RECONSTRUCTION FROM HETEROGENEOUS MOLECULE POPULATIONS

点击翻译按钮获取中文摘要

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.