TULIP: A transformer-based unsupervised language model for interacting peptides and T cell receptors that generalizes to unseen epitopes.

The accurate prediction of binding between T cell receptors (TCR) and their cognate epitopes is key to understanding the adaptive immune response and developing immunotherapies. Current methods face two significant limitations: the shortage of comprehensive high-quality data and the bias introduced by the selection of the negative training data commonly used in the supervised learning approaches. We propose a method, Transformer-based Unsupervised Language model for Interacting Peptides and T cell receptors (TULIP), that addresses both limitations by leveraging incomplete data and unsupervised learning and using the transformer architecture of language models. Our model is flexible and integrates all possible data sources, regardless of their quality or completeness. We demonstrate the existence of a bias introduced by the sampling procedure used in previous supervised approaches, emphasizing the need for an unsupervised approach. TULIP recognizes the specific TCRs binding an epitope, performing well on unseen epitopes. Our model outperforms state-of-the-art models and offers a promising direction for the development of more accurate TCR epitope recognition models.

Development of novel monoclonal antibodies for blocking NF-κB activation induced by CD2v protein in African swine fever virus.

Background: CD2v, a critical outer envelope glycoprotein of the African swine fever virus (ASFV), plays a central role in the hemadsorption phenomenon during ASFV infection and is recognized as an essential immunoprotective protein. Monoclonal antibodies (mAbs) targeting CD2v have demonstrated promise in both diagnosing and combating African swine fever (ASF). The objective of this study was to develop specific monoclonal antibodies against CD2v. Methods: In this investigation, Recombinant CD2v was expressed in eukaryotic cells, and murine mAbs were generated through meticulous screening and hybridoma cloning. Various techniques, including indirect enzyme-linked immunosorbent assay (ELISA), western blotting, immunofluorescence assay (IFA), and bio-layer interferometry (BLI), were employed to characterize the mAbs. Epitope mapping was conducted using truncation mutants and epitope peptide mapping. Results: An optimal antibody pair for a highly sensitive sandwich ELISA was identified, and the antigenic structures recognized by the mAbs were elucidated. Two linear epitopes highly conserved in ASFV genotype II strains, particularly in Chinese endemic strains, were identified, along with a unique glycosylated epitope. Three mAbs, 2B25, 3G25, and 8G1, effectively blocked CD2v-induced NF-κB activation. Conclusions: This study provides valuable insights into the antigenic structure of ASFV CD2v. The mAbs obtained in this study hold great potential for use in the development of ASF diagnostic strategies, and the identified epitopes may contribute to vaccine development against ASFV.

Structural and functional characterization of nanobodies that neutralize Omicron variants of SARS-CoV-2.

The Omicron strains of SARS-CoV-2 pose a significant challenge to the development of effective antibody-based treatments as immune evasion has compromised most available immune therapeutics. Therefore, in the 'arms race' with the virus, there is a continuing need to identify new biologics for the prevention or treatment of SARS-CoV-2 infections. Here, we report the isolation of nanobodies that bind to the Omicron BA.1 spike protein by screening nanobody phage display libraries previously generated from llamas immunized with either the Wuhan or Beta spike proteins. The structure and binding properties of three of these nanobodies (A8, H6 and B5-5) have been characterized in detail providing insight into their binding epitopes on the Omicron spike protein. Trimeric versions of H6 and B5-5 neutralized the SARS-CoV-2 variant of concern BA.5 both in vitro and in the hamster model of COVID-19 following nasal administration. Thus, either alone or in combination could serve as starting points for the development of new anti-viral immunotherapeutics.

Bispecific antibodies targeting two glycoproteins on SFTSV exhibit synergistic neutralization and protection in a mouse model.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease with a high fatality rate of up to 30% caused by SFTS virus (SFTSV). However, no specific vaccine or antiviral therapy has been approved for clinical use. To develop an effective treatment, we isolated a panel of human monoclonal antibodies (mAbs). SF5 and SF83 are two neutralizing mAbs that recognize two viral glycoproteins (Gn and Gc), respectively. We found that their epitopes are closely located, and we then engineered them as several bispecific antibodies (bsAbs). Neutralization and animal experiments indicated that bsAbs display more potent protective effects than the parental mAbs, and the cryoelectron microscopy structure of a bsAb3 Fab-Gn-Gc complex elucidated the mechanism of protection. In vivo virus passage in the presence of antibodies indicated that two bsAbs resulted in less selective pressure and could efficiently bind to all single parental mAb-escape mutants. Furthermore, epitope analysis of the protective mAbs against SFTSV and RVFV indicated that they are all located on the Gn subdomain I, where may be the hot spots in the phleboviruses. Collectively, these data provide potential therapeutic agents and molecular basis for the rational design of vaccines against SFTSV infection.

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.

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 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.

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.

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.

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.

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.

Bilateral efforts to improve SERS detection efficiency of exosomes by Au/Na7PMo11O39 Combined with Phospholipid Epitope Imprinting.

Detection of cancer-related exosomes in body fluids has become a revolutionary strategy for early cancer diagnosis and prognosis prediction. We have developed a two-step targeting detection method, termed PS-MIPs-NELISA SERS, for rapid and highly sensitive exosomes detection. In the first step, a phospholipid polar site imprinting strategy was employed using magnetic PS-MIPs (phospholipids-molecularly imprinted polymers) to selectively isolate and enrich all exosomes from urine samples. In the second step, a nanozyme-linked immunosorbent assay (NELISA) technique was utilized. We constructed Au/Na7PMo11O39 nanoparticles (NPs) with both surface-enhanced Raman scattering (SERS) property and peroxidase catalytic activity, followed by the immobilization of CD9 antibodies on the surface of Au/Na7PMo11O39 NPs. The Au/Na7PMo11O39-CD9 antibody complexes were then used to recognize CD9 proteins on the surface of exosomes enriched by magnetic PS-MIPs. Lastly, the high sensitivity detection of exosomes was achieved indirectly via the SERS activity and peroxidase-like activity of Au/Na7PMo11O39 NPs. The quantity of exosomes in urine samples from pancreatic cancer patients obtained by the PS-MIPs-NELISA SERS technique showed a linear relationship with the SERS intensity in the range of 6.21 × 107-2.81 × 108 particles/mL, with a limit of detection (LOD) of 5.82 × 107 particles/mL. The SERS signal intensity of exosomes in urine samples from pancreatic cancer patients was higher than that of healthy volunteers. This bidirectional MIPs-NELISA-SERS approach enables noninvasive, highly sensitive, and rapid detection of cancer, facilitating the monitoring of disease progression during treatment and opening up a new avenue for rapid early cancer screening.

Short-time ozone treatment promotes protease-mediated destruction of B cell allergen epitopes by altering the structural characteristics of whey protein.

A novel processing method combining short-time ozone pretreatment with hydrolysis has been developed to reduce whey protein allergenicity. The results showed that ozone treatment altered the whey protein spatial structure, initially increasing the surface hydrophobicity index, and then decreasing due to polymer formation as the time increased. Under the optimized conditions of alkaline protease-mediated hydrolysis, a 10-second pre-exposure to ozone significantly promoted the reduction in the IgE binding capacity of whey protein without compromising the hydrolysis efficiency. Compared with whey protein, the degranulation of KU812 cells stimulated by this hydrolysate decreased by 20.54%, 17.99%, and 22.80% for IL-6, ß-hexosaminidase, and histamine, respectively. In vitro simulated gastrointestinal digestion confirmed increased digestibility and reduced allergenicity. Peptidomics identification revealed that short-time ozonation exposed allergen epitopes, allowing alkaline protease to target these epitopes more effectively, particularly those associated with α-lactalbumin. These findings suggest the promising application of this processing method in mitigating the allergenicity of whey protein.

Enhancing antitumor response by efficiently generating large-scale TCR-T cells targeting a single epitope across multiple cancer antigens.

The need to contrive interventions to curb the rise in cancer incidence and mortality is critical for improving patients' prognoses. Adoptive cell therapy is challenged with quality large-scale production, heightening its production cost. Several cancer types have been associated with the expression of highly-immunogenic CTAG1 and CTAG2 antigens, which share common epitopes. Targeting two antigens on the same cancer could improve the antitumor response of TCR-T cells. In this study, we exploited an efficient way to generate large-fold quality TCR-T cells and also demonstrated that the common epitopes of CTAG1 and CTAG2 antigens provide an avenue for improved cancer-killing via dual-antigen-epitope targeting. Our study revealed that xeno/sera-free medium could expand TCR-T cells to over 500-fold, posing as a better replacement for FBS-supplemented media. Human AB serum was also shown to be a good alternative in the absence of xeno/sera-free media. Furthermore, TCR-T cells stimulated with beads-coated T-activator showed a better effector function than soluble T-activator stimulated TCR-T cells. Additionally, TCR-T cells that target multiple antigens in the same cancer yield better anticancer activity than those targeting a single antigen. This showed that targeting multiple antigens with a common epitope may enhance the antitumor response efficacy of T cell therapies.

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.

Structure-based epitope prediction and assessment of cross-reactivity of Myrmecia pilosula venom-specific IgE and recombinant Sol g proteins (Solenopsis geminata).

The global distribution of tropical fire ants (Solenopsis geminata) raises concerns about anaphylaxis and serious medical issues in numerous countries. This investigation focused on the cross-reactivity of allergen-specific IgE antibodies between S. geminata and Myrmecia pilosula (Jack Jumper ant) venom proteins due to the potential emergence of cross-reactive allergies in the future. Antibody epitope analysis unveiled one predominant conformational epitope on Sol g 1.1 (PI score of 0.989), followed by Sol g 2.2, Sol g 4.1, and Sol g 3.1. Additionally, Pilosulin 1 showed high allergenic potential (PI score of 0.94), with Pilosulin 5a (PI score of 0.797) leading in B-cell epitopes. The sequence analysis indicated that Sol g 2.2 and Sol g 4.1 pose a high risk of cross-reactivity with Pilosulins 4.1a and 5a. Furthermore, the cross-reactivity of recombinant Sol g proteins with M. pilosula-specific IgE antibodies from 41 patients revealed high cross-reactivity for r-Sol g 3.1 (58.53%) and r-Sol g 4.1 (43.90%), followed by r-Sol g 2.2 (26.82%), and r-Sol g 1.1 (9.75%). Therefore, this study demonstrates cross-reactivity (85.36%) between S. geminata and M. pilosula, highlighting the allergenic risk. Understanding these reactions is vital for the prevention of severe allergic reactions, especially in individuals with pre-existing Jumper Jack ant allergy, informing future management strategies.

Assessing nanobody interaction with SARS-CoV-2 Nsp9.

The interaction between SARS-CoV-2 non-structural protein Nsp9 and the nanobody 2NSP90 was investigated by NMR spectroscopy using the paramagnetic perturbation methodology PENELOP (Paramagnetic Equilibrium vs Nonequilibrium magnetization Enhancement or LOss Perturbation). The Nsp9 monomer is an essential component of the replication and transcription complex (RTC) that reproduces the viral gRNA for subsequent propagation. Therefore preventing Nsp9 recruitment in RTC would represent an efficient antiviral strategy that could be applied to different coronaviruses, given the Nsp9 relative invariance. The NMR results were consistent with a previous characterization suggesting a 4:4 Nsp9-to-nanobody stoichiometry with the occurrence of two epitope pairs on each of the Nsp9 units that establish the inter-dimer contacts of Nsp9 tetramer. The oligomerization state of Nsp9 was also analyzed by molecular dynamics simulations and both dimers and tetramers resulted plausible. A different distribution of the mapped epitopes on the tetramer surface with respect to the former 4:4 complex could also be possible, as well as different stoichiometries of the Nsp9-nanobody assemblies such as the 2:2 stoichiometry suggested by the recent crystal structure of the Nsp9 complex with 2NSP23 (PDB ID: 8dqu), a nanobody exhibiting essentially the same affinity as 2NSP90. The experimental NMR evidence, however, ruled out the occurrence in liquid state of the relevant Nsp9 conformational change observed in the same crystal structure.

Animal-derived food allergen: A review on the available crystal structure and new insights into structural epitope.

Immunoglobulin E (IgE)-mediated food allergy is a rapidly growing public health problem. The interaction between allergens and IgE is at the core of the allergic response. One of the best ways to understand this interaction is through structural characterization. This review focuses on animal-derived food allergens, overviews allergen structures determined by X-ray crystallography, presents an update on IgE conformational epitopes, and explores the structural features of these epitopes. The structural determinants of allergenicity and cross-reactivity are also discussed. Animal-derived food allergens are classified into limited protein families according to structural features, with the calcium-binding protein and actin-binding protein families dominating. Progress in epitope characterization has provided useful information on the structural properties of the IgE recognition region. The data reveals that epitopes are located in relatively protruding areas with negative surface electrostatic potential. Ligand binding and disulfide bonds are two intrinsic characteristics that influence protein structure and impact allergenicity. Shared structures, local motifs, and shared epitopes are factors that lead to cross-reactivity. The structural properties of epitope regions and structural determinants of allergenicity and cross-reactivity may provide directions for the prevention, diagnosis, and treatment of food allergies. Experimentally determined structure, especially that of antigen-antibody complexes, remains limited, and the identification of epitopes continues to be a bottleneck in the study of animal-derived food allergens. A combination of traditional immunological techniques and emerging bioinformatics technology will revolutionize how protein interactions are characterized.

Comparative characterization of two monoclonal antibodies targeting canine PD-1.

Monoclonal antibodies targeting immune checkpoints have revolutionized oncology. Yet, the effectiveness of these treatments varies significantly among patients, and they are associated with unexpected adverse events, including hyperprogression. The murine research model used in drug development fails to recapitulate both the functional human immune system and the population heterogeneity. Hence, a novel model is urgently needed to study the consequences of immune checkpoint blockade. Dogs appear to be uniquely suited for this role. Approximately 1 in 4 companion dogs dies from cancer, yet no antibodies are commercially available for use in veterinary oncology. Here we characterize two novel antibodies that bind canine PD-1 with sub-nanomolar affinity as measured by SPR. Both antibodies block the clinically crucial PD-1/PD-L1 interaction in a competitive ELISA assay. Additionally, the antibodies were tested with a broad range of assays including Western Blot, ELISA, flow cytometry, immunofluorescence and immunohistochemistry. The antibodies appear to bind two distinct epitopes as predicted by molecular modeling and peptide phage display. Our study provides new tools for canine oncology research and a potential veterinary therapeutic.