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.

这项研究旨在开发新的方法和软件工具，用于从成像数据中开采大分子组件的挖掘结构。大分子组件（例如核糖体和病毒）负责驱动几乎所有细胞事件。这些组件的功能反过来又与它们的3结构密切相关，它们类似于互锁的难题，这些难题由数十个到数百种蛋白质组成，每个蛋白质都有其独特的形状。因此，对于理解细胞以及更广泛的生物系统功能的方式至关重要，对单个蛋白质及其在组装中的结构进行建模的能力至关重要。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密度量（例如X射线晶体学和电子冷冻微观），但从这种成像中创建结构模型仍是一种耗时且高度手动的过程数据的有限分辨率。该项目的目的是通过设计新颖的计算算法并开发全面的建模平台来简化图像对结构管道。该算法试图利用计算机图形和视觉的进步，同时结合图像数据，序列数据和专家知识，以提高通用建模任务的效率和准确性。该建模平台将将研究者的方法与第三方建模软件包集成在一起，以提供易于使用的一站式服务，以从原始图像和单个蛋白质序列从原始图像和单个蛋白质序列创建和验证大分子组件的结构。该平台将建立在现有的Gorgon软件（http://gorgon.wustl.edu）上，并与流行的EMAN2软件一起分发，用于密度图的图像分析。该项目的结果将直接影响减少生物学家在将实验结果转化为知识，发现和治疗的时间和精力。更具体地说，该项目将重点介绍算法开发目前依赖三个建模任务的算法开发手动劳动或计算上的昂贵。这些问题包括在各种非原子分辨率上检测二级结构元素（例如，α-螺旋和β-片），将蛋白质骨架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。该算法将基于计算机图形和视觉的成功技术，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“集线器”，该项目将重新设计Gorgon的软件体系结构和界面，以提高操作性，可伸缩性和可用性。插件还将用于第三方工具，这些工具提供互补的建模能力，例如比较建模和蛋白质折叠。