Computational Prediction of pH-Dependent Binding Energies in HPV Capsid Antibody Interactions

Abstract

HPV is the most common sexually transmitted infection in the world. In high-risk types, HPV infection is associated with virtually all cervical cancers and a significant proportion of anogenital and oropharyngeal cancers. Neutralizing antibodies can prevent HPV infection with their effectiveness depending on the way they interact with HPV capsid proteins. The ability of antibodies to attach to capsid proteins is influenced by pH variations, which can impact the characteristics and stability of both the viral capsid and the antibody. In this thesis, we apply a computational simulation pipeline to predict the pH-dependent binding energies in HPV capsid-antibody interactions. Our results predict that there is a strong preference for binding to antibody 28F10 for HPV subtypes 6, 16, 18, 33, and 58 while A12A3 shows a strong preference for HPV 35 and 59. The results also predict that both antibodies bind non-preferentially to the HPV 11 capsid.