Identifying Structural Properties of Proteins from X-ray Free Electron Laser Diffraction Patterns

Capturing structural information of a biological molecule is crucial to determine its function and understand its mechanics. X-ray Free Electron Lasers (XFEL) are an experimental method used to create diffraction patterns (images) that can reveal structural information. In this work we design, implement, and evaluate XPSI (X-ray Free Electron Laser-based Protein Structure Identifier), a framework capable of predicting three structural properties in molecules (i.e., orientation, conformation, and protein type) from their diffraction patterns. XPSI predicts these properties with high accuracy in challenging scenarios, such as recognizing orientations despite symmetries in diffraction patterns, distinguishing conformations even when they have similar structures, and identifying protein types under different noise conditions. Our framework shows low computational cost and high prediction accuracy compared to other machine learning methods such as random forest and neural networks.

Automated summary

Capturing the structural information of biological molecules is crucial for understanding their function and mechanism. This research presents a framework, XPSI, which utilizes X-ray Free Electron Lasers to accurately predict the orientation, conformation, and protein type of molecules from their diffraction patterns. Compared to other machine learning methods, XPSI shows low computational cost and high prediction accuracy.