# Reliable model building for cryo-EM

> **NIH NIH R43** · LIGO ANALYTICS, INC. · 2020 · $223,916

## Abstract

Project Abstract
Reliability of structural models is important for mechanistic studies of cellular processes and for rational design
of drugs and treatment. Cryo-EM single particle reconstruction (Cryo-EM SPR) is an expanding technique that
can generate atomic models for structural biology and has the advantage that it does not require samples to be
crystallized. Instead, atomic models are built based on maps obtained by averaging hundreds of thousands of
weak images, with each image containing snapshots of individual macromolecules suspended in a thin layer of
ice. Due to the intrinsic difficulties with averaging noisy images, cryo-EM maps frequently have limited resolution
and may represent averages of multiple structural states. Building and rebuilding structural models reliably in
such maps remains a challenge and the process is additionally complicated by the lack of established criteria for
validating the quality of models built at low resolution.
When groups of a few atoms are individually recognizable in maps, established methods easily create reliable
models. However, it is problematic when only larger groups of atoms are individually recognizable, and this is
referred to as low resolution. It has been observed that for resolutions as low as 5 Å, reliable models can be built
(with effort) if the maps are highly precise. This observation is an important factor for our plan to develop and
implement methods for automatic, comprehensive and accurate building of atomic models for such low
resolution. To achieve this goal, in Aim 1 we will improve the low resolution quality of cryo-EM maps by computing
maps so that they are corrected for physical effects currently not properly considered in map creation. In Aim 2,
we will develop and implement an integrative procedure for automatic model building at low resolution. It will
combine a fast 6D search using a library of continuous and discontinuous fragments obtained through the data
mining of known structures. The hypotheses obtained from the 6D search will be analyze for forming self-
consistent groups in a multi-stage process. This will be followed by GPU-accelerated molecular dynamics
computations restrained by experimental data. The output of dynamics will undergo an additional layer of data
mining to identify and trigger corrections of problematic starting assumptions and to create a concise description
of information present in a multitude of calculated structural states. The final step will involve an assessment of
possible unresolved ambiguities by the experimenter, who may have additional relevant knowledge guiding the
selection of a specific structural hypothesis.
Our SBIR phase I proposal will result in two software modules that will be incorporated into a commercial solution
for data processing, analysis, and validation in cryo-EM.

## Key facts

- **NIH application ID:** 10082049
- **Project number:** 1R43GM137671-01A1
- **Recipient organization:** LIGO ANALYTICS, INC.
- **Principal Investigator:** Yirui Guo
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $223,916
- **Award type:** 1
- **Project period:** 2020-08-01 → 2021-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10082049

## Citation

> US National Institutes of Health, RePORTER application 10082049, Reliable model building for cryo-EM (1R43GM137671-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10082049. Licensed CC0.

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