# Reconstruction of heterogeneous and small macromolecules by cyro-EM

> **NIH NIH R01** · PRINCETON UNIVERSITY · 2020 · $328,440

## Abstract

PROJECT SUMMARY
Single-particle electron cryomicroscopy (cryo-EM) has recently joined X-ray crystallography and
NMR spectroscopy as a high-resolution structural method for biological macromolecules. In
addition, cryo-EM produces images of individual molecules, and therefore has the potential to
resolve conformational changes. The proposal aims to develop new algorithms and software for
extending the application of cryo-EM to molecules that are either too small or too flexible to be
mapped by existing computational tools for cryo-EM. This extension requires solving two of the
most challenging computational problems posed by cryo-EM.
First, mapping the structural variability of macromolecules is widely recognized as the main
computational challenge in cryo-EM. Structural variations are of great significance to biologists,
as they provide insight into the functioning of molecular machines. Existing computational tools
are limited to a small number of distinct conformations, and therefore are incapable of tackling
highly mobile biomolecules with multiple, continuous spectra of conformational changes. The first
area of investigation in this project is the development of a computational framework to analyze
continuous variability. The proposed approach is based on a new mathematical representation of
continuously changing structures and its efficient estimation using Markov chain Monte Carlo
(MCMC) algorithms. MCMC algorithms have found great success in many other scientific
disciplines, yet they have been mostly overlooked for cryo-EM single particle analysis.
Second, a major limiting factor for present cryo-EM studies is the molecule size. Images of small
molecules (below ~50kDa) have too little signal to allow existing methods to provide valid 3-D
reconstructions. It is commonly believed that cryo-EM cannot be used for molecules that are too
small to be reliably detected and picked from micrographs. Challenging that widespread belief,
the second area of investigation focuses on developing a groundbreaking approach for
reconstructing small molecules directly from micrographs without particle picking. The new
approach is based on autocorrelation analysis and completely bypasses particle picking and
orientation assignment and requires just one pass over the data. The single-pass approach
opens new possibilities for real-time processing during data acquisition.

## Key facts

- **NIH application ID:** 9943364
- **Project number:** 1R01GM136780-01
- **Recipient organization:** PRINCETON UNIVERSITY
- **Principal Investigator:** Amit Singer
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $328,440
- **Award type:** 1
- **Project period:** 2020-06-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9943364

## Citation

> US National Institutes of Health, RePORTER application 9943364, Reconstruction of heterogeneous and small macromolecules by cyro-EM (1R01GM136780-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9943364. Licensed CC0.

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