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

> **NIH NIH R01** · CARNEGIE-MELLON UNIVERSITY · 2021 · $112,836

## 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 address 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
reconstruct 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 subtomogram 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
mitochondrial ultrastructures datasets to improve the final resolution. Implementing algorithms in
Aims 1-3, we will develop a user-friendly open-source graphical user interface α-tom to directly
benefit the scientific community. α-tom will be systematically compared with existing software
including IMOD, EMAN2, and Relion on simulated and benchmark datasets. To facilitate
distribution, α-tom will be integrated into existing software platforms Scipion 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
improve our understanding of the structure, function, and spatial organization of macromolecular
complexes in situ.

## Key facts

- **NIH application ID:** 10388867
- **Project number:** 3R01GM134020-02S1
- **Recipient organization:** CARNEGIE-MELLON UNIVERSITY
- **Principal Investigator:** Min Xu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $112,836
- **Award type:** 3
- **Project period:** 2020-06-10 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10388867

## Citation

> US National Institutes of Health, RePORTER application 10388867, Novel machine learning approaches for improving structural discrimination in cryo-electron tomography-Administrative Supplement (3R01GM134020-02S1). Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nih/10388867. Licensed CC0.

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