AI-first structural identification of pathogenic protein targets

Abstract

The likelihood for pandemics is increasing as the world population grows and becomes more interconnected. Obtaining structural knowledge of protein-protein interactions between a pathogen and its host can inform pathogenic mechanisms and treatment or vaccine design. Currently, there are 52 nonredundant human-pathogen interactions with known structure in the PDB, although there are 21064 with experimental support in the HPIDB, meaning that only 0.2% of known interactions have known structure. Recent improvements in structure prediction of protein complexes based on AlphaFold have made it possible to model heterodimeric complexes with very high accuracy. However, it is not known how this translates to host-pathogen interactions which share a different evolutionary relationship. Here, we analyse the structural protein-protein interaction network between ten different pathogens and their human host. We predict the structure of 9452 human-pathogen interactions of which only 10 have known structure. We find that we can model 30 interactions with an expected TM-score of [≥]0.9, expanding the structural knowledge in these networks three-fold. We select the highly-scoring Francisella tularensis dihydroprolyl dehydrogenase (IPD) complex with human immunoglobulin Kappa constant (IGKC) for detailed analysis with homology modeling and native mass spectrometry. Our results confirm the predicted 1:2:1 heterotetrameric complex with potential implications for bacterial immune response evasion. We are entering a new era where structure prediction can be used to guide vaccine and drug development towards new pathogenic targets in very short time frames.