Computational Prediction of Broadly Neutralizing HIV-1 Antibody Epitopes from Neutralization Activity Data
Ferguson, Andrew L.
Walker, Laura M.
Burton, Dennis R.
Chakraborty, Arup K.
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CitationFerguson, Andrew L., Emilia Falkowska, Laura M. Walker, Michael S. Seaman, Dennis R. Burton, and Arup K. Chakraborty. 2013. “Computational Prediction of Broadly Neutralizing HIV-1 Antibody Epitopes from Neutralization Activity Data.” PLoS ONE 8 (12): e80562. doi:10.1371/journal.pone.0080562. http://dx.doi.org/10.1371/journal.pone.0080562.
AbstractBroadly neutralizing monoclonal antibodies effective against the majority of circulating isolates of HIV-1 have been isolated from a small number of infected individuals. Definition of the conformational epitopes on the HIV spike to which these antibodies bind is of great value in defining targets for vaccine and drug design. Drawing on techniques from compressed sensing and information theory, we developed a computational methodology to predict key residues constituting the conformational epitopes on the viral spike from cross-clade neutralization activity data. Our approach does not require the availability of structural information for either the antibody or antigen. Predictions of the conformational epitopes of ten broadly neutralizing HIV-1 antibodies are shown to be in good agreement with new and existing experimental data. Our findings suggest that our approach offers a means to accelerate epitope identification for diverse pathogenic antigens.
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