Computational and AI-driven 3D structural analysis of human papillomavirus (HPV) oncoproteins E5, E6, and E7 reveal significant divergence of HPV E5 between low-risk and high-risk genotypes

Abstract

There are over 220 identified genotypes of Human papillomavirus (HPV), and the HPV genome encodes 3 major oncogenes, E5, E6, and E7. Conservation and divergence in protein sequence and function between low-risk versus high-risk oncogenic HPV genotypes has not been fully characterized. Here, we used modern computational and structural folding algorithms to perform a comparative analysis of HPV E5, E6, and E7 between multiple low risk and high risk genotypes. We first identified significantly greater sequence divergence in E5 between low- and high-risk genotypes compared to E6 and E7. Next, we used AlphaFold to model the structure of papillomavirus proteins and complexes with high confidence, including some with no established consensus structure. We observed that HPV E5, but not E6 or E7, had a dramatically different 3D structure between low-risk and high-risk genotypes. To our knowledge, this is the first comparative analysis of HPV proteins using Alphafold artificial intelligence (AI) system. The marked differences in E5 sequence and structure in high-risk HPVs may contribute in important and underappreciated ways to the development of HPV-associated cancers.