Collaborative Research: ABI Innovation: Algorithms And Tools For Modeling Macromolecular Assemblies

合作研究：ABI 创新：大分子组装建模的算法和工具

• 批准号: 1356306
• 负责人: Matthew Baker
• 金额: $ 28.83万
• 依托单位: Baylor College of Medicine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1356306&HistoricalAwards=false
• 关键词: Collaborative; Research; ABI; Innovation; Algorithms

This research seeks to develop novel methods and software tools for mining structures of large molecular assemblies from imaging data. Macromolecular assemblies, such as ribosomes and viruses, are responsible for driving nearly all cellular events. How these assemblies function, in turn, is closely related with their 3Dstructures, which are analogous to interlocking puzzles consisting of tens to hundreds of proteins, each having its own unique shape. The ability to model the structure of individual proteins as well as their architecture in an assembly is therefore critically important for understanding how the cell, and more broadly the biological system, function. While state-of-art imaging methods have been developed to capture macromolecular assemblies as 3D density volumes, such as X-ray crystallography and electron cryo-microscopy, creating structural models from such imagery remains a time-consuming and highly manual process in part due to the limited resolution of the data. The goal of the project is to streamline the image-to-structure pipeline by designing novel computational algorithms and developing a comprehensive modeling platform. The algorithms seek to leverage the advance in computer graphics and vision while combining image data, sequence data, and expert knowledge to improve the efficiency and accuracy of common modeling tasks. The modeling platform will integrate the investigator's methods with third-party modeling packages to provide an easy-to-use one-stop-shop for creating and validating structures of macromolecular assemblies all the way from raw images and individual protein sequences. The platform will be built upon the existing Gorgon software (http://gorgon.wustl.edu) and distributed together with the popular EMAN2 software for image analysis of density maps. The outcome of the project will have a direct impact on reducing the time and effort that biologists spend on translating experimental results to knowledge, discoveries, and treatments.More specifically, the project will focus on algorithmic development on three modeling tasks that currently either rely on manual labor or are computationally expensive. These problems include detecting secondary structure elements (e.g., alpha-helices and beta-sheets) at various non-atomic resolutions, tracing protein backbones in the density volume, and flexibly fitting probe structures into the volume. The algorithms will build upon successful techniques from computer graphics and vision, including mesh deformation using differential coordinates and spectral feature matching. To transform Gorgon into a modeling "hub", the software architecture and interface of Gorgon will be redesigned in this project to improve inter-operability, scalability, and usability. Plug-ins will also be developed for third-party tools that provide complementary modeling capability such as comparative modeling and protein folding.

本研究旨在开发新的方法和软件工具，从成像数据中挖掘大分子组装体的结构。大分子组装体，如核糖体和病毒，负责驱动几乎所有的细胞事件。反过来，这些组装体的功能与它们的三维结构密切相关，这类似于由数十到数百个蛋白质组成的连锁拼图，每个蛋白质都有自己独特的形状。因此，对单个蛋白质的结构以及它们在组装中的结构进行建模的能力对于理解细胞以及更广泛的生物系统的功能至关重要。虽然已经开发了最先进的成像方法来捕获大分子组件作为3D密度体积，例如X射线晶体学和电子冷冻显微镜，但从这种图像创建结构模型仍然是一个耗时且高度手动的过程，部分原因是数据的分辨率有限。该项目的目标是通过设计新颖的计算算法和开发综合建模平台来简化图像到结构的流水线。这些算法寻求利用计算机图形学和视觉的进步，同时结合图像数据、序列数据和专家知识，以提高常见建模任务的效率和准确性。建模平台将整合研究者的方法与第三方建模包，提供一个易于使用的一站式商店，用于从原始图像和单个蛋白质序列创建和验证大分子组装体的结构。该平台将建立在现有Gorgon软件（http：//www.example.com）的基础上，并与流行的EMAN 2软件一起分发，用于密度图的图像分析。gorgon.wustl.edu该项目的成果将对减少生物学家将实验结果转化为知识、发现和治疗所花费的时间和精力产生直接影响。更具体地说，该项目将专注于三项建模任务的算法开发，这些任务目前要么依赖于手工劳动，要么计算成本高昂。这些问题包括检测二级结构元件（例如，α-螺旋和β-折叠），在密度体积中追踪蛋白质骨架，并灵活地将探针结构拟合到体积中。这些算法将建立在计算机图形学和视觉的成功技术基础上，包括使用差分坐标和光谱特征匹配的网格变形。为了将Gorgon转换为建模“中心”，Gorgon的软件架构和界面将在本项目中重新设计，以提高互操作性，可扩展性和可用性。还将为第三方工具开发插件，提供互补的建模能力，如比较建模和蛋白质折叠。