Antigenic Variability

Application of in-silico approaches in subunit vaccines: Overcoming the challenges of antigen and adjuvant development

Subunit vaccines are crucial in preventing modern diseases due to their safety, stability, and ability to elicit targeted immune responses. However, challenges in antigen and adjuvant design hinder their development. Recent advancements in in-silico approaches, including reverse vaccinology, structural vaccinology, and machine learning, have revolutionized vaccine development from empirical practices to rational design approaches. This review summarizes the transformative impact of in-silico approaches on subunit vaccine development. We address the challenges of antigen identification and designation, highlighting how advanced computational techniques are employed to accelerate antigen acquisition. We also examine the challenges in adjuvant discovery and illustrate how machine learning helps overcome these barriers. Finally, we explore potential future directions for subunit vaccines, highlighting the importance of combining computational methods with other technologies to tackle the challenges associated with subunit vaccine development.

Eco-evolutionary dynamics of pathogen immune-escape: deriving a population-level phylodynamic curve

The phylodynamic curve (Grenfell et al. 2004 Science 303, 327-332 (doi:10.1126/science.1090727)) conceptualizes how immunity shapes the rate of viral adaptation in a non-monotonic fashion, through its opposing effects on viral abundance and the strength of selection. However, concrete and quantitative model realizations of this influential concept are rare. Here, we present an analytic, stochastic framework in which a population-scale phylodynamic curve emerges dynamically, allowing us to address questions regarding the risk and timing of the emergence of viral immune escape variants. We explore how pathogen- and population-specific parameters such as strength of immunity, transmissibility, seasonality and antigenic constraints affect the emergence risk. For pathogens exhibiting pronounced seasonality, we find that the timing of likely immune-escape variant emergence depends on the level of case importation between regions. Motivated by the COVID-19 pandemic, we probe the likely effects of non-pharmaceutical interventions (NPIs), and the lifting thereof, on the risk of viral escape variant emergence. Looking ahead, the framework has the potential to become a useful tool for probing how natural immunity, as well as choices in vaccine design and distribution and the implementation of NPIs, affect the evolution of common viral pathogens.

Redirecting the host immune response to bacterial infection with antibody-recruiting molecules (ARMs)

The increasing prevalence of antibiotic resistance, the stagnation of antibiotic development, and the adaptive capacity of bacteria to subvert the host immune response combine to pose significant global health concerns. Consequently, there is an urgent need to develop alternative therapeutic approaches to combat bacterial infections. Antibody-recruiting molecules (ARMs), which are bispecific small molecules that recruit endogenous antibodies to pathogenic cells or viruses, offer a promising avenue to harness the host immune system to target various diseases. In this review, we cover ARM strategies that have been developed for bacterial pathogens, including Gram-positive bacteria, Gram-negative bacteria, and mycobacteria, and we discuss the prospects and challenges of utilizing ARMs as alternatives to traditional antibiotic therapies.

Unveiling Interaction Signatures Across Viral Pathogens through VASCO: Viral Antigen-Antibody Structural COmplex dataset

Viral antigen-antibody (Ag-Ab) interactions shape immune responses, drive pathogen neutralization, and inform vaccine strategies. Understanding their structural basis is crucial for predicting immune recognition, optimizing immunogen design to induce broadly neutralizing antibodies (bnAbs), and developing antiviral therapeutics. However, curated structural benchmarks for viral Ag-Ab interactions remain scarce. To address this, we present VASCO (Viral Antibody-antigen Structural COmplex dataset), a high-resolution, non-redundant collection of ~1225 viral Ag-Ab complexes sourced from the Protein Data Bank (PDB) and refined via energy minimization. Spanning Coronaviruses, Influenza, Ebola, HIV, and others, VASCO provides a comprehensive structural reference for viral immune recognition. By comparing VASCO against general protein-protein interactions (GPPI), we identify distinct sequence and structural features that define viral Ag-Ab binding. While conventional descriptors show broad similarities across datasets, deeper analyses reveal key sequence-space interactions, secondary structure preferences, and manifold-derived latent features that distinguish viral complexes. These insights highlight the limitations of GPPI-trained predictive models and the need for specialized computational frameworks. VASCO serves as a critical resource for advancing viral immunology, improving predictive modeling, and guiding immunogen design to elicit protective antibody responses. By bridging sequence and structural immunological datasets, VASCO should enable better docking, affinity prediction, and antiviral therapeutic development-key to pandemic preparedness and emerging pathogen response.

Rational Design and In Vivo Characterization of mRNA-Encoded Broadly Neutralizing Antibody Combinations against HIV-1

Monoclonal antibodies have been used successfully as recombinant protein therapy; however, for HIV, multiple broadly neutralizing antibodies may be necessary. We used the mRNA-LNP platform for in vivo co-expression of 3 broadly neutralizing antibodies, PGDM1400, PGT121, and N6, directed against the HIV-1 envelope protein. mRNA-encoded HIV-1 antibodies were engineered as single-chain Fc (scFv-Fc) to overcome heavy- and light-chain mismatch. In vitro neutralization breadth and potency of the constructs were compared to their parental IgG form. We assessed the ability of these scFv-Fcs to be expressed individually and in combination in vivo, and neutralization and pharmacokinetics were compared to the corresponding full-length IgGs. Single-chain PGDM1400 and PGT121 exhibited neutralization potency comparable to parental IgG, achieving peak systemic concentrations ≥ 30.81 μg/mL in mice; full-length N6 IgG achieved a peak concentration of 974 μg/mL, but did not tolerate single-chain conversion. The mRNA combination encoding full-length N6 IgG and single-chain PGDM1400 and PGT121 was efficiently expressed in mice, achieving high systemic concentration and desired neutralization potency. Analysis of mice sera demonstrated each antibody contributed towards neutralization of multiple HIV-1 pseudoviruses. Together, these data show that the mRNA-LNP platform provides a promising approach for antibody-based HIV treatment and is well-suited for development of combination therapeutics.

Mosaic RBD nanoparticles protect against challenge by diverse sarbecoviruses in animal models

To combat future severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants and spillovers of SARS-like betacoronaviruses (sarbecoviruses) threatening global health, we designed mosaic nanoparticles that present randomly arranged sarbecovirus spike receptor-binding domains (RBDs) to elicit antibodies against epitopes that are conserved and relatively occluded rather than variable, immunodominant, and exposed. We compared immune responses elicited by mosaic-8 (SARS-CoV-2 and seven animal sarbecoviruses) and homotypic (only SARS-CoV-2) RBD nanoparticles in mice and macaques and observed stronger responses elicited by mosaic-8 to mismatched (not on nanoparticles) strains, including SARS-CoV and animal sarbecoviruses. Mosaic-8 immunization showed equivalent neutralization of SARS-CoV-2 variants, including Omicrons, and protected from SARS-CoV-2 and SARS-CoV challenges, whereas homotypic SARS-CoV-2 immunization protected only from SARS-CoV-2 challenge. Epitope mapping demonstrated increased targeting of conserved epitopes after mosaic-8 immunization. Together, these results suggest that mosaic-8 RBD nanoparticles could protect against SARS-CoV-2 variants and future sarbecovirus spillovers.

Mosaic RBD nanoparticles protect against multiple sarbecovirus challenges in animal models

To combat future SARS-CoV-2 variants and spillovers of SARS-like betacoronaviruses (sarbecoviruses) threatening global health, we designed mosaic nanoparticles presenting randomly-arranged sarbecovirus spike receptor-binding domains (RBDs) to elicit antibodies against conserved/relatively-occluded, rather than variable/immunodominant/exposed, epitopes. We compared immune responses elicited by mosaic-8 (SARS-CoV-2 and seven animal sarbecoviruses) and homotypic (only SARS-CoV-2) RBD-nanoparticles in mice and macaques, observing stronger responses elicited by mosaic-8 to mismatched (not on nanoparticles) strains including SARS-CoV and animal sarbecoviruses. Mosaic-8 immunization showed equivalent neutralization of SARS-CoV-2 variants including Omicron and protected from SARS-CoV-2 and SARS-CoV challenges, whereas homotypic SARS-CoV-2 immunization protected only from SARS-CoV-2 challenge. Epitope mapping demonstrated increased targeting of conserved epitopes after mosaic-8 immunization. Together, these results suggest mosaic-8 RBD-nanoparticles could protect against SARS-CoV-2 variants and future sarbecovirus spillovers.

Mathematical Modeling of Proliferative Immune Response Initiated by Interactions Between Classical Antigen-Presenting Cells Under Joint Antagonistic IL-2 and IL-4 Signaling

During an adaptive immune response from pathogen invasion, multiple cytokines are produced by various immune cells interacting jointly at the cellular level to mediate several processes. For example, studies have shown that regulation of interleukin-4 (IL-4) correlates with interleukin-2 (IL-2) induced lymphocyte proliferation. This motivates the need to better understand and model the mechanisms driving the dynamic interplay of proliferation of lymphocytes with the complex interaction effects of cytokines during an immune response. To address this challenge, we adopt a hybrid computational approach comprising of continuous, discrete and stochastic non-linear model formulations to predict a system-level immune response as a function of multiple dependent signals and interacting agents including cytokines and targeted immune cells. We propose a hybrid ordinary differential equation-based (ODE) multicellular model system with a stochastic component of antigen microscopic states denoted as Multiscale Multicellular Quantitative Evaluator (MMQE) implemented using MATLAB. MMQE combines well-defined immune response network-based rules and ODE models to capture the complex dynamic interactions between the proliferation levels of different types of communicating lymphocyte agents mediated by joint regulation of IL-2 and IL-4 to predict the emergent global behavior of the system during an immune response. We model the activation of the immune system in terms of different activation protocols of helper T cells by the interplay of independent biological agents of classic antigen-presenting cells (APCs) and their joint activation which is confounded by the exposure time to external pathogens. MMQE quantifies the dynamics of lymphocyte proliferation during pathogen invasion as bivariate distributions of IL-2 and IL-4 concentration levels. Specifically, by varying activation agents such as dendritic cells (DC), B cells and their joint mechanism of activation, we quantify how lymphocyte activation and differentiation protocols boost the immune response against pathogen invasion mediated by a joint downregulation of IL-4 and upregulation of IL-2. We further compare our in-silico results to in-vivo and in-vitro experimental studies for validation. In general, MMQE combines intracellular and extracellular effects from multiple interacting systems into simpler dynamic behaviors for better interpretability. It can be used to aid engineering of anti-infection drugs or optimizing drug combination therapies against several diseases.