IMP: Software for Hybrid Determination of Macromolecular Assembly Structures

IMP：大分子组装结构混合测定软件

• 批准号: 10477207
• 负责人: ANDREJ SALI
• 金额: $ 34.24万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-04-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10477207
• 关键词: 26S proteasome; Address; Algorithms; Archives; Benchmarking; Biological; CCL21 gene; Cells; Chemicals; Chimera organism; Collaborations; Communities; Complex; Computer software; Computing Methodologies; Cysteine; Data; Data Sources; Databases; Deposition; Deuterium; Development; Disease; Documentation; Educational workshop; Electron Microscopy; Fluorescence Resonance Energy Transfer; Foundations; Funding; Goals; Hybrids; Hydrogen; Knowledge; Letters; Licensing; Literature; Maps; Mass Spectrum Analysis; Membrane Transport Proteins; Methods; Modeling; Molecular Conformation; Molecular Structure; Nuclear Pore Complex; PrP; Productivity; Proteins; Proteomics; Protocols documentation; Research Project Grants; Resolution; Resources; Roentgen Rays; Sampling; Scheme; Shapes; Structural Models; Structure; System; Testing; Trees; Uncertainty; Variant; Vertebral column; Work; X-Ray Crystallography; base; biological systems; computer program; crosslink; data repository; density; design; flexibility; improved; insight; kinematics; macromolecular assembly; mutant; open source; pathogen; programs; protein data bank; repository; restraint; spindle pole body; tool

Project Summary The broad goal is to develop and apply computational methods for building structural models of proteins and their assemblies. One successful approach, integrative structure modeling, casts the building of such models as a computational optimization problem where knowledge about the assembly is encoded into the scoring function used to evaluate candidate models. We propose to extend and enhance the Integrative Modeling Platform (IMP) program that provides programmatic support for developing and distributing integrative structure modeling pro- tocols. IMP already allows representing molecules at multiple resolutions, using spatial restraints from many types of data, and searching for solutions by a variety of sampling algorithms. So far, it has been applied mostly to data from electron microscopy (EM), mass spectrometry, small angle X-ray scattering, Förster resonance energy transfer, crosslinking, hydrogen deuterium exchange (HDX), and various proteomics methods. IMP is easily extensible to add support for new data sources and algorithms, and is distributed under an open source license. Here, we propose to extend IMP to address a greater range of biological problems and make it more generally useful to the scientific community. Specifically, in Aim 1, we will develop integrative threading for computing an atomic model based on a density map determined at medium resolution (4-8 Å) by EM or X- ray crystallography. This goal will be achieved by simultaneously sampling both threading and conformation based on the density map as well as other data, such as chemical cross-links and HDX protection factors. This method is significant because it will produce atomic resolution models from medium-resolution maps determined by either EM or X-ray crystallography. In Aim 2, we will develop a Bayesian integrative method for modeling ensembles of similar systems. Data from different samples, ensembles, and/or variants are often pooled together to model a single representative structure. This synthesis is problematic when the variation between the actual structures across the samples, ensembles, and variants is larger than the uncertainty of the data. We will address the challenge by developing a general and flexible scheme for representing and scoring related structural en- sembles. This method is significant because it will improve the accuracy of the model and the estimate of its uncertainty. In Aim 3, we will maximize the impact of IMP on the community, by delivering a well-tested and maintained software package that is documented with mailing lists, examples, and demonstrations at local and external workshops, by hosting select users at UCSF, and by pursuing closer integration with other software packages and community resources, including databases such as the Protein Data Bank (PDB), structure view- ers such as Chimera and VMD, and other modeling programs such as NAMD and ReaDDy. The proposed aims are informed by and will shape the nascent worldwide PDB effort on representing, validating, archiving, and disseminating integrative structure models.

项目摘要 广泛的目标是开发和应用计算方法来建立蛋白质的结构模型， 他们的集会。一个成功的方法，集成结构建模，铸造这样的模型的建设， 一个计算优化问题，其中关于组件的知识被编码到评分函数中 用于评估候选模型。我们建议扩展和增强集成建模平台（IMP） 该程序为开发和分发集成结构建模程序提供程序支持， 去吧。IMP已经允许在多个分辨率下表示分子，使用来自多个分辨率的空间限制。 类型的数据，并通过各种采样算法搜索解决方案。到目前为止，它已被应用于大多数 电子显微镜（EM）、质谱、小角X射线散射、Förster共振 能量转移、交联、氢氘交换（HDX）和各种蛋白质组学方法。IMP是 易于扩展，以添加对新数据源和算法的支持，并在开放源代码下分发。 执照在这里，我们建议扩展IMP以解决更大范围的生物学问题，并使其 对科学界更有帮助。具体来说，在目标1中，我们将开发集成线程 用于基于通过EM或X射线以中等分辨率（4-8 μ m）确定的密度图来计算原子模型， 射线晶体学这一目标将通过同时对穿线和构造进行采样来实现 基于密度图以及其他数据，例如化学交联和HDX保护因子。这 方法是重要的，因为它将产生原子分辨率模型从中等分辨率的地图确定 通过EM或X射线晶体学。在目标2中，我们将开发一种贝叶斯综合建模方法 类似系统的集合。来自不同样本、集合和/或变体的数据通常被汇集在一起 来模拟一个单一的代表性结构。这种合成是有问题的， 样本、集合和变体之间的结构的不确定性大于数据的不确定性。我们将解决 通过开发一个通用的和灵活的方案来表示和评分相关的结构化en， 合奏。该方法具有重要意义，因为它将提高模型的精度， 不确定性在目标3中，我们会提供一套行之有效， 维护的软件包，在本地和 外部研讨会，通过在加州大学旧金山分校主办精选用户，并通过追求与其他软件更紧密的集成 软件包和社区资源，包括数据库，如蛋白质数据库（PDB），结构视图- 例如Chimera和VMD，以及其他建模程序，例如NAMD和ReaDDy。拟议目标 通过全球PDB在表示、验证、归档和 推广一体化结构模式。