Image-derived generative modeling of pseudo-macromolecular structures - towards the statistical assessment of Electron CryoTomography template matching

TL;DR

This paper introduces a generative adversarial network-based approach to create pseudo-macromolecular structures from cryo-electron tomography data, enabling statistical hypothesis testing for template matching to improve accuracy and reduce false positives.

Contribution

It presents a novel image-derived generative model for macromolecular structures, facilitating statistical assessment in template matching within cryo-electron tomography.

Findings

High statistical credibility in identifying subtomograms

Significant reduction in false positive rates

Effective application on both simulated and experimental data

Abstract

Cellular Electron CryoTomography (CECT) is a 3D imaging technique that captures information about the structure and spatial organization of macromolecular complexes within single cells, in near-native state and at sub-molecular resolution. Although template matching is often used to locate macromolecules in a CECT image, it is insufficient as it only measures the relative structural similarity. Therefore, it is preferable to assess the statistical credibility of the decision through hypothesis testing, requiring many templates derived from a diverse population of macromolecular structures. Due to the very limited number of known structures, we need a generative model to efficiently and reliably sample pseudo-structures from the complex distribution of macromolecular structures. To address this challenge, we propose a novel image-derived approach for performing hypothesis testing for template matching by constructing generative models using the generative adversarial network. Finally, we conducted hypothesis testing experiments for template matching on both simulated and experimental subtomograms, allowing us to conclude the identity of subtomograms with high statistical credibility and significantly reducing false positives.