# Reliable model building for cryo-EM

> **NIH NIH R44** · LIGO ANALYTICS, INC. · 2022 · $865,376

## Abstract

Project Abstract
Atomic models generated by experimental structural biology are used to understand cellular processes at the
molecular level, and also for rational design of drugs and treatments. Cryogenic electron microscopy single
particle reconstruction (cryo-EM SPR) generates such atomic models based on interpretation of maps of
Coulomb potential that are obtained by averaging many thousands of weak images, with each image containing
projection snapshots of individual macromolecules suspended in a thin layer of ice. Consequently, cryo-EM maps
represent averages of structural states and frequently have limited and uneven quality resulting from both
flexibility of particles used in cryo-EM SPR as well as their non-random distributions in ice called preferred
orientation. Building and rebuilding structural models in maps that are low quality due to lack of detail (resolution)
or having sampling and reconstruction problems is a challenge.
In this proposal, we will address these challenges by developing and implementing methods for automatic and
comprehensive model building and validation with carefully designed feedback loops to experimental data
analysis. The initial and intermediate models generated by our model building methods will aid cryo-EM SPR
projects by stabilizing convergence of computations at all steps of reconstruction, but without introducing bias,
with bias removal and quantification being addressed explicitly. Aim 1 will focus on methods that will use ab initio
predicted models or experimental models to improve cryo-EM SPR so that they can be used without introducing
bias. In Aim 2, a new weighting of information for refinement and validation of the structural models will be
developed and implemented to account for uneven quality of information in cryo-EM SPR. Aim 3 will focus on
developing and implementing methods for directly coupling 2D classification to model building. Finally, Aim 4 will
target analysis and modeling of internal motions to guide the structural interpretation and to improve the
resolution of reconstructions. The results will be incorporated into the commercially distributed suite cryoEMMA.
The competitive advantage of these approaches arises from their ability to provide highly informative 3D
reconstructions in the presence of severe preferred orientation and for single particle signal-to-noise lower than
in the current approaches.

## Key facts

- **NIH application ID:** 10484081
- **Project number:** 2R44GM137671-02
- **Recipient organization:** LIGO ANALYTICS, INC.
- **Principal Investigator:** Raquel Bromberg
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $865,376
- **Award type:** 2
- **Project period:** 2020-08-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10484081

## Citation

> US National Institutes of Health, RePORTER application 10484081, Reliable model building for cryo-EM (2R44GM137671-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10484081. Licensed CC0.

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