Mapping Polyclonal HIV-1 Antibody Responses via Next-Generation Neutralization Fingerprinting

Computational neutralization fingerprinting, NFP, is an efficient and accurate method for predicting the epitope specificities of polyclonal antibody responses to HIV-1 infection. Here, we present next-generation NFP algorithms that substantially improve prediction accuracy for individual donors and enable serologic analysis for entire cohorts. Specifically, we developed algorithms for: (a) selection of optimized virus neutralization panels for NFP analysis, (b) estimation of NFP prediction confidence for each serum sample, and (c) identification of sera with potentially novel epitope specificities. At the individual donor level, the next-generation NFP algorithms particularly improved the ability to detect multiple epitope specificities in a sample, as confirmed both for computationally simulated polyclonal sera and for samples from HIV-infected donors. Specifically, the next-generation NFP algorithms detected multiple specificities in twice as many samples of simulated sera. Further, unlike the first-generation NFP, the new algorithms were able to detect both of the previously confirmed antibody specificities, VRC01-like and PG9-like, in donor CHAVI 0219. At the cohort level, analysis of ~150 broadly neutralizing HIV-infected donor samples suggested a potential connection between clade of infection and types of elicited epitope specificities. Most notably, while 10E8-like antibodies were observed in infections from different clades, an enrichment of such antibodies was predicted for clade B samples. Ultimately, such large-scale analyses of antibody responses to HIV-1 infection can help guide the design of epitope-specific vaccines that are tailored to take into account the prevalence of infecting clades within a specific geographic region. Overall, the next-generation NFP technology will be an important tool for the analysis of broadly neutralizing polyclonal antibody responses against HIV-1.Author Summary: HIV-1 remains a significant global health threat, with no effective vaccine against the virus currently available. Since traditional vaccine design efforts have had limited success, much effort in recent years has focused on gaining a better understanding of the ways select individuals are able to effectively neutralize the virus upon natural infection, and to utilize that knowledge for the design of optimized vaccine candidates. Primary emphasis has been placed on characterizing the antibody arm of the immune system, and specifically on antibodies capable of neutralizing the majority of circulating HIV-1 strains. Various experimental techniques can be applied to map the epitope targets of these antibodies, but more recently, the development of computational methods has provided an efficient and accurate alternative for understanding the complex antibody responses to HIV-1 in a given individual. Here, we present the next generation of this computational technology, and show that these new methods have significantly improved accuracy and confidence, and that they enable the interrogation of biologically important questions that can lead to new insights for the design of an effective vaccine against HIV-1.