Biochemical and biophysical characterization of natural polyreactivity in antibodies

Anti-HLA antibodies may be a subset of polyreactive immunoglobulins generated after viral superinfection

Chronic rejection remains an obstacle to long-term allograft survival. Donor-specific anti-HLA antibodies (DSA) play a significant role in causing chronic antibody-mediated allograft rejection. Exposure to mismatched HLA antigens via transfusion, pregnancy, or transplanted tissue has been described in the literature as an immunogenic stimulus of anti-HLA antibodies. Yet anti-HLA antibodies also develop in the absence of traditional sensitization events and molecular mimicry has been postulated as a stimulus for these naturally occurring alloantibodies. While heterologous reactivity has been documented between virus components and allogeneic T cells, there is insufficient evidence to support the development of anti-HLA antibodies from viral components. We hypothesized that anti-HLA antibodies may develop following viral coinfection or superinfection. The objectives of this investigation included: 1) developing an in-silico algorithm to identify viral peptide components that exhibit HLA-specific homology, and 2) identifying cellular changes that take place during ischemia/reperfusion injury which could facilitate the generation of novel anti-HLA antibodies from viral sources. We developed the neoepitope transplant rejection and autoimmune disease (NETRAD) algorithm to identify amino acid sequence homology between viral envelope proteins and HLA. The algorithm integrates post-translational protein modifications that are consistent with ischemia/reperfusion injury. Seventy-two HLA-specific epitopes were demarcated as examples using this approach. In conclusion, we present in-silico evidence which supports the identification of anti-HLA antibodies as a subset of polyreactive antibodies generated from stress-modified viral envelope proteins. Remarkably, each targeted HLA epitope associated with a distinct anti-HLA antibody could be consistently attributed to a major envelope glycoprotein component of Epstein Barr virus. Transplant Immunology manuscript # TRIM-D-24-00351. Dryad data repository:https://doi.org/10.5061/dryad.qjq2bvqpq.

Binding Promiscuity of Therapeutic Factor VIII

The binding promiscuity of proteins defines their ability to indiscriminately bind multiple unrelated molecules. Binding promiscuity is implicated, at least in part, in the off-target reactivity, nonspecific biodistribution, immunogenicity, and/or short half-life of potentially efficacious protein drugs, thus affecting their clinical use. In this review, we discuss the current evidence for the binding promiscuity of factor VIII (FVIII), a protein used for the treatment of hemophilia A, which displays poor pharmacokinetics, and elevated immunogenicity. We summarize the different canonical and noncanonical interactions that FVIII may establish in the circulation and that could be responsible for its therapeutic liabilities. We also provide information suggesting that the FVIII light chain, and especially its C1 and C2 domains, could play an important role in the binding promiscuity. We believe that the knowledge accumulated over years of FVIII usage could be exploited for the development of strategies to predict protein binding promiscuity and therefore anticipate drug efficacy and toxicity. This would open a mutational space to reduce the binding promiscuity of emerging protein drugs while conserving their therapeutic potency.

Hybrid Mass Spectrometry Applied across the Production of Antibody Biotherapeutics

Post expression from the host cells, biotherapeutics undergo downstream processing steps before final formulation. Mass spectrometry and biophysical characterization methods are valuable for examining conformational and stoichiometric changes at these stages, although typically not used in biomanufacturing, where stability is assessed via bulk property studies. Here we apply hybrid MS methods to understand how solution condition changes impact the structural integrity of a biopharmaceutical across the processing pipeline. As an exemplar product, we use the model IgG1 antibody, mAb4. Flexibility, stability, aggregation propensity, and bulk properties are evaluated in relation to perfusion media, purification stages, and formulation solutions. Comparisons with Herceptin, an extensively studied IgG1 antibody, were conducted in a mass spectrometry-compatible solution. Despite presenting similar charge state distributions (CSD) in native MS, mAb4, and Herceptin show distinct unfolding patterns in activated ion mobility mass spectrometry (aIM-MS) and differential scanning fluorimetry (DSF). Herceptin's greater structural stability and aggregation onset temperature (Tagg) are attributed to heavier glycosylation and kappa-class light chains, unlike the lambda-class light chains in mAb4. Hydrogen-deuterium exchange mass spectrometry (HDX-MS) revealed that mAb4 undergoes substantial structural changes during purification, marked by high flexibility, low melting temperature (Tm), and prevalent repulsive protein-protein interactions but transitions to a compact and stable structure in high-salt and formulated environments. Notably, in formulation, the third constant domain (CH3) of the heavy chain retains flexibility and is a region of interest for aggregation. Future work could translate features of interest from comprehensive studies like this to targeted approaches that could be utilized early in the development stage to aid in decision-making regarding targeted mutations or to guide the design space of bioprocesses and formulation choices.

Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions

Understanding binding epitopes involved in protein-protein interactions and accurately determining their structure are long-standing goals with broad applicability in industry and biomedicine. Although various experimental methods for binding epitope determination exist, these approaches are typically low throughput and cost-intensive. Computational methods have potential to accelerate epitope predictions; however, recently developed artificial intelligence (AI)-based methods frequently fail to predict epitopes of synthetic binding domains with few natural homologues. Here we have developed an integrated method employing generalized-correlation-based dynamic network analysis on multiple molecular dynamics (MD) trajectories, initiated from AlphaFold2Multimer structures, to unravel the structure and binding epitope of the therapeutic PD-L1:Affibody complex. Both AlphaFold2 and conventional molecular dynamics trajectory analysis were ineffective in distinguishing between two proposed binding models, parallel and perpendicular. However, our integrated approach, utilizing dynamic network analysis, demonstrated that the perpendicular mode was significantly more stable. These predictions were validated using a suite of experimental epitope mapping protocols, including cross-linking mass spectrometry and next-generation sequencing-based deep mutational scanning. Conversely, AlphaFold3 failed to predict a structure bound in the perpendicular pose, highlighting the necessity for exploratory research in the search for binding epitopes and challenging the notion that AI-generated protein structures can be accepted without scrutiny. Our research underscores the potential of employing dynamic network analysis to enhance AI-based structure predictions for more accurate identification of protein-protein interaction interfaces.

Single-domain antibodies against SARS-CoV-2 RBD from a two-stage phage screening of universal and focused synthetic libraries

BACKGROUND

Coronavirus disease 2019 (COVID-19) is an evolving global pandemic, and nanobodies, as well as other single-domain antibodies (sdAbs), have been recognized as a potential diagnostic and therapeutic tool for infectious diseases. High-throughput screening techniques such as phage display have been developed as an alternative to in vivo immunization for the discovery of antibody-like target-specific binders.

METHODS

We designed and constructed a highly diverse synthetic phage library sdAb-U (single-domain Antibody - Universal library ) based on a human framework. The SARS-CoV-2 receptor-binding domain (RBD) was expressed and purified. The universal library sdAb-U was panned against the RBD protein target for two rounds, followed by monoclonal phage ELISA (enzyme-linked immunosorbent assay) to identify RBD-specific binders (the first stage). High-affinity binders were sequenced and the obtained CDR1 and CDR2 sequences were combined with fully randomized CDR3 to construct a targeted (focused) phage library sdAb-RBD, for subsequent second-stage phage panning (also two rounds) and screening. Then, sequences with high single-to-background ratios in phage ELISA were selected for expression. The binding affinities of sdAbs to RBD were measured by an ELISA-based method. In addition, we conducted competition ELISA (using ACE2 ectodomain S19-D615) and SARS-CoV-2 pseudovirus neutralization assays for the high-affinity RBD-binding sdAb39.

RESULTS

Significant enrichments were observed in both the first-stage (universal library) and the second-stage (focused library) phage panning. Five RBD-specific binders were identified in the first stage with high ELISA signal-to-background ratios. In the second stage, we observed a much higher possibility of finding RBD-specific clones in phage ELISA. Among 45 selected RBD-positive sequences, we found eight sdAbs can be well expressed, and five of them show high-affinity to RBD (EC50 < 100nM). We finally found that sdAb39 (EC50 ~ 4nM) can compete with ACE2 for binding to RBD.

CONCLUSION

Overall, this two-stage strategy of synthetic phage display libraries enables rapid selection of SARS-CoV-2 RBD sdAb with potential therapeutic activity, and this two-stage strategy can potentially be used for rapid discovery of sdAbs against other targets.