Computational tools to facilitate in situ structure determination using cryo-ET

TL;DR

This paper introduces new computational tools that make it easier to determine protein structures inside cells using cryo-ET, helping researchers study proteins in their natural environment.

Contribution

The paper presents new image processing tools that streamline cryo-ET data analysis for high-resolution in situ structure determination.

Findings

New tools streamline the analysis of large cryo-ET datasets with hundreds of tilt series.

Validation on challenging datasets shows effectiveness in uncovering cellular patterns and conformational heterogeneity.

These tools are expected to broaden the adoption of cryo-ET for high-resolution in situ studies.

Abstract

Cryo-electron tomography combined with sub-volume averaging is a powerful technique capable of determining the structure of proteins imaged within the native context of cells at near-atomic resolution. While high-throughput techniques for sample preparation and tilt-series acquisition are beginning to provide sufficient data to allow structural studies of proteins at physiological concentrations, the complex data analysis pipeline and the demanding storage and computational requirements pose major barriers for the development and broader adoption of this technology. Here, I will present recent advances in image processing that have facilitated the conversion of raw tilt series into high- resolution structures by streamlining the analysis of large datasets containing hundreds of tilt series. We validated these tools on challenging datasets, demonstrating their effectiveness at uncovering cellular patterns and revealing conformational heterogeneity in situ. Availability of these tools will contribute to broadening the adoption of cryo-ET as a powerful technique for studying proteins in their native environment at high-resolution.