Graph Identification of Proteins in Tomograms (GRIP-Tomo) 2.0: Topologically Aware Classification for Proteins

TL;DR

GRIP-Tomo 2.0 introduces a topologically aware machine learning pipeline that efficiently identifies proteins in cryo-ET data, significantly improving accuracy and robustness over previous methods.

Contribution

It presents a novel graph network-based approach with topological features and synthetic noise simulation, enhancing protein detection in cryo-ET images.

Findings

Over 90% accuracy on synthetic datasets

Over 80% accuracy on real datasets

Enhanced performance with new pipeline upgrades

Abstract

Cryo-electron tomography (cryo-ET) enables structural characterization of biomolecules under near-native conditions. Existing approaches for interpreting the resulting three-dimensional volumes are computationally expensive and have difficulty interpreting density associated with small proteins/complexes. To explore alternate approaches for identifying proteins in cryo-ET data we pursued a Graph Network and topologically invariant approach. Here, we report on a fast algorithm that distinguishes volumes containing protein density from noise by searching for nuances of evolutionarily conversed motifs and the geometrical characteristics of protein structure. GRIP-Tomo 2.0 is a machine-learning pipeline that extracts interpretable topological features of protein structures within noisy experimental backgrounds. Compared to version 1.0, the new pipeline includes three upgrades that significantly improve performance including synthetic tomogram generation simulating realistic noise, graph-based persistent feature extraction as protein fingerprints, and high-performance computing acceleration. GRIP-Tomo 2.0 achieves over 90% accuracy in distinguishing proteins from noise for synthetic datasets and over 80% accuracy for real datasets, which represents a foundational step toward advancing cryo-ET workflows and empowering automated detection of both small and large proteins for visual proteomics.