PROJECT 1 - LAWRENCE BERKELEY LAB - PHENIX

项目 1 - 劳伦斯伯克利实验室 - 凤凰城

• 批准号: 7208309
• 负责人: PAUL Damien ADAMS
• 金额: $ 70.88万
• 依托单位: UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7208309
• 关键词: X ray crystallography; bioengineering /biomedical engineering; biomedical automation; computer human interaction; computer program /software; data management; functional /structural genomics; macromolecule; mathematical model; model design /development; structural biology

Project I: During the first 5 years of the PHENIX project we have implemented a program for automated analysis of crystallographic data (mmtbx.xtriage), we have developed a highly automated program for anomalous substructure location (HySS), and we have created a new automated structure refinement program (phenix.refine). We have played an essential role in the birth of PHENIX by developing the open source C++ Computational Crystallography Toolbox (cctbx) and the PHENIX graphical user interface (GUI). We have worked closely with the other members of the PHENIX project to create an integrated system for automated structure determination that provides command-line interfaces, a visual programming interface, and the highly automated Wizard interface. We will use our strong foundation of computational algorithms and software to address some of the most challenging bottlenecks remaining in structure determination; classification of experimental data for decisionmaking, substructure location from weak signals, and productive and minimally-biased structure refinement at all resolution ranges. Our new algorithms which automatically assign a probability score to each dataset that describes the likelihood of successful structure solution will be used in subsequent decision-making by Projects II and IV. The result will be an increase in the efficiency of structure solution. The chances of successful substructure location will be improved by new algorithms for automated space group determination, optimal data cutoff calculation, and the application of improved likelihood scoring functions, developed in collaboration with Project III. This unique capability will make it possible for PHENIX to successfully-begin the process of structure solution, where other systems currently fail. We will develop new algorithms, including the use of normal models, for the refinement of models against experimental data at any resolution limit, by using automatic model parameterizations that maintain a reasonable ratio of parameters to observed data. We will also extend structure refinement to include algorithms for the local rebuilding of models, including peptide flips and rotamer refitting, in collaboration with Projects II and V. This approach will speed the convergence of refinement and reduce the need for manual intervention in many cases. Finally, we will enhance the PHENIX software by further development of our cctbx library, the PHENIX GUI, Project Data Storage, and the developer environment. Our new algorithms will enable researchers to determine the structures of challenging, high-value biological macromolecules which are important for understanding biology and ultimately improving human health.

项目I：在Phenix项目的前5年中，我们实施了一个用于自动化的程序 分析晶体学数据（mmtbx.xtriage），我们已经开发了一个高度自动化的程序 异常的子结构位置（HYSS），我们创建了一个新的自动化结构改进 程序（phenix.refine）。我们通过开放露天在苯克斯的诞生中发挥了重要作用 源C ++计算晶体学工具箱（CCTBX）和PHENIX图形用户界面（GUI）。 我们已经与Phenix项目的其他成员紧密合作，以创建一个集成系统 提供命令行接口的自动结构确定，视觉编程接口， 以及高度自动化的向导接口。 我们将利用我们强大的计算算法和软件基础来解决一些最大的问题 在结构确定中剩下的挑战瓶颈；实验数据进行决策的分类， 来自弱信号的子结构位置，以及生产性和最小偏置结构的完善 所有分辨率范围。我们的新算法会自动为每个数据集分配概率分数 描述成功结构解决方案的可能性将用于随后的决策 项目II和IV。结果将是结构解决方案的效率提高。机会 自动空间组的新算法将改善成功的子结构位置 确定，最佳数据截止计算以及提高的可能性评分功能的应用， 与项目III合作开发。这种独特的能力将使Phenix成为可能 成功地展示了当前其他系统失败的结构解决方案的过程。我们将开发新的 算法，包括使用普通模型，以针对任何实验数据进行改进 分辨率限制，使用自动模型参数化，以维持参数的合理比率 观察到数据。我们还将扩展结构改进，以包括用于本地重建的算法 与项目II和V合作的模型，包括肽翻转和Rotamer Reftitting。 在许多情况下，加快了精炼的收敛并减少对手动干预的需求。最后， 我们将通过进一步开发CCTBX库，Phenix GUI，Project来增强Phenix软件 数据存储和开发人员环境。 我们的新算法将使研究人员能够确定具有挑战性的高价值生物学的结构 大分子对于理解生物学并最终改善人类健康很重要。