The RV144 Thai trial HIV-1 vaccine of recombinant poxvirus (ALVAC) and recombinant HIV-1 gp120 subtype B/subtype E (B/E) proteins demonstrated 31% vaccine efficacy. Here we design an ALVAC/Pentavalent B/E/E/E/E vaccine to increase the diversity of gp120 motifs in the immunogen to elicit a broader antibody response and enhance protection. We find that immunization of rhesus macaques with the pentavalent vaccine results in protection of 55% of pentavalent-vaccine-immunized macaques from simian–human immunodeficiency virus (SHIV) challenge. Systems serology of the antibody responses identifies plasma antibody binding to HIV-infected cells, peak ADCC antibody titres, NK cell-mediated ADCC and antibody-mediated activation of MIP-1β in NK cells as the four immunological parameters that best predict decreased infection risk that are improved by the pentavalent vaccine. Thus inclusion of additional gp120 immunogens to a pox-prime/protein boost regimen can augment antibody responses and enhance protection from a SHIV challenge in rhesus macaques.

Antibodies are the primary correlate of protection for most licensed vaccines; however, their mechanisms of protection may vary, ranging from physical blockade to clearance via the recruitment of innate immunity. Here, we uncover striking functional diversity in vaccine-induced antibodies that is driven by immunization site and is associated with reduced risk of SIV infection in nonhuman primates. While equivalent levels of protection were observed following intramuscular (IM) and aerosol (AE) immunization with an otherwise identical DNA prime-Ad5 boost regimen, reduced risk of infection was associated with IgG-driven antibody-dependent monocyte-mediated phagocytosis in the IM vaccinees, but with vaccine-elicited IgA-driven neutrophil-mediated phagocytosis in AE-immunized animals. Thus, although route-independent correlates indicate a critical role for phagocytic Fc-effector activity in protection from SIV, the site of immunization may drive this Fc activity via distinct innate effector cells and antibody isotypes. Moreover, the same correlates predicted protection from SHIV infection in a second nonhuman primate vaccine trial using a disparate IM canarypox prime-protein boost strategy, analogous to that used in the first moderately protective human HIV vaccine trial. These data identify orthogonal functional humoral mechanisms, initiated by distinct vaccination routes and immunization strategies, pointing to multiple, potentially complementary correlates of immunity that may support the rational design of a protective vaccine against HIV.

Abstract Motivation Understanding how antibodies specifically interact with their antigens can enable better drug and vaccine design, as well as provide insights into natural immunity. Experimental structural characterization can detail the ‘ground truth’ of antibody–antigen interactions, but computational methods are required to efficiently scale to large-scale studies. To increase prediction accuracy as well as to provide a means to gain new biological insights into these interactions, we have developed a unified deep learning-based framework to predict binding interfaces on both antibodies and antigens. Results Our framework leverages three key aspects of antibody–antigen interactions to learn predictive structural representations: (i) since interfaces are formed from multiple residues in spatial proximity, we employ graph convolutions to aggregate properties across local regions in a protein; (ii) since interactions are specific between antibody–antigen pairs, we employ an attention layer to explicitly encode the context of the partner; (iii) since more data are available for general protein–protein interactions, we employ transfer learning to leverage this data as a prior for the specific case of antibody–antigen interactions. We show that this single framework achieves state-of-the-art performance at predicting binding interfaces on both antibodies and antigens, and that each of its three aspects drives additional improvement in the performance. We further show that the attention layer not only improves performance, but also provides a biologically interpretable perspective into the mode of interaction. Availability and implementation The source code is freely available on github at https://github.com/vamships/PECAN.git.

AbstractUnderstanding how antibodies specifically interact with their antigens can enable better drug and vaccine design, as well as provide insights into natural immunity. Experimental structural characterization can detail the “ground truth” of antibody-antigen interactions, but computational methods are required to efficiently scale to large-scale studies. In order to increase prediction accuracy as well as to provide a means to gain new biological insights into these interactions, we have developed a unified deep learning-based framework to predict binding interfaces on both antibodies and antigens. The framework leverages three key aspects of antibody-antigen interactions in order to learn predictive structural representations: (1) since interfaces are formed from multiple residues in spatial proximity, we employ graph convolutions to aggregate properties across local regions in a protein; (2) since interactions are specific between antibody-antigen pairs, we employ an attention layer to explicitly encode the context of the partner; (3) since more data is available for general protein-protein interactions, we employ transfer learning to leverage this data as a prior for the specific case of antibody-antigen interactions. We show that this single framework achieves state-of-the-art performance at predicting binding interfaces on both antibodies and antigens, and that each of its three aspects drives additional improvement in the performance. We further show that the attention layer not only improves performance, but also provides a biologically interpretable perspective into the mode of interaction.