RECONSTRUCTION FROM HETEROGENEOUS MOLECULE POPULATIONS

从异质分子群重建

• 批准号: 7357279
• 负责人: JOACHIM FRANK
• 金额: $ 3.37万
• 依托单位: WADSWORTH CENTER
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-02-01 至 2007-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7357279
• 关键词: RECONSTRUCTION; HETEROGENEOUS; MOLECULE; POPULATIONS

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. 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. We are at the start of the new TRD#3 project, in the part identified in the research plan as ¿exploration phase. Despite the shortness of the time period, we made first progress in three areas related to the aims of this TRD: 1) Dr. Bill Baxter, with the help of a student intern, developed a method of determining ¿good particles¿ that is based on an angular correlation signature. He showed that under certain circumstances, particle selection can be fully automated by reference to a spread diagram in which clustering can be observed. It is possible (and this will be further explored) that the method also lends itself to classification of heterogeneous populations of macromolecules. 2) Jie Fu, a graduate student working under Dr. Frank¿s mentorship, has started working on unsupervised classification by a method of tracking manifolds, an idea outlined in the Renewal application. Molecules are pre-classified by reference to an existing 3D template, yielding orientational classes. According to the theory, heterogeneity of a molecule population should be manifest in the appearance of clusters within each orientational class, and, most importantly, these clusters should form continuous manifolds across the angular range. First results were obtained both with a phantom data set and with an experimental data set, both for ribosome bound with a ligand. It was shown that in a case where heterogeneity is caused by presence/absence of a ligand in the mass range of the EF-G, separation of the populations can be achieved down to signal-to-noise ratios of 0.2. A paper summarizing these results is being written up. 3) Dr. Shaikh worked on the development of an automated particle selection and verification method based on correspondence analysis and classification. The method is now routinely used by members of Dr. Frank¿s group, and has been presented as part of the Cryo-EM Workshop at Scripps. Dr. Shaikh made the following presentation: ¿ Poster entitled ¿Particle-verification for single-particle reconstruction using correspondence analysis and classification¿ (T. Shaikh and J. Frank) at the Gordon Conference, New London, NH, June 14, 2005. Dr. Frank made the following presentation: Poster entitled ¿Unsupervised Classification Using Continuity of Classes in Hyperspace.¿ (J. Fu, H. Gao, and J. Frank) at the same Gordon Conference.

该子项目是利用NIH/NCRR资助的中心赠款提供的资源的许多研究子项目之一。子项目和研究者（PI）可能从另一个NIH来源获得主要资金，因此可以在其他CRISP条目中表示。所列机构为中心，不一定是研究者所在机构。该TRD解决了在大分子的冷冻EM单粒子重建中至关重要的问题，并且在许多情况下，该问题是阻止实现高分辨率（优于10 μ m）的单一障碍。这个问题是由于部分配体占据和构象可变性导致的样品中分子的异质性。我们将开发通用的方法从他们的cryo-EM预测，这将包括监督和无监督的分类方法的异质分子群体的分类。我们将与该领域的领先专家进行互动，并使用来自PI小组和其他追求单粒子重建的小组的典型数据。所产生的软件，如果成功，将提供给广大社区。 具体目标：1）（探索阶段）：探索改进现有基于模板的方法的单粒子投影分类方法，或利用未改变对象的投影之间的一般内在数学关系。在项目的这一阶段，将设计自组织（SOM）等算法，或探索现有算法的实用性。体模数据集来自现有的分子密度图或来自呈现不同构象或配体结合状态的X射线结构。这些地图被系统地投影到各个方向，所得到的投影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中，在定义良好的实验cryo-EM数据集上测试所得到的算法和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，这样就可以很容易地评估新分类方法带来的改进。 3)（传播阶段）：将该软件与现有天基信息平台软件相结合，并编写全面的文件。以明确的形式公布基本概念也将允许软件包的其他作者，例如EMAN（Ludtke等人，2001年）执行自己的版本，以便更广泛地传播。 我们正处于新的TRD#3项目的开始阶段，在研究计划中确定为探索阶段的部分。尽管时间很短，但我们在与本TRD目标相关的三个领域取得了第一次进展：1）比尔巴克斯特博士在一名实习生的帮助下，开发了一种基于角相关特征的确定好粒子的方法。他表明，在某些情况下，粒子选择可以通过参考可以观察到聚类的散布图来完全自动化。这是可能的（这将进一步探讨），该方法也适用于大分子的异质群体的分类。 2)在Frank博士指导下工作的研究生Jie Fu已经开始通过跟踪流形的方法进行无监督分类，这是Renewal应用程序中概述的想法。通过参考现有的3D模板对分子进行预分类，产生取向类。根据该理论，分子群体的异质性应该表现在每个取向类内的簇的外观中，并且最重要的是，这些簇应该在角范围内形成连续的流形。第一个结果得到了一个幻影数据集和实验数据集，都与配体结合的核糖体。结果表明，在EF-G的质量范围内存在/不存在配体导致异质性的情况下，可以实现低至0.2的信噪比的群体分离。一份总结这些结果的论文正在撰写中。 3)Shaanxi博士致力于开发一种基于对应分析和分类的自动颗粒选择和验证方法。该方法现在被Frank博士的小组成员常规使用，并已作为Scripps的Cryo-EM研讨会的一部分。 Shawei博士做了以下演讲：<$Poster题为<$Particle-verification for single-particle reconstruction using correspondence analysis and classification <$<$（T。2005年6月14日，在新罕布什尔州新伦敦举行的戈登会议上。Frank博士作了以下发言： 海报题为无监督分类使用超空间中的类的连续性。(J. Fu，H. Gao和J. Frank）在同一次戈登会议上。