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，这项建议描述了一项研究计划，该计划寻求开发计算技术，该技术将改进对相对粒子排列的估计，从而提高由给定数量的粒子图像产生的3D重建的质量。目前，结构分析仅限于约10埃分辨率。我们的目标是提高现有的能力，从单个粒子固有的噪声图像中提取近原子分辨率的3D结构，方法是设计新的大规模 确定和改进取向参数的并行算法。我们的初步结果表明，当使用当前的精化方法时，不能保证获得最佳的3D结构。此外，我们证明了当用于精化的参考结构被系统扭曲时，现有的精化过程可能陷入局部极小。在该提议的框架内，我们将开发从给定的单粒子图像集合中探索所有可能解(所有取向参数)的计算方法。为了实现这一目标，我们将确定当前使用的算法的局限性，特别关注定向参数的确定和改进，以及在给定最佳估计的情况下生成最佳3D结构 方向参数。新的精化和优化算法将从组合优化的角度进行阐述。具体地说，从头算结构的确定将使用启发式增广并行遗传算法来实现。采用局部搜索的方法对候选解进行改进，同时修正三维密度图和方向参数。结果将在新的统计测试的帮助下进行评估，该测试将：根据图像质量衡量3D重建的质量。软件将以确保完全可移植的方式开发，并将 移植到SPARX和Spider系统中。