Project 4: Integrating Model Validation and Improvement with the Structure

项目 4：将模型验证和改进与结构相结合

• 批准号: 8227544
• 负责人: JANE Shelby RICHARDSON
• 金额: $ 18.37万
• 依托单位: UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-07-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8227544
• 关键词: Accounting; Address; Archives; Automation; Biological; Biological Process; Biomedical Research; Cells; Communities; Complex; Computing Methodologies; Crystallography; Data; Diagnosis; Ensure; Goals; Libraries; Maps; Medical; Methodology; Methods; Modeling; Molecular Conformation; Molecular Machines; Nucleic Acids; Phase; Procedures; Process; Protocols documentation; Research Infrastructure; Resolution; Sampling; Solutions; Structure; System; Systems Biology; Universities; Validation; Work; abstracting; electron density; improved; innovation; insight; member; protein complex; software systems; structural biology; tool; user-friendly

Project Summary/Abstract Phenix has already achieved an extremely effective, highly automated, and user-friendly system that addresses the entire structure-solution process for macromolecular crystallography. While work will continue on further improvements to the infrastructure, completeness, and integrated tracking, the primary emphasis going forward will be enabling routine, successful, and accurate solution of difficult structures: low resolution (worse than 3A), cases with weak phase information, and large complexes. At low resolution where fit of the model to the data can only be approximate, it is always necessary to make use of outside information, and the Phenix teams propose to augment that process in several innovative ways. The overall goal of Project IV is to provide dynamic structure validation throughout the Phenix crystallographic software system, to automate improved structure solution and accuracy at all resolutions. Difficult structures will be enabled by developing resolution-tuned libraries and strategies, implementing context-dependent parameters, and diagnosing and avoiding systematic distortions both of electron density maps and of the models built from them. Automated methodologies will be developed for exploiting various types of additional information, primarily explicit all-atom contact analysis, more thorough and guided local conformational sampling directly in refinement, and model-building help from the complementary methodology of computational predictions (collaboratively with members of the Rosetta developer community). In particular, crystallography at low resolution would benefit greatly from reliable predictions capable of working piecewise on subsections of a large structure. Accuracy will be improved across the full range of resolutions by more correct treatment of alternate conformations at high resolution, by integrated tracking and presentation of local validation criteria, automation of many types of corrections, and at low resolution by combining all analyses to achieve an internally consistent, relaxed, and well packed model that stays consistent with the experimental data. These procedures and their automated use in the Phenix protocols should ensure outstanding accuracy for the resulting structures. Relevance: Much of the current excitement within structural biology, and in the related fields such as cell, evolutionary, biomedical, and systems biology that make use of that structural information, is focused on low resolution structures of large "molecular machines" that perform complex and highly regulated biological functions. The improvements in accuracy that we hope to achieve for these difficult structures will enable the entire biomedical research community to gain rich biological and medical insights from large, low-resolution structures more often.

项目摘要/摘要 菲尼克斯已经实现了一个极其有效、高度自动化和用户友好的系统，解决了大分子结晶学的整个结构-溶液过程。虽然将继续在基础设施、完整性和综合跟踪方面进一步改进工作，但未来的主要重点将是实现对困难结构的常规、成功和准确的解决方案：低分辨率(低于3A)、相位信息较弱的情况和大型综合体。在低分辨率下，模型与数据的拟合只能是近似的，总是需要利用外部信息，菲尼克斯团队建议通过几种创新的方式来增强这一过程。 项目IV的总体目标是在整个菲尼克斯晶体学软件系统中提供动态结构验证，使改进的结构解决方案和在所有分辨率下的准确性实现自动化。 通过开发分辨率调整的库和策略，实施与上下文相关的参数，诊断和避免电子密度图及其建立的模型的系统性扭曲，将使困难的结构成为可能。将开发自动化方法来利用各种类型的额外信息，主要是显式的全原子接触分析，在改进过程中直接进行更全面和指导的局部构象采样，以及从计算预测的补充方法中帮助建立模型(与Rosetta开发人员社区的成员合作)。特别是，低分辨率的结晶学将极大地受益于能够对大型结构的子部分进行分段工作的可靠预测。通过在高分辨率下更正确地处理交替构象，通过集成跟踪和显示局部 验证标准、多种类型修正的自动化以及在低分辨率下组合所有分析以实现与实验数据保持一致的内部一致、宽松且包装良好的模型。这些程序及其在菲尼克斯协议中的自动使用应可确保所得到的结构具有出众的准确性。 相关性：目前结构生物学以及利用这些结构信息的相关领域，如细胞、进化、生物医学和系统生物学中的许多令人兴奋的东西，都集中在执行复杂和高度调控的生物功能的大型“分子机器”的低分辨率结构上。我们希望为这些困难的结构实现的精确度的提高，将使整个生物医学研究界能够更频繁地从大型、低分辨率结构中获得丰富的生物学和医学见解。