Residue-level prediction of HIV-1 antibody epitopes based on neutralization of diverse viral strains.

TitleResidue-level prediction of HIV-1 antibody epitopes based on neutralization of diverse viral strains.
Publication TypeJournal Article
Year of Publication2013
AuthorsChuang G-Y, Acharya P, Schmidt SD, Yang Y, Louder MK, Zhou T, Kwon Y D, Pancera M, Bailer RT, Doria-Rose NA, Nussenzweig MC, Mascola JR, Kwong PD, Georgiev IS
JournalJ Virol
Date Published09/01/2013
KeywordsAntibodies, Neutralizing, Computational Biology, env Gene Products, Human Immunodeficiency Virus, Epitopes, HIV Antibodies, HIV-1

Delineation of antibody epitopes at the residue level is key to understanding antigen resistance mutations, designing epitope-specific probes for antibody isolation, and developing epitope-based vaccines. Ideally, epitope residues are determined in the context of the atomic-level structure of the antibody-antigen complex, though structure determination may in many cases be impractical. Here we describe an efficient computational method to predict antibody-specific HIV-1 envelope (Env) epitopes at the residue level, based on neutralization panels of diverse viral strains. The method primarily utilizes neutralization potency data over a set of diverse viral strains representing the antigen, and enhanced accuracy could be achieved by incorporating information from the unbound structure of the antigen. The method was evaluated on 19 HIV-1 Env antibodies with neutralization panels comprising 181 diverse viral strains and with available antibody-antigen complex structures. Prediction accuracy was shown to improve significantly over random selection, with an average of greater-than-8-fold enrichment of true positives at the 0.05 false-positive rate level. The method was used to prospectively predict epitope residues for two HIV-1 antibodies, 8ANC131 and 8ANC195, for which we experimentally validated the predictions. The method is inherently applicable to antigens that exhibit sequence diversity, and its accuracy was found to correlate inversely with sequence conservation of the epitope. Together the results show how knowledge inherent to a neutralization panel and unbound antigen structure can be utilized for residue-level prediction of antibody epitopes.

Alternate JournalJ. Virol.
PubMed ID23843642
PubMed Central IDPMC3753990
Grant ListAI 100148-01 / AI / NIAID NIH HHS / United States
AI 100663-01 / AI / NIAID NIH HHS / United States
P01 AI100148 / AI / NIAID NIH HHS / United States
/ / Howard Hughes Medical Institute / United States
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