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Open AccessArticle

Predicting Antibody Neutralization Efficacy in Hypermutated Epitopes Using Monte Carlo Simulations

1
Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain
2
IrsiCaixa AIDS Research Institute, 08916 Badalona, Spain
3
Institut Germans Trias i Pujol, 08916 Badalona, Spain
4
School of Medicine, University of Vic–Central University of Catalonia, 08500 Vic, Spain
5
Catalan Institution for Research and Advanced Studies, 08010 Barcelona, Spain
*
Author to whom correspondence should be addressed.
Polymers 2020, 12(10), 2392; https://doi.org/10.3390/polym12102392
Received: 22 September 2020 / Revised: 9 October 2020 / Accepted: 15 October 2020 / Published: 17 October 2020
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. View Full-Text
Keywords: HIV-1; computational modelling; Monte Carlo simulations; antibody binding efficacy; CD4bs antibodies HIV-1; computational modelling; Monte Carlo simulations; antibody binding efficacy; CD4bs antibodies
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MDPI and ACS Style

Amengual-Rigo, P.; Carrillo, J.; Blanco, J.; Guallar, V. Predicting Antibody Neutralization Efficacy in Hypermutated Epitopes Using Monte Carlo Simulations. Polymers 2020, 12, 2392.

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