Learning context-aware structural representations to predict antigen and antibody binding interfaces.

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.

Antibody Fab-Fc properties outperform titer in predictive models of SIV vaccine-induced protection.

Characterizing the antigen-binding and innate immune-recruiting properties of the humoral response offers the chance to obtain deeper insights into mechanisms of protection than revealed by measuring only overall antibody titer. Here, a high-throughput, multiplexed Fab-Fc Array was employed to profile rhesus macaques vaccinated with a gp120-CD4 fusion protein in combination with different genetically encoded adjuvants, and subsequently subjected to multiple heterologous simian immunodeficiency virus (SIV) challenges. Systems analyses modeling protection and adjuvant differences using Fab-Fc Array measurements revealed a set of correlates yielding strong and robust predictive performance, while models based on measurements of response magnitude alone exhibited significantly inferior performance. At the same time, rendering Fab-Fc measurements mathematically independent of titer had relatively little impact on predictive performance. Similar analyses for a distinct SIV vaccine study also showed that Fab-Fc measurements performed significantly better than titer. These results suggest that predictive modeling with measurements of antibody properties can provide detailed correlates with robust predictive power, suggest directions for vaccine improvement, and potentially enable discovery of mechanistic associations.

Systems serology for decoding infection and vaccine-induced antibody responses to HIV-1.

PURPOSE OF REVIEW: Experimental and analytical advances have enabled systematic, high-resolution studies of humoral immune responses, and are beginning to define mechanisms of immunity to HIV. RECENT FINDINGS: High-throughput, information-rich experimental and analytical methods, whether genomic, proteomic, or transcriptomic, have firmly established their value across a diversity of fields. Consideration of these tools as trawlers in 'fishing expeditions' has faded as 'data-driven discovery' has come to be valued as an irreplaceable means to develop fundamental understanding of biological systems. Collectively, studies of HIV-1 infection and vaccination including functional, biophysical, and biochemical humoral profiling approaches have provided insights into the phenotypic characteristics of individual and pools of antibodies. Relating these measures to clinical status, protection/efficacy outcomes, and cellular profiling data using machine learning has offered the possibility of identifying unanticipated mechanisms of action and gaining insights into fundamental immunological processes that might otherwise be difficult to decipher. SUMMARY: Recent evidence establishes that systematic data collection and application of machine learning approaches can identify humoral immune correlates that are generalizable across distinct HIV-1 immunogens and vaccine regimens and translatable between model organisms and the clinic. These outcomes provide a strong rationale supporting the utility and further expansion of these approaches both in support of vaccine development and more broadly in defining mechanisms of immunity.

Route of immunization defines multiple mechanisms of vaccine-mediated protection against SIV.

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.

Pentavalent HIV-1 vaccine protects against simian-human immunodeficiency virus challenge.

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-1ß 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.