The principles of affinity maturation of antibodies (Abs), which underlies B cell-mediated immunity, are still under debate. It is unclear whether the antigen (Ag) binding site is a preferred target for mutations, and what the role of activation-induced deaminase (AID) hotspots is in this process. Here we report a structural analysis of 3495 residues that have been replaced through somatic hypermutations (SHMs) in 196 Abs. We show that there is no correlation between the propensity of an amino acid to be in AID hotspot and the probability that it is replaced during the SHM process. Although AID hotspots may be necessary to enable SHMs, they are not a major driving force in determining which residues are mutated. We identified Ab positions that are highly mutated and significantly affect binding. The effect of mutation on binding energy is a major factor in determining which structural regions of the Ab are mutated. There is a clear preference for mutations at the Ag-binding site. However, positions outside this region that also affect binding are often preferred targets for SHMs. As for amino acid preferences, a general trend during SHM is to make Ab-Ag interfaces more similar to protein-protein interfaces in general. In different regions of the Ab, there are different sets of preferences for amino acid substitution. This mapping improves our understanding of Ab affinity maturation and may assist in Ab engineering.

Determining which parts of the Ab are essential for Ag recognition and binding is crucial for understanding B cell–mediated immunity. Identification of fragments of Abs that maintain specificity to the Ag will also allow for the development of improved Ab-based therapy and diagnostics. In this article, we show that structural analysis of Ab–Ag complexes reveals which fragments of the Ab may bind the Ag on their own. In particular, it is possible to predict whether a given CDR is likely to bind the Ag as a peptide by analyzing the energetic contribution of each CDR to Ag binding and by assessing to what extent the interaction between that CDR and the Ag depends on other CDRs. To demonstrate this, we analyzed five Ab–Ag complexes and predicted for each of them which of the CDRs may bind the Ag on its own as a peptide. We then show that these predictions are in agreement with our experimental analysis and with previously published experimental results. These findings promote our understanding of the modular nature of Ab–Ag interactions and lay the foundation for the rational design of active CDR-derived peptides.

Synthetic libraries are a major source of human-like antibody (Ab) drug leads. To assess the similarity between natural Abs and the products of these libraries, we compared large sets of natural and synthetic Abs using "CDRs Analyzer," a tool we introduce for structural analysis of Ab-antigen (Ag) interactions. Natural Abs, we found, recognize their Ags by combining multiple complementarity-determining regions (CDRs) to create an integrated interface. Synthetic Abs, however, rely dominantly, sometimes even exclusively on CDRH3. The increased contribution of CDRH3 to Ag binding in synthetic Abs comes with a substantial decrease in the involvement of CDRH2 and CDRH1. Furthermore, in natural Abs CDRs specialize in specific types of non-covalent interactions with the Ag. CDRH1 accounts for a significant portion of the cation-pi interactions; CDRH2 is the major source of salt-bridges and CDRH3 accounts for most hydrogen bonds. In synthetic Abs this specialization is lost, and CDRH3 becomes the main sources of all types of contacts. The reliance of synthetic Abs on CDRH3 reduces the complexity of their interaction with the Ag: More Ag residues contact only one CDR and fewer contact 3 CDRs or more. We suggest that the focus of engineering attempts on CDRH3 results in libraries enriched with variants that are not naturallike. This may affect not only Ag binding, but also Ab expression, stability and selectivity. Our findings can help guide library design, creating libraries that can bind more epitopes and Abs that better mimic the natural antigenic interactions.

Obesity is associated with increased morbidity and mortality from influenza and SARS-CoV-2 infection. While vaccination is the most effective strategy for preventing influenza virus infection, our previous studies showed that influenza vaccines fail to provide optimal protection in obese individuals despite reaching canonical correlates of protection.

As highlighted by the ongoing COVID-19 pandemic, vaccination is critical for infectious disease prevention and control. Obesity is associated with increased morbidity and mortality from respiratory virus infections. While obese individuals respond to influenza vaccination, what is considered a seroprotective response may not fully protect the global obese population. In a cohort vaccinated with the 2010-2011 trivalent inactivated influenza vaccine, baseline immune history and vaccination responses were found to significantly differ in obese individuals compared to healthy controls, especially towards the 2009 pandemic strain of A/H1N1 influenza virus. Young, obese individuals displayed responses skewed towards linear peptides versus conformational antigens, suggesting aberrant obese immune response. Overall, these data have vital implications for the next generation of influenza vaccines, and towards the current SARS-CoV-2 vaccination campaign.

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The global emergence of SARS-CoV-2 variants and Omicron in particular, has highlighted the risk of novel variants that will evade adaptive immune responses following vaccination and natural infection. Determining which viral mutations escape T-cells and antibodies is crucial for designing therapeutics and vaccines and assessing the implications of newly emerging variants. We developed an automated immunoinformatic pipeline that considers both data on viral sequences and experimentally identified T-cell and antibody responses, to identify specific viral mutations that may be associated with immune escape. We used an unsupervised clustering method to cluster a set of 108 non-redundant anti-SARS-CoV-2 spike protein antibodies with solved 3D structures from the PDB database, and identified 9 unique antibody clusters. We computed the predicted changes in binding energies (ΔΔG) for each antibody and each specific mutation within its contact footprint. Using these ΔΔG scores, we computed an antibody escape score for each mutation. We also predicted the change in binding between predicted and experimentally verified T-cell epitopes and their presenting MHC molecules. We ranked frequent spike protein mutations based on their predicted effect on immune evasion, as well as individual SARS-CoV-2 variants, with a specific focus on VOCs. Our novel immune escape scores identified several key mutations in the Omicron variant, which were not present in previous VOCs, and may be used to identify key mutations that are likely associated with immune escape, which may appear in novel variants of concern. Supported by an NIH grant (75N93021C00016)