Graph identification of proteins in tomograms (GRIP-Tomo)

TL;DR

This paper introduces a novel graph-based pattern mining method for identifying proteins in tomograms that is robust to data distortions and does not rely on predefined templates, improving interpretability and classification accuracy.

Contribution

The study presents a topological graph-based approach for protein identification in tomograms that is invariant to common data distortions and does not require prior templates or human bias.

Findings

Accurately identified proteins from simulated tomograms with distortions.

Demonstrated robustness to missing data, tumbling, and missing wedge effects.

Enabled classification of proteins based on topological features.

Abstract

In this study, we present a method of pattern mining based on network theory that enables the identification of protein structures or complexes from synthetic volume densities, without the knowledge of predefined templates or human biases for refinement. We hypothesized that the topological connectivity of protein structures is invariant, and they are distinctive for the purpose of protein identification from distorted data presented in volume densities. Three-dimensional densities of a protein or a complex from simulated tomographic volumes were transformed into mathematical graphs as observables. We systematically introduced data distortion or defects such as missing fullness of data, the tumbling effect, and the missing wedge effect into the simulated volumes, and varied the distance cutoffs in pixels to capture the varying connectivity between the density cluster centroids in the presence of defects. A similarity score between the graphs from the simulated volumes and the graphs transformed from the physical protein structures in point data was calculated by comparing their network theory order parameters including node degrees, betweenness centrality, and graph densities. By capturing the essential topological features defining the heterogeneous morphologies of a network, we were able to accurately identify proteins and homo-multimeric complexes from ten topologically distinctive samples without realistic noise added. Our approach empowers future developments of tomogram processing by providing pattern mining with interpretability, to enable the classification of single-domain protein native topologies as well as distinct single-domain proteins from multimeric complexes within noisy volumes.