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.