# AI-first structural identification of pathogenic protein target interfaces

## TL;DR

AI tools like AlphaFold are used to predict protein structures involved in human-pathogen interactions, tripling structural coverage and revealing potential immune evasion mechanisms.

## Contribution

The study applies AI-driven structure prediction to 9,452 human-pathogen interactions, identifying 30 high-confidence structures and validating one with experimental methods.

## Key findings

- Structure prediction tripled the number of known human-pathogen interaction structures.
- A predicted 1:2:1 heterotetramer involving Francisella tularensis and human IGKC was confirmed experimentally.
- The findings suggest potential roles in immune evasion and highlight AI's role in accelerating vaccine and drug development.

## Abstract

The risk of pandemics is increasing as global population growth and interconnectedness accelerate. Understanding the structural basis of protein-protein interactions between pathogens and hosts is critical for elucidating pathogenic mechanisms and guiding treatment or vaccine development. Despite 21,064 experimentally supported human-pathogen interactions in the HPIDB, only 52 have resolved structures in the PDB, representing just 0.2%. Advances in protein complex structure prediction, such as AlphaFold, now enable highly accurate modelling of heterodimeric complexes, though their application to host-pathogen interactions, which have distinct evolutionary dynamics, remains underexplored. Here, we investigate the structural protein-protein interaction network between humans and ten pathogens, predicting structures for 9,452 interactions, only 10 of which have known structures. We identify 30 interactions with an expected TM-score ≥0.9, tripling the structural coverage in these networks. A detailed analysis of the Francisella tularensis dihydroprolyl dehydrogenase (IPD) complex with human immunoglobulin kappa constant (IGKC) using homology modelling and native mass spectrometry confirms a predicted 1:2:1 heterotetramer, suggesting potential roles in immune evasion. These findings highlight the transformative potential of structure prediction for rapidly advancing vaccine and drug development against novel pathogenic targets.

New infectious diseases are emerging at an increasing pace, and the ability to quickly understand how pathogens interact with the human body is more important than ever. One powerful way to gain this understanding is by examining the three-dimensional structures of the proteins involved, like seeing how puzzle pieces fit together. But while thousands of these interactions have been observed experimentally, only a small fraction have known structures. Recent advances in artificial intelligence, such as AlphaFold, now allow us to predict these structures with high accuracy. In this study, we applied structure prediction to over 9000 protein-protein interactions between humans and ten different pathogens. We generated high-confidence structural models for several interactions that previously lacked any structural information. We further investigated one of these predictions, involving the bacterium Francisella tularensis and a human immune protein, using native mass spectrometry. Our analysis supports the predicted complex and suggests a potential role in immune system evasion. This work demonstrates how structure prediction can rapidly expand our understanding of host-pathogen interactions and help guide the development of new treatments and vaccines against future infectious threats.