Predicting Antibody Neutralization Efficacy in Hypermutated Epitopes Using Monte Carlo Simulations

Human Immunodeficiency Virus 1 (HIV-1) evades adaptive immunity by means of its extremely high mutation rate, which allows the HIV envelope glycoprotein to continuously escape from the action of antibodies. However, some broadly neutralizing antibodies (bNAbs) targeting specific viral regions show the ability to block the infectivity of a large number of viral variants. The discovery of these antibodies opens new avenues in anti-HIV therapy; however, they are still suboptimal tools as their amplitude of action ranges between 50% and 90% of viral variants. In this context, being able to discriminate between sensitive and resistant strains to an antibody would be of great interest for the design of optimal clinical antibody treatments and to engineer potent bNAbs for clinical use. Here, we describe a hierarchical procedure to predict the antibody neutralization efficacy of multiple viral isolates to three well-known anti-CD4bs bNAbs: VRC01, NIH45-46 and 3BNC117. Our method consists of simulating the three-dimensional binding process between the gp120 and the antibody by using Protein Energy Landscape Exploration (PELE), a Monte Carlo stochastic approach. Our results clearly indicate that the binding profiles of sensitive and resistant strains to a bNAb behave differently, showing the latter’s weaker binding profiles, that can be exploited for predicting antibody neutralization efficacy in hypermutated HIV-1 strains.
This research was funded by a predoctoral fellowship from the Government of Catalonia (2020FI_B2_00138 to P.A.-R.). The research was conducted under funds from the Spanish Government, grant PID2019-106370RB-I00. J.B. laboratory is funded by the Spanish Institute of Health Carlos III (ISCIII, projects PI17/01518 and PI20/00093) and by MAC Foundation (project E-C-YY-24414).
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