# Novel machine learning approaches for improving structural discrimination in cryo-electron tomography

> **NIH NIH R01** · CARNEGIE-MELLON UNIVERSITY · 2022 · $327,428

## Abstract

Project Summary
Cellular cryo-electron tomography (Cryo-ET) has made possible the observation of cellular organelles and
macromolecular complexes at nanometer resolution with native conformations. The rapid increasing amount
of Cryo-ET data available however brings along some major challenges for analysis which we will timely ad-
dress in this proposal. We will design novel data-driven machine learning algorithms for improving structural
discrimination and resolution. In particular, we have the following specific aims: (1) We will develop a novel
Autoencoder and Iterative region Matching (AIM) algorithm for marker-free alignment of image tilt-series to re-
construct tomograms with improved resolution; (2) We will develop a saliency-based auto-picking algorithm for
better detecting macromolecular complexes, and combine it with an innovative 2D-to-3D framework to further
improve structure detection accuracy; (3) We will design an end-to-end convolutional model for pose-invariant
clustering of subtomograms. This model will produce an initial clustering which will be refined by a new subto-
mogram averaging algorithm that automatically down-weights subtomograms of noise and little contribution; (4)
We will perform experimental evaluations by using previously reported bacterial secretion systems and mito-
chondrial ultrastructures datasets to improve the final resolution. Implementing algorithms in Aims 1-3, we will
develop a user-friendly open-source graphical user interface -tom to directly benefit the scientific community.
 -tom will be systematically compared with existing software including IMOD, EMAN2, and Relion on simulated
and benchmark datasets. To facilitate distribution, -tom will be integrated into existing software platforms Sci-
pion and TomoMiner. Our data-driven algorithms and software not only will facilitate and accelerate the future
use of Cryo-ET, but also can be readily used on analyzing the existing large amounts of Cryo-ET data to im-
prove our understanding of the structure, function, and spatial organization of macromolecular complexes in
situ.

## Key facts

- **NIH application ID:** 10454131
- **Project number:** 5R01GM134020-03
- **Recipient organization:** CARNEGIE-MELLON UNIVERSITY
- **Principal Investigator:** Min Xu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $327,428
- **Award type:** 5
- **Project period:** 2020-06-10 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10454131

## Citation

> US National Institutes of Health, RePORTER application 10454131, Novel machine learning approaches for improving structural discrimination in cryo-electron tomography (5R01GM134020-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10454131. Licensed CC0.

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