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 年里，我们实施了一个自动化计划 分析晶体学数据 (mmtbx.xtriage)，我们开发了一个高度自动化的程序 异常子结构位置（HySS），我们创建了一个新的自动化结构细化 程序（phenix.refine）。通过开发开放的平台，我们在 PHENIX 的诞生中发挥了至关重要的作用 源 C++ 计算晶体学工具箱 (cctbx) 和 PHENIX 图形用户界面 (GUI)。 我们与 PHENIX 项目的其他成员密切合作，创建了一个集成系统 自动结构确定，提供命令行界面、可视化编程界面、 以及高度自动化的向导界面。 我们将利用强大的计算算法和软件基础来解决一些最棘手的问题 结构确定中仍存在的挑战性瓶颈；用于决策的实验数据分类， 从弱信号中定位子结构，以及高效且最小偏差的结构细化 在所有分辨率范围内。我们的新算法会自动为每个数据集分配概率分数 描述了成功的结构解决方案的可能性将用于后续决策 项目二和四。结果将提高结构解决方案的效率。的机会 成功的子结构定位将通过自动空间群的新算法得到改进 确定、最佳数据截止计算以及改进的似然评分函数的应用， 与 Project III 合作开发。这种独特的能力将使 PHENIX 能够 成功开始结构解决方案的过程，而其他系统目前失败。我们将开发新的 算法，包括使用正常模型，根据任意时间的实验数据改进模型 分辨率限制，通过使用自动模型参数化来保持合理的参数比率 到观察到的数据。我们还将扩展结构细化，以包括局部重建的算法 模型，包括肽翻转和旋转异构体改装，与项目 II 和 V 合作。这种方法将 加速细化的收敛，并在许多情况下减少人工干预的需要。最后， 我们将通过进一步开发我们的 cctbx 库、PHENIX GUI、项目来增强 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的结构 对于理解生物学并最终改善人类健康非常重要的大分子。