RECONSTRUCTION FROM HETEROGENEOUS MOLECULE POPULATIONS

本子项目是利用由NIH/NCRR资助的中心赠款提供的资源的众多研究子项目之一。子项目和研究者（PI）可能已经从另一个NIH来源获得了主要资金，因此可以在其他CRISP条目中表示。列出的机构是中心的，不一定是研究者的机构。该TRD解决了在低温电镜大分子单粒子重建中最重要的问题，在许多情况下，这是阻碍获得高分辨率（优于10¿）的单一障碍。这个问题是由于部分配体占用和构象变异性导致样品中分子的不均匀性。我们将从异质分子群体的冷冻电镜预测中开发一般的分类方法，包括监督和非监督分类方法。我们将与该领域的领先专家进行互动，并使用来自PI¿s小组和其他追求单粒子重建的小组的典型数据。由此产生的软件，如果成功，将提供给广泛的社区。具体目标：1)（探索阶段）：探索单粒子投影的分类方法，这些方法可以改进现有的基于模板的方法，或者利用不变物体投影之间的一般内在数学关系。在项目的这个阶段，将设计自组织（SOMs）等算法，或探索现有算法的效用。幻影数据集来源于现有的分子密度图或x射线结构，这些x射线结构呈现不同的构象或配体结合状态。这样的地图被系统地投影到各个方向，最终的投影是低通滤波的，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Wadsworth中心内外进行的单粒子项目中明确定义的实验低温电镜数据集上测试所得算法和SOMs。理想情况下，这些数据应该是在以前的出版物中描述过的，这样就可以很容易地评估新分类方法所带来的改进。3)（发布阶段）：将软件与已有的SPIDER软件进行集成，并开发全面的文档。以明确的形式发布基本概念也将允许其他软件包的作者，如EMAN （Ludtke et al., 2001）实现他们自己的版本，以便更广泛地传播。我们正处于新的TRD#3项目的开始，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TRD目标相关的三个领域取得了初步进展：1)Bill Baxter博士在一名学生实习生的帮助下，开发了一种基于角相关特征确定“好粒子”的方法。他表明，在某些情况下，粒子选择可以完全自动化，参考一个可以观察到聚类的扩散图。这是可能的（这将进一步探索），该方法也适用于大分子异质群体的分类。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研究无监督分类，这是renew申请中概述的一个想法。分子通过参考现有的3D模板进行预分类，产生定向类。根据该理论，分子群体的异质性应该在每个取向类的簇中表现出来，最重要的是，这些簇应该在整个角度范围内形成连续的流形。第一个结果是通过幻影数据集和实验数据集获得的，两者都是与配体结合的核糖体。结果表明，在异质性是由EF-G质量范围内配体的存在或不存在引起的情况下，种群的分离可以实现到0.2的信噪比。正在撰写一篇总结这些结果的论文。3) Shaikh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。该方法现在被Frank博士的团队成员常规使用，并已作为Scripps的Cryo-EM研讨会的一部分提出。Shaikh博士在2005年6月14日的Gordon会议上做了以下演讲：题为“使用对应分析和分类进行单粒子重建的粒子验证”的海报（T. Shaikh和J. Frank）。Dr. Frank做了下面的演讲：海报题目是：在超空间中使用类的连续性进行无监督分类。（J. Fu， H. Gao和J. Frank）在同一个戈登会议上。

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. 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.