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，旨在开发计算技术，该技术将改进相对粒子排列的估计，从而提高由给定数量的粒子图像生成的 3D 重建的质量。目前结构分析仅限于大约 10 埃的分辨率。我们的目标是通过设计新的大规模技术，提升现有能力，从单粒子固有的噪声图像中提取近原子分辨率的 3D 结构。 用于确定和细化方向参数的并行算法。我们在初步结果中表明，使用当前的细化方法无法保证获得最佳 3D 结构。此外，我们证明，当用于细化的参考结构被系统地扭曲时，现有的细化过程可能会陷入局部最小值。在该提案的框架内，我们将开发探索所有可能解决方案（所有解决方案）空间的计算方法。方向参数）来自给定的一组单粒子图像。为了实现这一目标，我们将确定当前使用的算法的局限性，特别关注方向参数的确定和细化以及在给出最佳 3D 结构估计的情况下生成最佳 3D 结构。 方向参数。新的细化和优化算法将根据组合优化来制定。具体来说，从头开始的结构确定将通过启发式增强并行遗传算法来实现。将使用局部搜索方法改进候选解决方案，其中同时校正 3D 密度图和方向参数。结果将借助新的统计测试进行评估，该测试将：测量 3D 重建相对于图像质量的质量。该软件将以确保完全可移植性的方式进行开发，并且将 移植到 SPARX 和 SPIDER 系统中。