Building protein structure models for intermediate resolution cryo-electron microscopy maps

建立中等分辨率冷冻电子显微镜图的蛋白质结构模型

基本信息

批准号：
10266083
负责人：
Daisuke Kihara
金额：
$ 30.55万
依托单位：
PURDUE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-20 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10266083
关键词：
3-Dimensional Algorithms Amino Acids Area Biological Cells Chimera organism Code Communication Communities Complement Computer software Computing Methodologies Cryoelectron Microscopy DNA Data Development Disease Electron Microscopy Goals Grain Human Intervention Investigation Knowledge Ligands Maps Methodology Methods Modeling Molecular Molecular Conformation Multiprotein Complexes Nature Positioning Attribute Preparation Protein Region Proteins Publishing RNA Research Research Personnel Resolution Sampling Side Source Code Structural Models Structure Techniques Three-Dimensional Image Three-dimensional analysis Visualization software X-Ray Crystallography base computerized tools data acquisition deep learning density detection method graphical user interface image processing improved machine learning method macromolecular assembly macromolecule model building novel programs protein function protein structure protein structure prediction repository software development structural biology three dimensional structure tool

项目摘要

Project Summary Cryo-electron microscopy (cryo-EM) is an emerging technique in structural biology, which is capable of determining three-dimensional (3D) structures of biological macromolecules. Compared to conventional structural biology techniques, such as X-ray crystallography and NMR, a major advantage of cryo-EM is its ability to solve large macromolecular assemblies. Moreover, recent technical breakthroughs in cryo-EM have enabled determination of 3D structures at nearly atomic-level resolutions. Cryo-EM will undoubtedly become a method of central importance in structural biology in the next decade. With the rapid accumulation of cryo-EM structure data, it has become crucial to develop computational methods that can effectively build and extract 3D structures of biological macromolecules from EM maps. The goal of this project is to develop computational methods for modeling both global and local structures and for interpreting 3D structures embedded in EM maps of around 4 Å to medium-resolution. Recently, we have developed a new de novo protein structure modeling method, MAINMAST, which can model protein structures from an EM density map without using existing template or fragment structures on the map. Based on the successful development of MAINMAST, we further extend the capability of MAINMAST toward more accurate modeling and for multiple-chain modeling. In addition, we will also develop novel modeling methods for medium-resolution EM maps, which combine a coarse-grained protein structure modeling technique, methods in protein structure prediction, and a low- resolution image processing approach with deep learning, a state-of-the-art powerful machine learning method. The proposed project capitalizes on the tremendous efforts and progress made in structural determination with cryo-EM by developing computational tools that allow researchers to perform efficient and reliable structure analyses for 3D EM density maps. The project will greatly facilitate investigation into the molecular mechanisms of macromolecule function by providing an efficient means of 3D structure modeling.

项目摘要冷冻电子显微镜（Cryo-EM）是结构生物学的新兴技术，能够确定生物大分子的三维（3D）结构。与常规结构生物学技术，例如X射线晶体学和NMR，Cryo-EM的主要优势是它能够求解大型大分子组件的能力。而且，Cryo-EM最近的技术突破已启用了几乎原子级分辨率下的3D结构的确定。冷冻 - 毫无疑问将成为一个在未来十年中，结构生物学的核心重要性。随着冷冻EM的快速积累结构数据，开发可以有效构建和提取的计算方法至关重要来自EM图的生物大分子的3D结构。该项目的目的是开发计算建模全球和局部结构以及解释嵌入在EM中的3D结构的方法大约4Å至中分辨率的地图。最近，我们开发了一种新的从头蛋白结构建模方法，主流，可以在不使用EM密度图中对蛋白质结构进行建模地图上的现有模板或碎片结构。基于Mainmast的成功发展，我们进一步扩展了主流对更准确的建模和多链建模的能力。在此外，我们还将开发用于中分辨率EM图的新型建模方法，该方法结合了粗粒蛋白结构建模技术，蛋白质结构预测的方法，低 - 深度学习的分辨率图像处理方法是一种最先进的强大机器学习方法。拟议的项目利用了与结构决心所取得的巨大努力和进步通过开发允许研究人员执行高效可靠结构的计算工具的冷冻EM 分析3D EM密度图。该项目将极大地促进对分子的研究大分子功能的机理通过提供3D结构建模的有效均值。