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Comprehensive analysis of macromolecule structural variability in CryoEM/CryoET

CryoEM/CryoET 中大分子结构变异性的综合分析

基本信息

批准号：
10711754
负责人：
Muyuan Chen
金额：
$ 36.6万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-21 至 2028-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10711754
关键词：
3-Dimensional Address Algorithms Amino Acid Sequence Behavior Biochemistry Biological Process Cells Classification Complex Computing Methodologies Cryoelectron Microscopy Data Set Environment Exercise Gaussian model Goals Heterogeneity Homologous Protein Ice Image In Situ Individual Knowledge Maps Memory Methods Modeling Modernization Molecular Conformation Movement Noise Property Protein Analysis Protein Conformation Proteins Protocols documentation Research Personnel Resolution Rotation Sampling Signal Transduction Software Tools Structure Structure-Activity Relationship Sum System Tomogram Visualization Work biological systems combat computerized data processing computerized tools cost cryogenics deep neural network density dynamic system electron tomography flexibility innovation insight macromolecule microscopic imaging molecular modeling novel particle protein complex protein structure structural biology

项目摘要

Project Summary This proposal aims to develop computational tools that analyze the structural variability of the macromolecules imaged by Cryogenic electron microscopy (CryoEM) and Cryogenic electron tomography (CryoET). As the function of most macromolecules involves dynamic interactions among their own components or with other molecules, the structural flexibility of those macromolecules is often key to accomplishing their functions. CryoEM/CryoET makes snapshots of macromolecules embedded in vitrified ice, which provides direct information of individual protein particles in different compositional and conformational states. Using advanced computational methods, we will be able to resolve the structural heterogeneity of proteins and gain a deeper understanding of their structure-function relationship. The algorithm developed in this proposal will be using the Gaussian mixture model for protein structure representation and deep neural network for embedding snapshot images of proteins onto a latent space depicting their conformational states. In this proposal, we address the issue of protein structural variability from three aspects. First, we will build a pipeline for simultaneous orientation and conformation refinement for single particle analysis, which will make it possible to solve systems with large-scale structural variability. Second, we will integrate constraints from molecular models into our pipeline, so that prior knowledge from biochemistry can be used to guide the protein heterogeneity analysis. Finally, we will focus on CryoET and expand the method to look into the dynamic of macromolecular systems inside cells. In sum, the proposed work will produce software tools for a comprehensive analysis of protein structural variability, which will provide new insights into the functioning mechanism of macromolecules.

项目总结