Major advances in donor identification, antigen probe design, and experimental methods to clone pathogen-specific antibodies have led to an exponential growth in the number of newly characterized broadly neutralizing antibodies (bnAbs) that recognize the HIV-1 envelope glycoprotein. Characterization of these bnAbs has defined new epitopes and novel modes of recognition that can result in potent neutralization of HIV-1. However, the translation of envelope recognition profiles in biophysical assays into an understanding of in vivo activity has lagged behind, and identification of subjects and mAbs with potent antiviral activity has remained reliant on empirical evaluation of neutralization potency and breadth. To begin to address this discrepancy between recombinant protein recognition and virus neutralization, we studied the fine epitope specificity of a panel of CD4-binding site (CD4bs) antibodies to define the molecular recognition features of functionally potent humoral responses targeting the HIV-1 envelope site bound by CD4. Whereas previous studies have used neutralization data and machine-learning methods to provide epitope maps, here, this approach was reversed, demonstrating that simple binding assays of fine epitope specificity can prospectively identify broadly neutralizing CD4bs–specific mAbs. Building on this result, we show that epitope mapping and prediction of neutralization breadth can also be accomplished in the assessment of polyclonal serum responses. Thus, this study identifies a set of CD4bs bnAb signature amino acid residues and demonstrates that sensitivity to mutations at signature positions is sufficient to predict neutralization breadth of polyclonal sera with a high degree of accuracy across cohorts and across clades.
Hao D. Cheng, Sebastian K. Grimm, Morgan S.A. Gilman, Luc Christian Gwom, Devin Sok, Christopher Sundling, Gina Donofrio, Gunilla B. Karlsson Hedestam, Mattia Bonsignori, Barton F. Haynes, Timothy P. Lahey, Isaac Maro, C. Fordham von Reyn, Miroslaw K. Gorny, Susan Zolla-Pazner, Bruce D. Walker, Galit Alter, Dennis R. Burton, Merlin L. Robb, Shelly J. Krebs, Michael S. Seaman, Chris Bailey-Kellogg, Margaret E. Ackerman
Classification model accuracies