AI-first structural identification of pathogenic protein target interfaces

Area of Science:

• Structural biology
• Infectious disease research
• Computational biology

Background:

• Pandemic risk is rising due to increased global connectivity.
• Understanding host-pathogen protein interactions is crucial for disease control.
• Existing structural data for these interactions is severely limited (0.2%).

Purpose of the Study:

• To explore the structural protein-protein interaction network between humans and ten pathogens.
• To leverage advanced protein structure prediction for host-pathogen interactions.
• To identify novel structural targets for therapeutic and vaccine development.

Main Methods:

• Utilized AlphaFold and homology modeling for structure prediction.
• Investigated 9,452 human-pathogen interactions, focusing on those with unknown structures.
• Employed native mass spectrometry for experimental validation.

Main Results:

• Predicted structures for 9,452 interactions, significantly expanding coverage.
• Identified 30 high-confidence interactions (TM-score ≥0.9), tripling existing structural data.
• Confirmed a novel heterotetrameric complex involving Francisella tularensis and human IGKC.

Conclusions:

• Protein structure prediction is a powerful tool for host-pathogen interaction studies.
• The identified interactions offer new targets for drug and vaccine design.
• Findings pave the way for rapid advancement in combating emerging infectious diseases.