GLOBAL OPTIMIZATION OF REFINEMENT PARAMETERS

细化参数的全局优化

• 批准号: 6757130
• 负责人: PAWEL A. PENCZEK
• 金额: $ 19.14万
• 依托单位: UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-06-01 至 2008-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6757130
• 关键词: bioimaging /biomedical imaging; biotechnology; computer program /software; computer system design /evaluation; cryoelectron microscopy; high throughput technology; image processing; macromolecule; mathematical model; model design /development; nanotechnology; parallel processing; three dimensional imaging /topography

Within this Program Project, this proposal describes a research plan that seeks to develop computational technology that will give improved estimates of the relative particle alignments and thereby improve the quality of 3-D reconstructions that are produced by a given number of particle images. Currently structural analysis is limited to approximately 10 Angstroms resolution. Our goal is to advance the existing capabilities to extract near atomic resolution 3D structures from the inherently noisy images of single particles by designing new massively parallel algorithms for determination and refinement of orientation parameters. We show in preliminary results that attainment of an optimal 3D structure cannot be guaranteed when current refinement methods are used. Moreover, we demonstrate that when the reference structure used for refinement is systematically distorted the existing refinement procedure can be trapped in a local minimum.Within the framework of this proposal, we will develop computational methods that explore the space of all possible Solutions (of all orientation parameters) from a given set of single particle images. To achieve this goal, we will determine the limitations of currently used algorithms with particular attention to the determination and refinement of the orientation parameters and the generation of the optimal 3D structure given the best estimate of the orientation parameters. The new refinement and optimization algorithms will be formulated in terms of combinatorial optimization. Specifically, the ab initio structure determination will be achieved with heuristic-augmented parallel genetic algorithm. The candidate solutions will be improved using a local search method in which the 3D density map and the orientation parameters are corrected simultaneously. The results will be evaluated with the help of new statistical test that will :measure the quality of 3D reconstruction with respect to the quality of images.The software will be developed in ways that assure full portability and it will be ported within the SPARX and SPIDER systems.

在该计划项目中，该提案描述了一项研究计划，该计划旨在开发计算技术，该计划将改进对相对粒子比对的估计，从而提高由给定数量的粒子图像产生的3-D重建质量。目前的结构分析仅限于大约10个埃埃斯特罗姆分辨率。我们的目标是通过大规模设计新的粒子的固有嘈杂图像，从而提高现有的能力，以从固有的嘈杂图像中提取附近的原子分辨率3D结构 用于确定和完善方向参数的并行算法。我们在初步结果中显示，当使用当前的细化方法时，无法确保达到最佳3D结构。此外，我们证明，当系统地扭曲用于完善的参考结构时，现有的细化过程可能会被困在本地最小值中。在该提案的框架中，我们将开发从给定的单个粒子图像集中探索所有可能解决方案（所有方向参数）的所有可能解决方案的空间（所有方向参数）。为了实现这一目标，我们将尤其关注定向参数的确定和完善目前使用的算法的局限 方向参数。新的改进和优化算法将根据组合优化制定。具体而言，通过启发式增强的平行遗传算法将实现AB的结构确定。将使用局部搜索方法改进候选解决方案，其中3D密度图和方向参数同时纠正。将在新的统计测试的帮助下评估结果：测量图像质量的3D重建质量。软件将以确保完全可移植性的方式开发，并且将是 移植在Sparx和Spider系统中。