旋标签）。正如项目1研究计划中所讨论的那样，我们现在已经 初步结果表明我们可以比以前更准确，更可靠地计算EPR光谱 先前报道了。仍然需要改进，我们介绍了当前基于MD的详细分析 下面的EPR光谱模拟突出了当前方法中可能的不足，并讨论 具体的策略和测试以解决这些问题。只有在我们令人信服地确定我们 可以直接使用组合的MD/Brownian动力学模拟协议来直接计算EPR光谱 认真解决EPR鹿实验中获得的自旋标签对距离的计算，或追求 制定不需要多个冗长的MD模拟的更简单的计算策略 明确的溶剂以估计这些距离。许多问题影响了自旋标签的可靠MD模拟 配对距离，包括审稿人为项目1提出的几个（例如，潜在函数参数， 静电处理，周期性边界效应等）我们在修订的研究中介绍了初步数据 下面的计划解决了这些问题和其他重要因素，以及实现改进的策略 EPR光谱计算和鹿距离估计是在新的特定目标1的背景下进行的。 AIM 1在原始提案中（现在重新编号的特定目标2）包含了先前的详细讨论 旨在探索（有限）远程距离限制对3D结构模型的影响的研究 一代。评论者＃1指出，此目标中概述的一般策略是合理的，但时间是耗时。 我们在下面注意到某些步骤的一些特定效率提高，以减少整体 该协议的计算费用（尽管这仍然是一项非平凡的计算任务）。审稿人也是1 注意到与此目标有关的几个具体问题或建议。替代指标，例如骨干扭转 提出了为结构比较和聚类的角度而不是蛋白质主链RMSD值。 这当然是一个合理的建议，我们探索了一些简单的替代比较 指标。骨干扭转角度比较或其他简单的定量评估，例如体积或 形状描述符本质上具有吸引力，尽管这些指标对于结构而言的“直观”程度较小 比较（至少在这个阶段对我们来说）。我们在下面讨论骨干扭转角作为潜在的 提出的新工作中非常有用和有效的比较度量。我们还与 几个专注于蛋白质结构预测并因此执行这些类型的计算的同事 常规。有趣的是，通过 这些小组（显然，该计划比我们意识到的更受欢迎）。审稿人也是1 建议我们考虑用于实际聚类过程的替代程序，这是最多的 合理的。我们的协议中没有任何东西可以使用Jeff Barton的“ OC”程序，这很直接 在我们的工作控制脚本中整合替代聚类算法，因此我们将在我们之后探索其他算法 已经建立了我们协议的适用性和范围。评论者＃1还建议我们考虑 在我们相对较小的3D模型数据集中增强结构“多样性”的策略；这个建议是 与审稿人＃2提出的关切密切相关，每次运行10,000-20,000个试用结构将是 不足以充分采样3D结构空间。我们相信一组适当的远程 距离限制将充分限制可行的3D结构解决方案空间，以降低其严重性 问题。我们以前的结果以及其他几个研究小组的结果清楚地表明 少量的长距离距离限制可以大大减少3D构象搜索空间 对于蛋白质模型的构建，尽管不能保证任何任意的远距离距离 约束将实现此目标，我们必须在特定目标2中概述的其他测试以更好 了解相对较小的远程距离限制的有效性可能如何减少 搜索空间。我们还描述了一种改善试验结构的结构“多样性”的新策略， 它利用了Rosetta中纳入的3D模型生成技术（Wollacott等，2007； Rohl等，等等 2004）。两位审阅者都对用于“排名”结构解决方案的评分功能表示担忧。 这里没有简单或明显的答案，我们只能采用研究计划中概述的策略 以下。我们解决这个问题的真正解决方案是使用模型生成和其他鹿的迭代过程 距离测量系统地减少可接受的结构模型的数量。我们现在提供 对我们用于选择其他标签网站的策略的更详细讨论以说明更多 显然，按照审阅者＃1的要求，我们期望这个过程将如何工作。我们还提供了更详细的 解释我们如何与基主题识别和同源性结合3D模型生成协议 我们迄今为止研究的测试系统建模技术。最后，我们在修订的目标2中讨论2 和三种方法，包括模型中的其他EPR实验数据以外的占空间距离 生成和改进程序。我们应该重申，计算的目标 具体目标2是生成低至中间分辨率3D模型的生成。在这个阶段说话是不合适的 关于在EPR鹿距离测量中的真实3D结构的改进，与 例如，常规的NMR或X射线结构改进程序。此时一个更现实的目标是 先前未表征的蛋白质的结构基序鉴定以及我们先前针对测试系统的研究 下面介绍的表明这是可行的。 目标2在原始建议中（现在是特定的目标3）主要集中于开发分析工具 从鹿测量结果获得的占空间距离的距离。本节已大大修改为 更好地描述这项工作与项目1的紧密整合，以及解决由 评论者。为各种策略提供了更多方法论细节，并计划实施 更详细地讨论了粗粒模型。我们讨论与适当性有关的问题 深度构象采样和计算结果的“验证”标准，例如距离 分布。评论者＃1还提出了一个有关约束质量的问题，这是一个相当棘手的问题 具有EPR距离测量值。在某些情况下，表现出较大距离的测量距离 分布可能被归类为较小质量的数据点（至少在3D模型构建或 改进）。但是，EPR领域中的许多人都会例外，以正确地争论 距离分布本身就是重要且有益的数据。我们在 新的特定目标3中的更多详细信息。 原始目标3（现在目标4）主要是“工具包”设计和应用于项目1-3中的特定任务 然后将更广泛的传播包装给普通用户社区。这些目标没有修改 来自原始建议。 研究计划中的重大修订由相关文本围绕相关文本进行了大胆的方括号[]。