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修订，以解决具体问题和担忧 审稿人提出的，并强调这一核心的原则重点。该组织的主要目标 计算核心是1）开发一套计算工具和协议以方便分析和 EPR 光谱数据的解释，包括从 DEER 实验获得的距离测量 双自旋标记蛋白质，2) 为各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，该计算支持包括详细的平衡 MD 模拟 CDB3探索P327R点突变体可能引发的构象变化，并初步 结果在项目 2 研究计划中进行了描述。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 引入淀粉样β肽。这种构象分析对于解决距离问题非常重要 EPR 实验和顺磁性中对自旋标签侧链构象行为的依赖性 共振增强核磁共振研究。 促进 EPR 数据分析的实用计算工具和协议的开发取决于 关键在于项目 1 中获得的数据，并且需要几个谨慎的步骤。首先，重要的是要确定 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 主干动力学，加上模拟全局蛋白质翻滚的布朗动力学计算， 直接计算单标记蛋白质的 EPR 谱。正如审稿人指出的，之前发表的尝试 利用这种类型的策略并不完全令人满意或令人信服。然而，之前的这些 研究是基于相当有限的MD模拟，并且可能受到我们所发现的一些其他问题的影响。 在下面的研究计划中更详细地说明了这一点。重要的是要建立一个模拟策略 用于计算 EPR 谱，以便确定我们可以捕获重要特征和行为 自旋标记的蛋白质可以为不同的样品产生独特的 EPR 光谱（例如，尖锐、独特的 完全移动自旋标签的典型光谱信号与更广泛、更复杂的信号 代表部分固定的旋转标签）。正如项目 1 研究计划中所讨论的，我们现在有 初步结果表明我们可以比以前更准确、更可靠地计算 EPR 谱 之前报道过。仍然需要改进，我们对当前基于MD的进行详细分析 下面的 EPR 光谱模拟强调了当前方法中可能存在的不足，并讨论 解决这些问题的具体策略和测试。只有在我们令人信服地确定我们 可以使用组合的 MD/布朗动力学模拟协议直接计算 EPR 谱，我们可以吗 认真解决EPR DEER实验中获得的自旋标签对距离的计算，或者追求 开发更简单的计算策略，不需要多次、冗长的 MD 模拟 显式溶剂来估计这些距离。许多问题影响自旋标签的可靠 MD 模拟 对距离，包括项目 1 审阅者提出的几个距离（例如，势函数参数、 静电处理、周期性边界效应等）我们在修订后的研究中提供了初步数据 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 谱计算和 DEER 距离估计是在新的具体目标 1 的背景下提出的。 最初提案中的目标 1（现在重新编号为具体目标 2）包含了之前的详细讨论 旨在探索（有限）远距离约束对 3D 结构模型的影响的研究 一代。审稿人 #1 指出，该目标中概述的总体策略是合理的，但相当耗时。 我们在下面注意到某些步骤的一些具体效率改进，这些步骤减少了总体 该协议的计算费用（尽管这仍然是一项不平凡的计算任务）。评论者 #1 也 注意到与这一目标相关的一些具体关切或建议。替代指标，例如骨干扭转 建议使用角度而不是蛋白质骨架 RMSD 值进行结构比较和聚类。 这当然是一个合理的建议，我们已经探索了一些简单的替代比较 指标。骨干扭转角比较，或其他简单的定量评估，例如体积或 形状描述符本质上很有吸引力，尽管这些指标对于结构来说不太“直观” 比较（至少对于现阶段的我们来说）。我们下面讨论使用主干扭转角作为潜在的 在提出的新工作中非常有用和有效的比较指标。我们也与大家讨论过这个问题 几位专注于蛋白质结构预测并因此执行此类计算的同事 例行公事地。有趣的是，我们被推荐回 SUPPOSE 算法进行主干 RMSD 比较 这些群体（该计划显然比我们意识到的更受欢迎）。评论者 #1 也 建议我们考虑实际聚类过程的替代程序，这是最重要的 合理的。我们的协议中没有任何内容要求我们使用 Jeff Barton 的“OC”程序，而且很简单 在我们的作业控制脚本中集成替代聚类算法，因此我们将在完成之后探索其他算法 确定了我们协议的适用性和范围。审稿人 #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 提出的担忧密切相关，即每次运行将有 10,000-20,000 个试验结构 不足以充分采样 3D 结构空间。我们相信，一套适当的长期 距离约束将充分限制可行的 3D 结构解空间，以降低这种情况的严重性 问题。我们之前的结果以及其他几个研究小组的结果清楚地表明， 少量的远程距离约束可以显着减少 3D 构象搜索空间 对于蛋白质模型构建，虽然不能保证任意一组长程距离 约束条件将实现这一目标，我们必须执行特定目标 2 中概述的额外测试，以更好地 了解相对较小的远程距离约束集合在减少 搜索空间。我们还描述了一种提高试验结构结构“多样性”的新策略， 它利用 Rosetta 中纳入的 3D 模型生成技术（Wollacott 等人，2007 年；Rohl 等人， 2004）。两位评审员都对用于“排名”结构解决方案的评分函数表示担忧。 这里没有简单或明显的答案，我们只能遵循研究计划中概述的策略 以下。我们对这个问题的真正解决方案是使用模型生成的迭代过程和额外的 DEER 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记位点的策略进行更详细的讨论，以说明更多 按照审稿人 #1 的要求，我们清楚地期望这个过程将如何进行。我们还提供了更详细的 解释我们如何将 3D 模型生成协议与基序识别和同源性结合起来 我们迄今为止研究的测试系统的建模技术。最后，我们在修订后的目标2中讨论 以及在模型中包含超出残基间距离的额外 EPR 实验数据的 3 种方法 生成和细化过程。我们应该再次强调，中概述的计算目标 具体目标 2 是生成低到中分辨率的 3D 模型。这个阶段不适合说话 关于在相同上下文中根据 EPR DEER 距离测量进行真实 3D 结构细化，对于 例如，传统的 NMR 或 X 射线结构细化程序。此时更现实的目标是 以前未表征的蛋白质的结构基序识别，以及我们之前对测试系统的研究 下面的介绍表明这是可行的。 最初提案中的目标 2（现在的具体目标 3）主要侧重于开发分析工具 从 DEER 测量中获得的残基间距离。本节已作重大修改，以 更好地描述这项工作与项目 1 的紧密集成，并解决由 审稿人。为各种战略提供了更多方法细节，并计划实施 更详细地讨论了粗粒度模型。我们讨论与充分性相关的问题 深度构象采样，以及计算结果“验证”的标准，例如距离 分布。审稿人 #1 还提出了关于约束质量的问题，这是一个相当棘手的问题 与 EPR 距离测量。在某些情况下，测量的距离表现出较大的距离 分布可能被归类为质量较低的数据点（至少在 3D 模型构建或 细化）。然而，EPR 领域的许多人会对这种描述提出异议，认为正确 大距离分布本身就是一个重要且信息丰富的数据。我们讨论这个问题 新的特定目标 3 中有更多详细信息。 最初的目标 3（现在的目标 4）主要需要“工具包”设计和应用到项目 1-3 中的特定任务， 其次是包装以更广泛地传播给一般用户社区。这些目标没有改变 来自最初的提案。 研究计划中的主要修订用粗体方括号 [] 围绕相关文本进行划分。