# Comprehensive analysis of macromolecule structural variability in CryoEM/CryoET

> **NIH NIH R01** · STANFORD UNIVERSITY · 2024 · $365,956

## Abstract

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.

## Key facts

- **NIH application ID:** 10932180
- **Project number:** 5R01GM150905-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Muyuan Chen
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $365,956
- **Award type:** 5
- **Project period:** 2023-09-21 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10932180

## Citation

> US National Institutes of Health, RePORTER application 10932180, Comprehensive analysis of macromolecule structural variability in CryoEM/CryoET (5R01GM150905-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10932180. Licensed CC0.

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