RECONSTRUCTION FROM HETEROGENEOUS MOLECULE POPULATIONS

从异质分子群重建

• 批准号: 8172273
• 负责人: JOACHIM FRANK
• 金额: $ 5.41万
• 依托单位: WADSWORTH CENTER
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-04-07 至 2011-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8172273
• 关键词: Address; Algorithms; Architecture; Binding; Bioinformatics; Classification; Collaborations; Communities; Complex; Computer Retrieval of Information on Scientific Projects Database; Computer software; Data; Data Set; Deposition; Development; Documentation; Escherichia coli; Eukaryota; European; Funding; Future; Grant; Hand; Heterogeneity; Image; Institutes; Institution; Journals; Ligand Binding; Ligands; Light; Maps; Medical center; Mentorship; Methods; Molecular Conformation; Nature; Noise; Paper; Peptide Elongation Factor G; Pharmacy facility; Phase; Population; Postdoctoral Fellow; Process; Publications; Publishing; Reporting; Research; Research Personnel; Resolution; Resources; Ribosomes; Roentgen Rays; Sampling; Signal Transduction; Source; Spain; Spiders; Structure; Students; Techniques; Test Result; Testing; United States National Institutes of Health; Work; Writing; abstracting; base; density; design; macromolecule; novel; particle; performance tests; prevent; programs; 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. Our focus has been on the implementation and testing of a novel classification algorithm which we published jointly with the group of Jose-Maria Carazo in Madrid in Nature Methods earlier this year (Scheres et al., 2007, previously listed as one of the highlights). We now have access to some supercomputer centers and explore how to efficiently implement the XMIPPS programs from Madrid, so they utilize massively parallel architecture. Our aim is to test the performance of the program both on phantom data and on experimental data routinely encountered in applications of the single-particle reconstruction technique. Dr. William Baxter has laid the groundwork for the creation of quantitatively satisfactory phantom data which are suitable for testing classification algorithms. A large heterogeneous dataset (~195,000 particles, from an initial set of ~1,000,000) obtained by Derek Taylor in Dr. Frank's group (eRF1 and eRF3 binding to the eukaryotic ribosome, in collaboration with Dr. Tatyana Pestova, SUNY Downstate Medical Center) was used to explore various classification strategies. We were successful in identifying classes corresponding to complexes in which the ribosome was bound to either or both of the factors, thus shedding light on the termination process in eukaryotes. These results will be written up and prepared for publication. 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 Dr. Jose-Maria Carazo group in Spain, 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, under Dr. Frank's mentorship, and RVBC-supported postdoc Tanvir Shaikh) were unsuccessful (details to be found in Dr. 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 now been published in Journal of Structural Biology (Baxter et al., 2008). The article features an estimation of the SNRs along with their spectral distributions (SSNRs). A phantom dataset was computed in a two-step process using the structural (i.e., pre-CTF) and post-CTF SNR values from our estimation, and deposited at the European Bioinformatics Institute (EBI) in Cambridge.

该副本是使用众多研究子项目之一 由NIH/NCRR资助的中心赠款提供的资源。子弹和 调查员（PI）可能已经从其他NIH来源获得了主要资金， 因此可以在其他清晰的条目中代表。列出的机构是 对于中心，这是调查员的机构。 抽象的： 该TRD解决了一个大分子的冷冻EM单粒子重建中至关重要的问题，在许多情况下，这是一种障碍，阻止了高分辨率的属性（大于10€）。这个问题是由于部分配体占用率和构象变异性，样品中分子的异质性。 我们的重点是我们今年早些时候与马德里的Jose-Maria Carazo共同发表的一种新型分类算法的实施和测试（Scheres等，2007，此前被列为亮点之一）。现在，我们可以访问一些超级计算机中心，并探索如何从马德里有效实施XMIPPS程序，因此它们利用大量并行体系结构。我们的目的是在幻影数据和单粒子重建技术应用中常规遇到的实验数据上测试该程序的性能。威廉·巴克斯特（William Baxter）博士为创建适合测试分类算法的定量满足幻影数据的基础奠定了基础。德里克·泰勒（Derek Taylor）在弗兰克（Frank）博士组中获得的一个大型异质数据集（〜195,000个颗粒，从最初的〜1,000,000集中）（ERF1和ERF3与真核生物核糖体结合，与Tatyana Pestova博士与Suny Downy Downstate Medical Center合作）用于探索各种分类策略。我们成功地识别了与核糖体绑定到任何一个或两个因素的复合物相对应的类，从而阐明了真核生物中终止过程的启示。这些结果将被写成并准备出版。 具体目标 1。（探索阶段）：探索单粒子项目的分类方法，这些项目可以完善现有的基于模板的方法，或探索未改变对象项目之间的一般内在数学关系。在项目的这个阶段，将设计诸如自组织（SOM）之类的算法，或者探索了现有算法。幻影数据集源自分子的现有密度图或X射线结构，这些结构呈现出不同的会议或配体结合状态。这样的地图系统地将其投影到各种方向上，由此产生的项目被低通滤波并被噪声污染。这些数据将允许确定哪种算法或哪种SOM配置将在不同的分辨率和信噪比的比率下表现最佳。 2。（测试阶段）：测试所得的算法和SOM在定义明确的实验性冷冻EM数据集中，来自沃兹沃思中心内外进行的单粒子项目。理想情况下，这些应该是以前出版物中表征的数据，以便可以轻松评估由于新的分类方法而进行的改进。 3。（传播阶段）：将软件与现有的蜘蛛软件集成并制定全面文档。以明确形式出版的基础概念还将允许其他软件包的作者（例如Eman（Ludtke等，2001）实施自己的版本，以进行更广泛的传播。 选择最大似然分类（ML3D）标准 我们与我们在TRD3的主要合作者Jose-Maria Carazo Group的合作，取得了显着的结果，这显然有助于在3DEM社区中普及了最大的类似方法。根据EF-G结合和核糖体会议中相关的“棘轮”变化，对90,000个核糖体图像进行了分类。在Scheres等人撰写的《自然方法论文》的合作出版之后。在2007年，该领域的几个EM组发生了大量应用。 由于这种方法的成功，我们已经停止追求“集群跟踪”方法（Fu等，J。结构生物学，2007年），因为努力在全球范围内扩展集群跟踪（在BMS学生Jie Fu的手中，根据Frank博士的心态，RVBC支持的rvbc-Supported tanvir shaikh）是因为Dife fule（jie ful diste ful）。将来可能需要更大的数据集来追求这一特定的发展。 我们的合作者之一Harry Zuzan博士正在研究Scheres最大样子方法的GPU（图形处理单元）。可能会预期高达100个加速度。 Zuzan博士正在作为私人努力，因为他现在被一家药房公司聘用。一旦他成功，他已承诺与我们共享该软件以及硬件规格。 幻影数据集的构建 为了实现分类方法或任何特定方法的参数设置的客观比较，我们着手基于有或没有EF-G结合的大肠杆菌核糖体构建幻影数据集。我们认为，这样的努力不仅可以为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，在产生幻影数据的所有尝试中，都忽略了重要的噪声来源，即结构噪声。如上一报告所述，我们进行了实验，以估计EM图像形成的各个步骤（包括结构噪声的SNR）的信噪比（SNR）。该估计的方法和结果现已发表在结构生物学杂志上（Baxter等，2008）。本文以SNR及其频谱分布（SSNR）的估计为估计。使用我们的估计中的结构（即CTF前）和CTF后SNR值在两步过程中计算幻影数据集，并沉积在剑桥的欧洲生物信息学研究所（EBI）。