Contemporary HIV-1 consensus Env with AI-assisted redesigned hypervariable loops promote antibody binding.

An effective HIV-1 vaccine must elicit broadly neutralizing antibodies (bnAbs) against highly diverse Envelope glycoproteins (Env). Since Env with the longest hypervariable (HV) loops is more resistant to the cognate bnAbs than Env with shorter HV loops, we redesigned hypervariable loops for updated Env consensus sequences of subtypes B and C and CRF01_AE. Using modeling with AlphaFold2, we reduced the length of V1, V2, and V5 HV loops while maintaining the integrity of the Env structure and glycan shield, and modified the V4 HV loop. Spacers are designed to limit strain-specific targeting. All updated Env are infectious as pseudoviruses. Preliminary structural characterization suggests that the modified HV loops have a limited impact on Env's conformation. Binding assays show improved binding to modified subtype B and CRF01_AE Env but not to subtype C Env. Neutralization assays show increases in sensitivity to bnAbs, although not always consistently across clades. Strikingly, the HV loop modification renders the resistant CRF01_AE Env sensitive to 10-1074 despite the absence of a glycan at N332.

Multi-epitope vaccine design using in silico analysis of glycoprotein and nucleocapsid of NIPAH virus.

According to the 2018 WHO R&D Blueprint, Nipah virus (NiV) is a priority disease, and the development of a vaccine against NiV is strongly encouraged. According to criteria used to categorize zoonotic diseases, NiV is a stage III disease that can spread to people and cause unpredictable outbreaks. Since 2001, the NiV virus has caused annual outbreaks in Bangladesh, while in India it has caused occasional outbreaks. According to estimates, the mortality rate for infected individuals ranges from 70 to 91%. Using immunoinformatic approaches to anticipate the epitopes of the MHC-I, MHC-II, and B-cells, they were predicted using the NiV glycoprotein and nucleocapsid protein. The selected epitopes were used to develop a multi-epitope vaccine construct connected with linkers and adjuvants in order to improve immune responses to the vaccine construct. The 3D structure of the engineered vaccine was anticipated, optimized, and confirmed using a variety of computer simulation techniques so that its stability could be assessed. According to the immunological simulation tests, it was found that the vaccination elicits a targeted immune response against the NiV. Docking with TLR-3, 7, and 8 revealed that vaccine candidates had high binding affinities and low binding energies. Finally, molecular dynamic analysis confirms the stability of the new vaccine. Codon optimization and in silico cloning showed that the proposed vaccine was expressed to a high degree in Escherichia coli. The study will help in identifying a potential epitope for a vaccine candidate against NiV. The developed multi-epitope vaccine construct has a lot of potential, but they still need to be verified by in vitro & in vivo studies.

GNUV201, a novel human/mouse cross-reactive and low pH-selective anti-PD-1 monoclonal antibody for cancer immunotherapy.

BACKGROUND: Several PD-1 antibodies approved as anti-cancer therapies work by blocking the interaction of PD-1 with its ligand PD-L1, thus restoring anti-cancer T cell activities. These PD-1 antibodies lack inter-species cross-reactivity, necessitating surrogate antibodies for preclinical studies, which may limit the predictability and translatability of the studies. RESULTS: To overcome this limitation, we have developed an inter-species cross-reactive PD-1 antibody, GNUV201, by utilizing an enhanced diversity mouse platform (SHINE MOUSE™). GNUV201 equally binds to human PD-1 and mouse PD-1, equally inhibits the binding of human PD-1/PD-L1 and mouse PD-1/PD-L1, and effectively suppresses tumor growth in syngeneic mouse models. The epitope of GNUV201 mapped to the "FG loop" of hPD-1, distinct from those of Keytruda® ("C'D loop") and Opdivo® (N-term). Notably, the structural feature where the protruding epitope loop fits into GNUV201's binding pocket supports the enhanced binding affinity due to slower dissociation (8.7 times slower than Keytruda®). Furthermore, GNUV201 shows a stronger binding affinity at pH 6.0 (5.6 times strong than at pH 7.4), which mimics the hypoxic and acidic tumor microenvironment (TME). This phenomenon is not observed with marketed antibodies (Keytruda®, Opdivo®), implying that GNUV201 achieves more selective binding to and better occupancy on PD-1 in the TME. CONCLUSIONS: In summary, GNUV201 exhibited enhanced affinity for PD-1 with slow dissociation and preferential binding in TME-mimicking low pH. Human/monkey/mouse inter-species cross-reactivity of GNUV201 could enable more predictable and translatable efficacy and toxicity preclinical studies. These results suggest that GNUV201 could be an ideal antibody candidate for anti-cancer drug development.

Tumor Antigens beyond the Human Exome.

With the advent of immunotherapeutics, a new era in the combat against cancer has begun. Particularly promising are neo-epitope-targeted therapies as the expression of neo-antigens is tumor-specific. In turn, this allows the selective targeting and killing of cancer cells whilst healthy cells remain largely unaffected. So far, many advances have been made in the development of treatment options which are tailored to the individual neo-epitope repertoire. The next big step is the achievement of efficacious "off-the-shelf" immunotherapies. For this, shared neo-epitopes propose an optimal target. Given the tremendous potential, a thorough understanding of the underlying mechanisms which lead to the formation of neo-antigens is of fundamental importance. Here, we review the various processes which result in the formation of neo-epitopes. Broadly, the origin of neo-epitopes can be categorized into three groups: canonical, noncanonical, and viral neo-epitopes. For the canonical neo-antigens that arise in direct consequence of somatic mutations, we summarize past and recent findings. Beyond that, our main focus is put on the discussion of noncanonical and viral neo-epitopes as we believe that targeting those provides an encouraging perspective to shape the future of cancer immunotherapeutics.

Preparation and epitope analysis of monoclonal antibodies against African swine fever virus DP96R protein.

BACKGROUND: Many proteins of African swine fever virus (ASFV, such as p72, p54, p30, CD2v, K205R) have been successfully expressed and characterized. However, there are few reports on the DP96R protein of ASFV, which is the virulence protein of ASFV and plays an important role in the process of host infection and invasion of ASFV. RESULTS: Firstly, the prokaryotic expression vector of DP96R gene was constructed, the prokaryotic system was used to induce the expression of DP96R protein, and monoclonal antibody was prepared by immunizing mice. Four monoclonal cells of DP96R protein were obtained by three ELISA screening and two sub-cloning; the titer of ascites antibody was up to 1:500,000, and the monoclonal antibody could specifically recognize DP96R protein. Finally, the subtypes of the four strains of monoclonal antibodies were identified and the minimum epitopes recognized by them were determined. CONCLUSION: Monoclonal antibody against ASFV DP96R protein was successfully prepared and identified, which lays a foundation for further exploration of the structure and function of DP96R protein and ASFV diagnostic technology.

Characterization of a novel format scFv×VHH single-chain biparatopic antibody against metal binding protein MtsA.

Biparatopic antibodies (bpAbs) are engineered antibodies that bind to multiple different epitopes within the same antigens. bpAbs comprise diverse formats, including fragment-based formats, and choosing the appropriate molecular format for a desired function against a target molecule is a challenging task. Moreover, optimizing the design of constructs requires selecting appropriate antibody modalities and adjusting linker length for individual bpAbs. Therefore, it is crucial to understand the characteristics of bpAbs at the molecular level. In this study, we first obtained single-chain variable fragments and camelid heavy-chain variable domains targeting distinct epitopes of the metal binding protein MtsA and then developed a novel format single-chain bpAb connecting these fragment antibodies with various linkers. The physicochemical properties, binding activities, complex formation states with antigen, and functions of the bpAb were analyzed using multiple approaches. Notably, we found that the assembly state of the complexes was controlled by a linker and that longer linkers tended to form more compact complexes. These observations provide detailed molecular information that should be considered in the design of bpAbs.

Identification and characterization of a nonbiological small-molecular mimic of a Zika virus conformational neutralizing epitope.

Antigenic similarities between Zika virus (ZIKV) and other flaviviruses pose challenges to the development of virus-specific diagnostic tools and effective vaccines. Starting with a DNA-encoded one-bead-one-compound combinatorial library of 508,032 synthetic, non-natural oligomers, we selected and characterized small molecules that mimic ZIKV epitopes. High-throughput fluorescence-activated cell sorter-based bead screening was used to select molecules that bound IgG from ZIKV-immune but not from dengue-immune sera. Deep sequencing of the DNA from the "Zika-only" beads identified 40 candidate molecular structures. A lead candidate small molecule "CZV1-1" was selected that correctly identifies serum specimens from Zika-experienced patients with good sensitivity and specificity (85.3% and 98.4%, respectively). Binding competition studies of purified anti-CZV1-1 IgG against known ZIKV-specific monoclonal antibodies (mAbs) showed that CZV1-1 mimics a nonlinear, neutralizing conformational epitope in the domain III of the ZIKV envelope. Purified anti-CZV1-1 IgG neutralized infection of ZIKV in cell cultures with potencies comparable to highly specific ZIKV-neutralizing mAbs. This study demonstrates an innovative approach for identification of synthetic non-natural molecular mimics of conformational virus epitopes. Such molecular mimics may have value in the development of accurate diagnostic assays for Zika, as well as for other viruses.

Functional and antigenic characterization of SARS-CoV-2 spike fusion peptide by deep mutational scanning.

The fusion peptide of SARS-CoV-2 spike protein is functionally important for membrane fusion during virus entry and is part of a broadly neutralizing epitope. However, sequence determinants at the fusion peptide and its adjacent regions for pathogenicity and antigenicity remain elusive. In this study, we perform a series of deep mutational scanning (DMS) experiments on an S2 region spanning the fusion peptide of authentic SARS-CoV-2 in different cell lines and in the presence of broadly neutralizing antibodies. We identify mutations at residue 813 of the spike protein that reduced TMPRSS2-mediated entry with decreased virulence. In addition, we show that an F823Y mutation, present in bat betacoronavirus HKU9 spike protein, confers resistance to broadly neutralizing antibodies. Our findings provide mechanistic insights into SARS-CoV-2 pathogenicity and also highlight a potential challenge in developing broadly protective S2-based coronavirus vaccines.

Reinvestigation of the internal glycan rearrangement of Lewis a and blood group type H1 epitopes.

Protonated ions of fucose-containing oligosaccharides are prone to undergo internal glycan rearrangement which results in chimeric fragments that obfuscate mass-spectrometric analysis. Lack of accessible tools that would facilitate systematic analysis of glycans in the gas phase limits our understanding of this phenomenon. In this work, we use density functional theory modeling to interpret cryogenic IR spectra of Lewis a and blood group type H1 trisaccharides and to establish whether these trisaccharides undergo the rearrangement during gas-phase analysis. Structurally unconstrained search reveals that none of the parent ions constitute a thermodynamic global minimum. In contrast, predicted collision cross sections and anharmonic IR spectra provide a good match to available experimental data which allowed us to conclude that fucose migration does not occur in these antigens. By comparing the predicted structures with those obtained for Lewis x and blood group type H2 epitopes, we demonstrate that the availability of the mobile proton and a large difference in the relative stability of the parent ions and rearrangement products constitute the prerequisites for the rearrangement reaction.

Enhanced potency of an IgM-like nanobody targeting conserved epitope in SARS-CoV-2 spike N-terminal domain.

Almost all the neutralizing antibodies targeting the receptor-binding domain (RBD) of spike (S) protein show weakened or lost efficacy against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged or emerging variants, such as Omicron and its sub-variants. This suggests that highly conserved epitopes are crucial for the development of neutralizing antibodies. Here, we present one nanobody, N235, displaying broad neutralization against the SARS-CoV-2 prototype and multiple variants, including the newly emerged Omicron and its sub-variants. Cryo-electron microscopy demonstrates N235 binds a novel, conserved, cryptic epitope in the N-terminal domain (NTD) of the S protein, which interferes with the RBD in the neighboring S protein. The neutralization mechanism interpreted via flow cytometry and Western blot shows that N235 appears to induce the S1 subunit shedding from the trimeric S complex. Furthermore, a nano-IgM construct (MN235), engineered by fusing N235 with the human IgM Fc region, displays prevention via inducing S1 shedding and cross-linking virus particles. Compared to N235, MN235 exhibits varied enhancement in neutralization against pseudotyped and authentic viruses in vitro. The intranasal administration of MN235 in low doses can effectively prevent the infection of Omicron sub-variant BA.1 and XBB in vivo, suggesting that it can be developed as a promising prophylactic antibody to cope with the ongoing and future infection.

Predicting TCR sequences for unseen antigen epitopes using structural and sequence features.

T-cell receptor (TCR) recognition of antigens is fundamental to the adaptive immune response. With the expansion of experimental techniques, a substantial database of matched TCR-antigen pairs has emerged, presenting opportunities for computational prediction models. However, accurately forecasting the binding affinities of unseen antigen-TCR pairs remains a major challenge. Here, we present convolutional-self-attention TCR (CATCR), a novel framework tailored to enhance the prediction of epitope and TCR interactions. Our approach utilizes convolutional neural networks to extract peptide features from residue contact matrices, as generated by OpenFold, and a transformer to encode segment-based coded sequences. We introduce CATCR-D, a discriminator that can assess binding by analyzing the structural and sequence features of epitopes and CDR3-ß regions. Additionally, the framework comprises CATCR-G, a generative module designed for CDR3-ß sequences, which applies the pretrained encoder to deduce epitope characteristics and a transformer decoder for predicting matching CDR3-ß sequences. CATCR-D achieved an AUROC of 0.89 on previously unseen epitope-TCR pairs and outperformed four benchmark models by a margin of 17.4%. CATCR-G has demonstrated high precision, recall and F1 scores, surpassing 95% in bidirectional encoder representations from transformers score assessments. Our results indicate that CATCR is an effective tool for predicting unseen epitope-TCR interactions. Incorporating structural insights enhances our understanding of the general rules governing TCR-epitope recognition significantly. The ability to predict TCRs for novel epitopes using structural and sequence information is promising, and broadening the repository of experimental TCR-epitope data could further improve the precision of epitope-TCR binding predictions.

Multi-epitope protein production and its application in the diagnosis of opisthorchiasis.

BACKGROUND: Opisthorchiasis and cholangiocarcinoma (CCA) continue to be public health concerns in many Southeast Asian countries. Although the prevalence of opisthorchiasis is declining, reported cases tend to have a light-intensity infection. Therefore, early detection by using sensitive methods is necessary. Several sensitive methods have been developed to detect opisthorchiasis. The immunological detection of antigenic proteins has been proposed as a sensitive method for examining opisthorchiasis. METHODS: The Opisthorchis viverrini antigenic proteins, including cathepsin B (OvCB), asparaginyl endopeptidase (OvAEP), and cathepsin F (OvCF), were used to construct multi-antigenic proteins. The protein sequences of OvCB, OvAEP, and OvCF, with a high probability of B cell epitopes, were selected using BepiPred 1.0 and the IEDB Analysis Resource. These protein fragments were combined to form OvCB_OvAEP_OvCF recombinant DNA, which was then used to produce a recombinant protein in Escherichia coli strain BL21(DE3). The potency of the recombinant protein as a diagnostic target for opisthorchiasis was assessed using immunoblotting and compared with that of the gold standard method, the modified formalin-ether concentration technique. RESULTS: The recombinant OvCB_OvAEP_OvCF protein showed strong reactivity with total immunoglobulin G (IgG) antibodies against light-intensity O. viverrini infections in the endemic areas. Consequently, a high sensitivity (100%) for diagnosing opisthorchiasis was reported. However, cross-reactivity with sera from other helminth and protozoan infections (including taeniasis, strongyloidiasis, giardiasis, E. coli infection, enterobiasis, and mixed infection of Echinostome spp. and Taenia spp.) and no reactivity with sera from patients with non-parasitic infections led to a reduced specificity of 78.4%. In addition, the false negative rate (FNR), false positive rate (FPR), positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy were 0%, 21.6%, 81.4%, 100%, and 88.9%, respectively. CONCLUSIONS: The high sensitivity of the recombinant OvCB_OvAEP_OvCF protein in detecting opisthorchiasis demonstrates its potential as an opisthorchiasis screening target. Nonetheless, research on reducing cross-reactivity should be undertaken by detecting other antibodies in other sample types, such as saliva, urine, and feces.

Characterizing the antigenic evolution of pandemic influenza A (H1N1) pdm09 from 2009 to 2023.

The H1N1pdm09 virus has been a persistent threat to public health since the 2009 pandemic. Particularly, since the relaxation of COVID-19 pandemic mitigation measures, the influenza virus and SARS-CoV-2 have been concurrently prevalent worldwide. To determine the antigenic evolution pattern of H1N1pdm09 and develop preventive countermeasures, we collected influenza sequence data and immunological data to establish a new antigenic evolution analysis framework. A machine learning model (XGBoost, accuracy = 0.86, area under the receiver operating characteristic curve = 0.89) was constructed using epitopes, physicochemical properties, receptor binding sites, and glycosylation sites as features to predict the antigenic similarity relationships between influenza strains. An antigenic correlation network was constructed, and the Markov clustering algorithm was used to identify antigenic clusters. Subsequently, the antigenic evolution pattern of H1N1pdm09 was analyzed at the global and regional scales across three continents. We found that H1N1pdm09 evolved into around five antigenic clusters between 2009 and 2023 and that their antigenic evolution trajectories were characterized by cocirculation of multiple clusters, low-level persistence of former dominant clusters, and local heterogeneity of cluster circulations. Furthermore, compared with the seasonal H1N1 virus, the potential cluster-transition determining sites of H1N1pdm09 were restricted to epitopes Sa and Sb. This study demonstrated the effectiveness of machine learning methods for characterizing antigenic evolution of viruses, developed a specific model to rapidly identify H1N1pdm09 antigenic variants, and elucidated their evolutionary patterns. Our findings may provide valuable support for the implementation of effective surveillance strategies and targeted prevention efforts to mitigate the impact of H1N1pdm09.

Immunoinformatics design of a structural proteins driven multi-epitope candidate vaccine against different SARS-CoV-2 variants based on fynomer.

The ideal vaccines for combating diseases that may emerge in the future require more than simply inactivating a few pathogenic strains. This study aims to provide a peptide-based multi-epitope vaccine effective against various severe acute respiratory syndrome coronavirus 2 strains. To design the vaccine, a library of peptides from the spike, nucleocapsid, membrane, and envelope structural proteins of various strains was prepared. Then, the final vaccine structure was optimized using the fully protected epitopes and the fynomer scaffold. Using bioinformatics tools, the antigenicity, allergenicity, toxicity, physicochemical properties, population coverage, and secondary and three-dimensional structures of the vaccine candidate were evaluated. The bioinformatic analyses confirmed the high quality of the vaccine. According to further investigations, this structure is similar to native protein and there is a stable and strong interaction between vaccine and receptors. Based on molecular dynamics simulation, structural compactness and stability in binding were also observed. In addition, the immune simulation showed that the vaccine can stimulate immune responses similar to real conditions. Finally, codon optimization and in silico cloning confirmed efficient expression in Escherichia coli. In conclusion, the fynomer-based vaccine can be considered as a new style in designing and updating vaccines to protect against coronavirus disease.

Enhancing tuberculosis vaccine development: a deconvolution neural network approach for multi-epitope prediction.

Tuberculosis (TB) a disease caused by Mycobacterium tuberculosis (Mtb) poses a significant threat to human life, and current BCG vaccinations only provide sporadic protection, therefore there is a need for developing efficient vaccines. Numerous immunoinformatic methods have been utilized previously, here for the first time a deep learning framework based on Deconvolutional Neural Networks (DCNN) and Bidirectional Long Short-Term Memory (DCNN-BiLSTM) was used to predict Mtb Multiepitope vaccine (MtbMEV) subunits against six Mtb H37Rv proteins. The trained model was used to design MEV within a few minutes against TB better than other machine learning models with 99.5% accuracy. The MEV has good antigenicity, and physiochemical properties, and is thermostable, soluble, and hydrophilic. The vaccine's BLAST search ruled out the possibility of autoimmune reactions. The secondary structure analysis revealed 87% coil, 10% beta, and 2% alpha helix, while the tertiary structure was highly upgraded after refinement. Molecular docking with TLR3 and TLR4 receptors showed good binding, indicating high immune reactions. Immune response simulation confirmed the generation of innate and adaptive responses. In-silico cloning revealed the vaccine is highly expressed in E. coli. The results can be further experimentally verified using various analyses to establish a candidate vaccine for future clinical trials.

A novel chimeric vaccine containing multiple epitopes for simulating robust immune activation against Klebsiella pneumoniae.

BACKGROUND: Due to antibiotic resistance, the Klebsiella genus is linked to morbidity and death, necessitating the development of a universally protective vaccine against Klebsiella pathogens. METHODS: Core sequence analysis prioritized non-redundant host molecules and expected lipid bilayer peptides from fully sequenced Klebsiella genomes. These proteins were refined to identify epitopes, examining their immunogenicity, toxicity, solubility, and interaction with MHC alleles. Epitopes were linked to CPG ODN C274 via EAAAK, HEYGAEALERAG, and GGGS linkers to enhance immunological responses. The vaccine's tertiary structure was modelled and docked with MHC-I and MHC-II. RESULTS: Fifty-five proteins were recognized in the Vaxign collection as having remarkable features. Twenty-three proteins with potential pathogenicity were then identified. Eight options for vaccines emerged after the immunogenicity of proteins was examined. The best antigens were three proteins: MrkD, Iron-regulated lipid membrane polypeptides, and RmpA. These compounds were selected for their sensitivity. The structural protein sequences of K. pneumoniae were utilized to identify seven CTL epitopes, seven HTL epitopes, and seven LBL epitopes, respectively. The produced immunization displayed a stable contact with the receptors, based on molecular dynamic simulations lasting 250 nanoseconds. Intermolecular binding free energies also indicated the dominance of the van der Waals and electrostatic energies. CONCLUSION: In summary, the results of this study might help scientists develop a novel vaccine to prevent K. pneumoniae infections.

Arabidopsis SBT5.2 and SBT1.7 subtilases mediate C-terminal cleavage of flg22 epitope from bacterial flagellin.

Plants initiate specific defense responses by recognizing conserved epitope peptides within the flagellin proteins derived from bacteria. Proteolytic cleavage of epitope peptides from flagellin by plant apoplastic proteases is thought to be crucial for the perception of the epitope by the plant receptor. However, the identity of the plant proteases involved in this process remains unknown. Here, we establish an efficient identification system for the target proteases in Arabidopsis apoplastic fluid; the method employs native two-dimensional electrophoresis followed by an in-gel proteolytic assay using a fluorescence-quenching peptide substrate. We designed a substrate to specifically detect proteolytic activity at the C-terminus of the flg22 epitope in flagellin and identified two plant subtilases, SBT5.2 and SBT1.7, as specific proteases responsible for the C-terminal cleavage of flg22. In the apoplastic fluid of Arabidopsis mutant plants deficient in these two proteases, we observe a decrease in the C-terminal cleavage of the flg22 domain from flagellin, leading to a decrease in the efficiency of flg22 epitope liberation. Consequently, defensive reactive oxygen species (ROS) production is delayed in sbt5.2 sbt1.7 double-mutant leaf disks compared to wild type following flagellin exposure.

Supramolecular Peptide Self-Assemblies Facilitate Oral Immunization.

Oral immunization is a promising strategy for preventing and treating gastrointestinal (GI) infections and diseases, as it allows for direct access to the disease site. To elicit immune responses within the GI tract, however, there are many obstacles that oral vaccines must surmount, including proteolytic degradation and thick mucus barriers. Here, we employed a modular self-assembling peptide nanofiber platform to facilitate oral immunization against both peptide and small molecule epitopes. Synthesizing nanofibers with d-amino acids rendered them resistant to proteases in vitro, whereas l-amino acid nanofibers were rapidly degraded. Additionally, the inclusion of peptide sequences rich in proline, alanine, and serine (PAS), increased nanofiber muco-penetration, and accelerated nanofiber transport through the GI tract. Oral immunization with PASylated nanofibers and mucosal adjuvant generated local and systemic immune responses to a peptide epitope but only for l-amino acid nanofibers. Further, we were able to apply this design to also enable oral immunization against a small molecule epitope and illustrated the therapeutic and prophylactic effectiveness of these immunizations in mouse models of colitis. These findings demonstrate that supramolecular peptide self-assemblies have promise as oral vaccines and immunotherapies.

A general approach for selection of epitope-directed binders to proteins.

Directing antibodies to a particular epitope among many possible on a target protein is a significant challenge. Here, we present a simple and general method for epitope-directed selection (EDS) using a differential phage selection strategy. This involves engineering the protein of interest (POI) with the epitope of interest (EOI) mutated using a systematic bioinformatics algorithm to guide the local design of an EOI decoy variant. Using several alternating rounds of negative selection with the EOI decoy variant followed by positive selection on the wild-type POI, we were able to identify highly specific and potent antibodies to five different EOI antigens that bind and functionally block known sites of proteolysis. Among these, we developed highly specific antibodies that target the proteolytic site on the CUB domain containing protein 1 (CDCP1) to prevent its proteolysis allowing us to study the cellular maturation of this event that triggers malignancy. We generated antibodies that recognize the junction between the pro- and catalytic domains for three different matrix metalloproteases (MMPs), MMP1, MMP3, and MMP9, that selectively block activation of each of these enzymes and impair cell migration. We targeted a proteolytic epitope on the cell surface receptor, EPH Receptor A2 (EphA2), that is known to transform it from a tumor suppressor to an oncoprotein. We believe that the EDS method greatly facilitates the generation of antibodies to specific EOIs on a wide range of proteins and enzymes for broad therapeutic and diagnostic applications.

Neutralization of EG.5, EG.5.1, BA.2.86, and JN.1 by antisera from dimeric receptor-binding domain subunit vaccines and 41 human monoclonal antibodies.

BACKGROUND: The recently circulating Omicron variants BA.2.86 and JN.1 were identified with more than 30 amino acid changes on the spike protein compared to BA.2 or XBB.1.5. This study aimed to comprehensively assess the immune escape potential of BA.2.86, JN.1, EG.5, and EG.5.1. METHODS: We collected human and murine sera to evaluate serological neutralization activities. The participants received three doses of coronavirus disease 2019 (COVID-19) vaccines or a booster dose of the ZF2022-A vaccine (Delta-BA.5 receptor-binding domain [RBD]-heterodimer immunogen) or experienced a breakthrough infection (BTI). The ZF2202-A vaccine is under clinical trial study (ClinicalTrials.gov: NCT05850507). BALB/c mice were vaccinated with a panel of severe acute respiratory syndrome coronavirus 2 RBD-dimer proteins. The antibody evasion properties of these variants were analyzed with 41 representative human monoclonal antibodies targeting the eight RBD epitopes. FINDINGS: We found that BA.2.86 had less neutralization evasion than EG.5 and EG.5.1 in humans. The ZF2202-A booster induced significantly higher neutralizing titers than BTI. Furthermore, BA.2.86 and JN.1 exhibited stronger antibody evasion than EG.5 and EG.5.1 on RBD-4 and RBD-5 epitopes. Compared to BA.2.86, JN.1 further lost the ability to bind to several RBD-1 monoclonal antibodies and displayed further immune escape. CONCLUSIONS: Our data showed that the currently dominating sub-variant, JN.1, showed increased immune evasion compared to BA.2.86 and EG.5.1, which is highly concerning. This study provides a timely risk assessment of the interested sub-variants and the basis for updating COVID-19 vaccines. FUNDING: This work was funded by the National Key R&D Program of China, the National Natural Science Foundation of China, the Beijing Life Science Academy, the Bill & Melinda Gates Foundation, and the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation (CPSF).