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.

项目总结/摘要 Phenix已经实现了一个非常有效，高度自动化和用户友好的系统，解决了大分子晶体学的整个结构解决方案过程。虽然工作将继续进一步改善基础设施，完整性和综合跟踪，但未来的主要重点将是实现困难结构的常规，成功和准确的解决方案：低分辨率（低于3A），相位信息弱的情况，以及大型复合体。在低分辨率下，模型与数据的拟合只能是近似的，因此总是需要利用外部信息，Phenix团队建议以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Phenix晶体学软件系统中提供动态结构验证，以在所有分辨率下自动改进结构解决方案和准确性。 困难的结构将通过开发分辨率调整库和策略，实施上下文相关参数，诊断和避免电子密度图和从它们构建的模型的系统性失真来实现。将开发自动化方法来利用各种类型的额外信息，主要是明确的全原子接触分析，更彻底和指导的局部构象采样直接在细化，和模型建立的帮助，从计算预测的补充方法（与罗塞塔开发人员社区的成员合作）。特别是，低分辨率的晶体学将大大受益于能够在大型结构的子部分上分段工作的可靠预测。通过在高分辨率下更正确地处理替代构象，通过对局部构象的综合跟踪和呈现， 验证标准，自动化的许多类型的校正，并在低分辨率下，通过结合所有的分析，以实现内部一致的，宽松的，以及包装良好的模型，保持与实验数据一致。这些程序及其在Phenix方案中的自动化使用应确保所得结构的出色准确性。 相关性：目前结构生物学和相关领域（如细胞、进化、生物医学和系统生物学）中的许多兴奋点都集中在执行复杂和高度调节的生物功能的大型“分子机器”的低分辨率结构上。我们希望在这些困难的结构中实现的准确性的提高将使整个生物医学研究界能够更经常地从大型低分辨率结构中获得丰富的生物学和医学见解。