Abstract
BackgroundRecent efforts in HIV-1 vaccine design have focused on immunogens that evoke potent neutralizing antibody responses to a broad spectrum of viruses circulating worldwide. However, the development of effective vaccines will depend on the identification and characterization of the neutralizing antibodies and their epitopes. We developed bioinformatics methods to predict epitope networks and antigenic determinants using structural information, as well as corresponding genotypes and phenotypes generated by a highly sensitive and reproducible neutralization assay.282 clonal envelope sequences from a multiclade panel of HIV-1 viruses were tested in viral neutralization assays with an array of broadly neutralizing monoclonal antibodies (mAbs: b12, PG9,16, PGT121 - 128, PGT130 - 131, PGT135 - 137, PGT141 - 145, and PGV04). We correlated IC50 titers with the envelope sequences, and used this information to predict antibody epitope networks. Structural patches were defined as amino acid groups based on solvent-accessibility, radius, atomic depth, and interaction networks within 3D envelope models. We applied a boosted algorithm consisting of multiple machine-learning and statistical models to evaluate these patches as possible antibody epitope regions, evidenced by strong correlations with the neutralization response for each antibody.ResultsWe identified patch clusters with significant correlation to IC50 titers as sites that impact neutralization sensitivity and therefore are potentially part of the antibody binding sites. Predicted epitope networks were mostly located within the variable loops of the envelope glycoprotein (gp120), particularly in V1/V2. Site-directed mutagenesis experiments involving residues identified as epitope networks across multiple mAbs confirmed association of these residues with loss or gain of neutralization sensitivity.ConclusionsComputational methods were implemented to rapidly survey protein structures and predict epitope networks associated with response to individual monoclonal antibodies, which resulted in the identification and deeper understanding of immunological hotspots targeted by broadly neutralizing HIV-1 antibodies.
Highlights
Recent efforts in Human Immunodeficiency Virus-1 (HIV-1) vaccine design have focused on immunogens that evoke potent neutralizing antibody responses to a broad spectrum of viruses circulating worldwide
We describe the computational method, and the predicted epitope networks for 21 HIV-1 monoclonal antibodies (mAbs), as well as the results from the lab experiments we performed in order to validate the algorithm
We developed a boosted algorithm consisting of four multivariate and univariate models: multiple linear regression (MLR), logistic regression (LR), support vector machine (SVM), and Fisher’s exact test (FET)
Summary
Recent efforts in HIV-1 vaccine design have focused on immunogens that evoke potent neutralizing antibody responses to a broad spectrum of viruses circulating worldwide. We developed bioinformatics methods to predict epitope networks and antigenic determinants using structural information, as well as corresponding genotypes and phenotypes generated by a highly sensitive and reproducible neutralization assay. HIV-1 vaccine research efforts include finding and characterizing broadly neutralizing antibodies (nAbs), and the epitopes they target [12,13]. Identification of the antigenic targets of nAbs along with mapping the immunologically important residues of known epitopes that affect neutralization is a major goal of current HIV-1 vaccine research. The HIV-1 envelope is highly variable, and as a consequence, identification of key residues that affect neutralization can be complex. The aim of this study is to develop a computational method for discovering and evaluating “epitope networks” that we define here as groups of interacting and variable residues that affect antibody binding
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