RECONSTRUCTION FROM HETEROGENEOUS MOLECULE POPULATIONS

从异质分子群重建

• 批准号: 7954575
• 负责人: JOACHIM FRANK
• 金额: $ 11.17万
• 依托单位: WADSWORTH CENTER
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-02-01 至 2010-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7954575
• 关键词: Accounting; Address; Affect; Algorithms; Architecture; Award; Binding; Bioinformatics; Biological; Bite; Classification; Collaborations; Communities; Computer Retrieval of Information on Scientific Projects Database; Computer software; Data; Data Set; Deposition; Development; Documentation; Escherichia coli; European; Frequencies; Funding; Future; Genes; Grant; Hand; Heterogeneity; Image; Imagery; Institutes; Institution; Journals; Ligand Binding; Ligands; Manuscripts; Maps; Memory; Methods; Molecular Conformation; Nature; Noise; Paper; Peptide Elongation Factor G; Pharmacy facility; Phase; Population; Preparation; Process; Publications; Reporting; Research; Research Personnel; Resolution; Resources; Ribosomes; Roentgen Rays; Sampling; Seeds; Signal Transduction; Source; Spiders; Structure; Students; Test Result; Testing; United States National Institutes of Health; Universities; Work; Writing; abstracting; base; density; design; experience; fortification; macromolecule; particle; prevent; reconstruction; research study; software development; structural biology; success; supercomputer

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. ABSTRACT: This TRD addresses a problem that is paramount in cryo-EM single-particle reconstruction of macromolecules, and that is in many cases the single obstacle preventing the attainment of high resolution (better than 10 ¿). This problem is the heterogeneity of molecules in the sample due to partial ligand occupancy and conformational variability. We will develop general approaches for the classification of heterogeneous molecule populations from their cryo-EM projections, which will include both supervised and unsupervised classification methods. We will interact with leading experts in this field and use typical data both from the PI's group and from other groups pursuing single-particle reconstruction. Resulting software, if successful, will be made available to a wide community. Specific Aims: 1) (Exploration phase): Explore methods of classification of single-particle projections that refine existing template-based approaches, or exploit general intrinsic mathematical relationships among projections of unchanged objects. In this phase of the project, algorithms such as self-organized (SOMs) will be designed, or the utility of existing ones explored. Phantom data sets are derived from existing density maps of molecules or from X-ray structures that present different conformations or states of ligand binding. Such maps are projected systematically into a variety of directions, the resulting projections are low-pass filtered and contaminated with noise. These data will allow a determination of which algorithm or which SOM configuration will perform best at different resolutions and signal-to-noise ratios. 2) (Testing phase): Test the resulting algorithms and SOMs on well-defined experimental cryo-EM data sets from single-particle projects that are conducted within and outside the Wadsworth Center. Ideally, these should be data that have been characterized in previous publications, so that the improvements due to the new classification approaches can be easily assessed. 3) (Dissemination phase): Integrate the software with existing SPIDER software and develop comprehensive documentation. Publication of the underlying concepts in explicit form will also allow other authors of software packages such as EMAN (Ludtke et al., 2001) to implement their own version, for wider dissemination. Choice of Maximum Likelihood Classification (ML3D) as standard A collaboration with the Jose-Maria Carazo group, our main collaborator in TRD3, produced remarkable results and this has evidently helped to popularize the Maximum-likelihood method within the 3DEM community. 90,000 ribosome images were classified according to EF-G binding and associated "ratcheting" changes in ribosome conformation. Following collaborative publication of the Nature Methods paper by Scheres et al. in 2007), there has been a surge of applications by several EM groups in the field. Because of the success of this approach, we have stopped pursuing the "cluster tracking" method (Fu et al., J. Structural Biology 2007) since efforts to expand the cluster tracking globally (in the hands of BMS student Jie Fu and RVBC-supported posrdoc Tanvir Shaikh) were unsuccessful (details to be found in Jie Fu's dissertation). Much larger datasets may be needed to pursue this particular development in the future. One of our collaborators, Dr. Harry Zuzan, is working on a GPU (graphics processing unit) implementation of Scheres' Maximum-likelihood method. Speedups of up to 100 might be expected. Dr. Zuzan is doing this as a private effort as he is now employed by a Pharmacy Company. He has promised to share the software as well as the hardware specifications with us once he succeeds. Construction of a Phantom Dataset To enable an objective comparison of classification methods, or parameter settings of any particular method, we set out to construct a phantom data set based on the E. coli ribosome with and without EF-G bound. We argued that such an effort would not only serve our own optimization efforts, but would also be welcomed by the entire 3DEM community. An analysis of the noise sources showed that an important source of noise, namely structural noise, had been overlooked in all previous attempts to produce phantom data. As described in the previous report, we conducted experiments to estimate the signal-to-noise ratio (SNR) of various steps of EM image formation, including the SNR of structural noise. The method and results of the estimation has been written up in a paper by Baxter et al., and submitted to the Journal of Structural Biology. The manuscript features both an estimation of the SNRs but also of their spectral distributions (SSNRs). Since the estimates of the SSNR distributions were of limited accuracy in the high-frequency range, the reviewers asked for an increase in the dataset for statistical fortification, and Dr. Baxter is now processing a larger dataset. However, this issue does not affect the accuracy of the SNR estimation. Concurrent with the preparation of a revised manuscript, we have therefore constructed a phantom dataset using the SNR values from our estimation, and have deposited the data with the European Bioinformatics Institute (EBI) in Cambridge. Experience with ML3D of Phantom Data, and Supercomputer applications Test computations for small datasets (decimated arrays and small number of images) showed very inconsistent results. The results were different for different choices of seeds, and this convinced us that we need to go to larger datasets to establish optimal settings. Our strategy was therefore to apply for a large allocation on the Teragrid. Dr. Baxter and Dr. Frank applied separately for accounts associated, respectively, with the RVBC at Wadsworth and accounts associated with Columbia University for the ribosome collaborative projects. On October 1, 2008 allocations of 100,000 and 450,000 were awarded. We had also initially hoped to be able to install XMIPP, the Madrid-based software in which ML3D is embedded, on RPI's Blue Gene. Unfortunately, incompatibility of The Blue Gene's 32-bit architecture with XMIPP and memory issues prevented progress with this particular supercomputer.

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。 抽象的： 该 TRD 解决了大分子冷冻电镜单粒子重建中最重要的问题，并且在许多情况下这是阻碍实现高分辨率（优于 10 ¿）的单一障碍。 这个问题是由于部分配体占据和构象变异导致的样品中分子的异质性。我们将开发根据冷冻电镜预测对异质分子群进行分类的通用方法，其中包括监督和无监督分类方法。 我们将与该领域的领先专家互动，并使用来自 PI 小组和其他追求单粒子重建小组的典型数据。 如果成功的话，最终的软件将提供给广大社区。 具体目标： 1)（探索阶段）：探索单粒子投影的分类方法，以改进现有的基于模板的方法，或利用未更改对象的投影之间的一般内在数学关系。 在此项目阶段，将设计自组织 (SOM) 等算法，或探索现有算法的实用性。 模型数据集源自现有的分子密度图或呈现不同配体结合构象或状态的 X 射线结构。 此类地图被系统地投影到各个方向，所得投影经过低通滤波并受到噪声污染。 这些数据将有助于确定哪种算法或哪种 SOM 配置在不同分辨率和信噪比下表现最佳。 2)（测试阶段）：在沃兹沃斯中心内外进行的单粒子项目的明确定义的实验冷冻电镜数据集上测试生成的算法和 SOM。 理想情况下，这些数据应该是以前出版物中描述过的数据，以便可以轻松评估新分类方法带来的改进。 3)（传播阶段）：将软件与现有的 SPIDER 软件集成并开发全面的文档。 以明确形式发布基本概念也将允许 EMAN（Ludtke 等人，2001）等软件包的其他作者实现他们自己的版本，以进行更广泛的传播。 选择最大似然分类 (ML3D) 作为标准 与我们在 TRD3 中的主要合作者 Jose-Maria Carazo 小组的合作产生了显着的成果，这显然有助于在 3DEM 社区内普及最大似然法。根据 EF-G 结合和核糖体构象的相关“棘轮”变化对 90,000 个核糖体图像进行分类。 Scheres 等人合作发表《自然方法》论文后。 2007 年），该领域的几个 EM 小组的申请激增。 由于这种方法的成功，我们已经停止追求“簇跟踪”方法（Fu et al., J. Structural Biology 2007），因为在全球范围内扩展簇跟踪的努力（由 BMS 学生 Jie Fu 和 RVBC 支持的 posrdoc Tanvir Shaikh 负责）不成功（详细信息可在 Jie Fu 的论文中找到）。 未来可能需要更大的数据集来实现这一特定的发展。 我们的合作者之一 Harry Zuzan 博士正在研究 Scheres 最大似然法的 GPU（图形处理单元）实现。 预计加速可达 100。 Zuzan 博士目前受雇于一家制药公司，因此他以个人名义完成了这项工作。 他承诺一旦成功，将与我们分享软件和硬件规格。 幻影数据集的构建 为了能够对分类方法或任何特定方法的参数设置进行客观比较，我们着手构建基于具有和不具有 EF-G 结合的大肠杆菌核糖体的模型数据集。 我们认为这样的努力不仅有利于我们自己的优化工作，而且还会受到整个 3DEM 社区的欢迎。 对噪声源的分析表明，在之前所有生成虚拟数据的尝试中，都忽略了一个重要的噪声源，即结构噪声。 如上一篇报告所述，我们进行了实验来估计电磁图像形成各个步骤的信噪比（SNR），包括结构噪声的信噪比。 Baxter等人将估算方法和结果写在论文中，并提交给《结构生物学杂志》。 该手稿不仅提供了 SNR 的估计，还提供了频谱分布 (SSNR) 的估计。 由于 SSNR 分布的估计在高频范围内的准确性有限，审稿人要求增加数据集以进行统计强化，而 Baxter 博士现在正在处理更大的数据集。 然而，这个问题并不影响SNR估计的准确性。 因此，在准备修订稿的同时，我们使用我们估计的 SNR 值构建了一个虚拟数据集，并将数据存放在剑桥的欧洲生物信息学研究所 (EBI)。 Phantom Data ML3D 和超级计算机应用程序的经验 小数据集（抽取数组和少量图像）的测试计算显示出非常不一致的结果。 不同选择的种子的结果是不同的，这使我们确信我们需要使用更大的数据集来建立最佳设置。 因此，我们的策略是在 Teragrid 上申请大量分配。 Baxter 博士和 Frank 博士分别申请了与沃兹沃思 RVBC 相关的帐户和与哥伦比亚大学相关的核糖体合作项目帐户。 2008 年 10 月 1 日，发放了 100,000 和 450,000 的拨款。 我们最初还希望能够在 RPI 的 Blue Gene 上安装 XMIPP，这是一款基于马德里、嵌入了 ML3D 的软件。 不幸的是，The Blue Gene 的 32 位架构与 XMIPP 的不兼容以及内存问题阻碍了这款特定超级计算机的进展。