RECONSTRUCTION FROM HETEROGENEOUS MOLECULE POPULATIONS

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 摘要： 这种TRD解决了在低温EM单粒子重建大分子中至关重要的一个问题，即在许多情况下，阻碍达到高分辨率(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化导致的样品中分子的异质性。我们将开发根据低温EM投影对异质分子群体进行分类的一般方法，其中将包括监督和非监督分类方法。我们将与这一领域的领先专家互动，并使用来自PI小组和其他寻求单粒子重建的小组的典型数据。如果成功，所产生的软件将被广泛的社区使用。 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，或利用不变对象的投影之间的一般内在数学关系。在该项目的这一阶段，将设计自组织(SOM)等算法，或探索现有算法的实用性。幻影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。这样的地图被系统地投影到各种方向，结果投影经过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配置在不同的分辨率和信噪比下执行得最好。 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低温电磁实验数据集上测试所产生的算法和SOM。理想情况下，这些数据应该是以前出版物中已经描述的数据，这样就可以很容易地评估新的分类方法带来的改进。 3)(传播阶段)：将该软件与现有的SPIDER软件相结合，并编制全面的文件。以明确的形式出版基本概念也将使诸如Eman(Ludtke等人，2001年)等软件包的其他作者能够实施他们自己的版本，以便更广泛地传播。 在上一个报告所述期间，完成了以下工作： 1)比尔·巴克斯特博士在一名实习生的帮助下，开发了一种基于角度相关信号来确定“好粒子”的方法。他指出，在某些情况下，粒子选择可以通过参照可以观察到聚集的扩散图来完全自动化。这是可能的(这将被进一步探索)，该方法也适合于对大分子的异质群体进行分类。 2)傅杰是一名研究生，在弗兰克博士的指导下工作，他已经开始用一种跟踪流形的方法进行无监督分类，这一想法在Renewal应用程序中概述了一下。通过参考现有的3D模板对分子进行预分类，从而产生取向类别。根据该理论，分子群体的异质性应该表现在每个取向类中的团簇的出现上，最重要的是，这些团簇应该在角度范围内形成连续的流形。第一个结果是用模体数据集和实验数据集获得的，这两个数据集都是关于核糖体与配体结合的。结果表明，在EF-G的质量范围内由于配基的存在/不存在而引起异质性的情况下，群体的分离可以达到0.2的信噪比。一篇总结这些结果的论文正在撰写中。 3)谢赫博士致力于开发一种基于对应分析和分类的自动粒子选择和验证方法。这种方法现在被弗兰克博士团队的成员常规使用，并作为斯克里普斯的冷冻-EM研讨会的一部分进行了介绍。 谢赫博士作了以下介绍： 2005年6月14日，在新伦敦市的戈登会议上，张贴题为“基于对应分析和分类的单粒子重建的粒子验证”(T.Shaikh和J.Frank)的海报。 弗兰克博士作了以下介绍： 海报标题为《利用超空间中类的连续性进行无监督分类》。(傅园慧、高晓松和J.Frank)在同一个Gordon Conference上。

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. In the previous reporting period, the following were accomplished: 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.