IMP: Software for Hybrid Determination of Macromolecular Assembly Structures

描述(由申请人提供)：广泛的目标是开发和应用计算方法，以建立蛋白质及其组装的结构和动力学的数据衍生模型。这些模型可以深入了解程序集如何工作、它们如何演变、如何控制它们以及如何设计类似的功能。一种成功的方法，一体化结构建模，将这种模型的建立转化为计算优化问题，其中关于组件的所有知识被编码到用于评估候选模型的评分函数中。本文提出对开源集成建模平台(IMP；http://integrativemodeling.org/))进行扩展和增强，为开发和分发集成结构建模协议提供编程支持。IMP允许以各种分辨率表示分子，使用基于多种类型数据的评分函数，并通过各种采样算法搜索解决方案。此外，IMP很容易扩展以添加对新数据源和算法的支持，并且是在开放源码许可下分发的，自2010年3月以来已有300多个独特的下载。到目前为止，它主要应用于电子显微镜、小角X射线散射和各种蛋白质组学方法的数据。一揽子计划将得到扩展，以便能够解决更广泛的生物学问题，并使其更普遍地适用于科学界。具体地说，IMP使用的传统评分功能将得到基于推理的评分功能的补充，这些功能可以从数据中提取尽可能多的信息。这些函数的制定将遵循贝叶斯方法，使用最少的假设和近似，以考虑数据中的错误和不完整以及不同种类的样本。通过将整个自由度集有效地划分为可能重叠的子集，通过传统的优化器或枚举法独立地找到子集的最优解和次优解，然后组合相容的解以获得整个系统的保证的最佳计分解的方法，将改进计分函数景观的采样。IMP也将得到扩展，以最大限度地利用质谱学提供的丰富信息。为了最大限度地发挥IMP及其对社区的效用，它将与其他程序包连接，包括结构查看器，如Chimera，结构预测和设计程序，如Rosetta，以及门户网站，如蛋白质模型门户。最后，该软件将经过良好的测试和记录，并将通过邮件列表、范例、研讨会上的演示以及在加州大学旧金山分校接待精选用户来支持不断增长的IMP社区。 公共卫生相关性：项目叙述我们建议扩展IMP，这是一个计算机程序，可以描述大型高分子机器的三维形状，而不是通过单一的实验技术来解决这些问题。这些结构将使我们能够更好地了解细胞在正常和疾病条件下的工作方式。

DESCRIPTION (provided by applicant): The broad goal is to develop and apply computational methods for building data-derived models of the structure and dynamics of proteins and their assemblies. These models can give insights into how the assemblies work, how they evolved, how they can be controlled, and how similar functionality can be designed. One successful approach, integrative structure modeling, casts the building of such models as a computational optimization problem where all knowledge about the assembly is encoded into the scoring function used to evaluate candidate models. It is proposed here to extend and enhance the open source Integrative Modeling Platform (IMP; http://integrativemodeling.org/) that provides programmatic support for developing and distributing integrative structure modeling protocols. IMP allows representation of molecules at a variety of resolutions, use of scoring functions based on many types of data, and searches for solutions by a variety of sampling algorithms. In addition, IMP is easily extensible to add support for new data sources and algorithms, and is distributed under an open source license, with more than 300 unique downloads since March 2010. So far, it has been applied mostly to data from electron microscopy, small angle X-ray scattering, and various proteomics methods. The package will be extended to allow addressing a greater range of biological problems and to make it more generally useful to the scientific community. Specifically, the traditional scoring functions used by IMP will be supplemented with inference-based scoring functions that extract the maximum possible information from the data. The formulation of these functions will follow a Bayesian approach with minimal assumptions and approximations, to account for errors and incompleteness in the data as well as a heterogeneous sample. Sampling of the scoring function landscape will be improved by a method that efficiently divides the complete set of degrees of freedom into potentially overlapping subsets, finds optimal and suboptimal solutions for the subsets independently by traditional optimizers or enumeration, and then combines compatible solutions to obtain guaranteed best-scoring solutions for the whole system. IMP will also be extended to make best use of the wealth of information provided by mass spectrometry. To maximize the impact of IMP and its utility to the community, it will be interfaced with other packages, including structure viewers such as Chimera, structure prediction and design programs such as Rosetta, and web portals such as the Protein Model Portal. Finally, the software will be well-tested and documented, and the growing IMP community will be supported with mailing lists, examples, demonstrations at workshops, and hosting of select users at UCSF. PUBLIC HEALTH RELEVANCE: Project Narrative We propose to extend IMP, a computer program that can describe the three-dimensional shapes of large macromolecular machines that are not amenable to solution with a single experimental technique. These structures will allow us to better understand the workings of the cell, both under normal and disease conditions.