Protein structure classification based on X-ray laser induced Coulomb explosion

TL;DR

This study demonstrates that Coulomb explosion footprints generated by high-intensity X-ray ionization can effectively classify proteins with similar structures, offering a novel approach for protein identification and analysis.

Contribution

The paper introduces a new method using simulated Coulomb explosion footprints combined with machine learning for protein classification based on shape and atomic content.

Findings

Explosion footprints can distinguish structurally similar proteins.

Principal component analysis and t-SNE effectively classify proteins.

Method could complement existing imaging techniques.

Abstract

We simulated the Coulomb explosion dynamics due to the fast ionization induced by high-intensity X-rays in six proteins that share similar atomic content and shape. We followed and projected the trajectory of the fragments onto a virtual detector, providing a unique explosion footprint. After collecting 500 explosion footprints for each protein, we utilized principal component analysis and t-distributed stochastic neighbor embedding to classify these. The results show that the classification algorithms were able to separate proteins on the basis of explosion footprints from structurally similar proteins into distinct groups. The explosion footprints, therefore, provide a unique identifier for each of the proteins. We envision that method could be used concurrently with single particle coherent imaging experiments to provide additional information on shape, mass, or conformation.