Planting a chemical flag on antigens.

Next-generation live vaccines are created by autonomous production of nitrated antigens.

Preparation of glycopeptide-modified pH-sensitive liposomes for promoting antigen cross-presentation and induction of antigen-specific cellular immunity.

Cross-presentation, exogenous antigen presentation onto major histocompatibility complex class I molecules on antigen presenting cells, is crucially important for inducing antigen-specific cellular immune responses for cancer immunotherapy and for the treatment of infectious diseases. One strategy to induce cross-presentation is cytosolic delivery of an exogenous antigen using fusogenic or endosomolytic molecule-introduced nanocarriers. Earlier, we reported liposomes modified with pH-responsive polymers to achieve cytosolic delivery of an antigen. Polyglycidol-based or polysaccharide-based pH-responsive polymers can provide liposomes with delivery performance of antigenic proteins into cytosol via membrane fusion with endosomes responding to acidic pH, leading to induction of cross-presentation. Mannose residue was introduced to pH-responsive polysaccharides to increase uptake selectivity to antigen presenting cells and to improve cross-presentation efficiency. However, direct introduction of mannose residue into pH-responsive polysaccharides suppressed cytoplasmic delivery performance of liposomes. To avoid such interference, for this study, mannose-containing glycans were incorporated separately into pH-responsive polysaccharide-modified liposomes. Soybean agglutinin-derived glycopeptide was used as a ligand for lectins on antigen presenting cells. Incorporation of glycopeptide significantly increased the cellular uptake of liposomes by dendritic cell lines and increased cross-presentation efficiency. Liposomes incorporated both glycopeptide and pH-responsive polysaccharides exhibited strong adjuvant effects in vitro and induced the increase of dendritic cells, M1 macrophages, and effector T cells in the spleen. Subcutaneous administration of these liposomes induced antigen-specific cellular immunity, resulting in strong therapeutic effects in tumor-bearing mice. These results suggest that separate incorporation of glycopeptides and pH-responsive polysaccharides into antigen-loaded liposomes is an effective strategy to produce liposome-based nanovaccines to achieve antigen cross-presentation and induction of cellular immunity towards cancer immunotherapy.

Supervised and unsupervised machine learning approaches for monitoring subvisible particles within an aluminum-salt adjuvanted vaccine formulation.

Suspensions of protein antigens adsorbed to aluminum-salt adjuvants are used in many vaccines and require mixing during vial filling operations to prevent sedimentation. However, the mixing of vaccine formulations may generate undesirable particles that are difficult to detect against the background of suspended adjuvant particles. We simulated the mixing of a suspension containing a protein antigen adsorbed to an aluminum-salt adjuvant using a recirculating peristaltic pump and used flow imaging microscopy to record images of particles within the pumped suspensions. Supervised convolutional neural networks (CNNs) were used to analyze the images and create "fingerprints" of particle morphology distributions, allowing detection of new particles generated during pumping. These results were compared to those obtained from an unsupervised machine learning algorithm relying on variational autoencoders (VAEs) that were also used to detect new particles generated during pumping. Analyses of images conducted by applying both supervised CNNs and VAEs found that rates of generation of new particles were higher in aluminum-salt adjuvant suspensions containing protein antigen than placebo suspensions containing only adjuvant. Finally, front-face fluorescence measurements of the vaccine suspensions indicated changes in solvent exposure of tryptophan residues in the protein that occurred concomitantly with new particle generation during pumping.

pH and ROS Responsiveness of Polymersome Nanovaccines for Antigen and Adjuvant Codelivery: An In Vitro and In Vivo Comparison.

The antitumor immunity can be enhanced through the synchronized codelivery of antigens and immunostimulatory adjuvants to antigen-presenting cells, particularly dendritic cells (DCs), using nanovaccines (NVs). To study the influence of intracellular vaccine cargo release kinetics on the T cell activating capacities of DCs, we compared stimuli-responsive to nonresponsive polymersome NVs. To do so, we employed "AND gate" multiresponsive (MR) amphiphilic block copolymers that decompose only in response to the combination of chemical cues present in the environment of the intracellular compartments in antigen cross-presenting DCs: low pH and high reactive oxygen species (ROS) levels. After being unmasked by ROS, pH-responsive side chains are exposed and can undergo a charge shift within a relevant pH window of the intracellular compartments in antigen cross-presenting DCs. NVs containing the model antigen Ovalbumin (OVA) and the iNKT cell activating adjuvant α-Galactosylceramide (α-Galcer) were fabricated using microfluidics self-assembly. The MR NVs outperformed the nonresponsive NV in vitro, inducing enhanced classical- and cross-presentation of the OVA by DCs, effectively activating CD8+, CD4+ T cells, and iNKT cells. Interestingly, in vivo, the nonresponsive NVs outperformed the responsive vaccines. These differences in polymersome vaccine performance are likely linked to the kinetics of cargo release, highlighting the crucial chemical requirements for successful cancer nanovaccines.

Minimalist Nanovaccine with Optimized Amphiphilic Copolymers for Cancer Immunotherapy.

Cancer vaccines with the ability to elicit tumor-specific immune responses have attracted significant interest in cancer immunotherapy. A key challenge for effective cancer vaccines is the spatiotemporal codelivery of antigens and adjuvants. Herein, we synthesized a copolymer library containing nine poly(ethylene glycol) methyl ether methacrylate-co-butyl methacrylate-co-2-(azepan-1-yl)ethyl methacrylate (PEGMA-co-BMA-co-C7AMA) graft copolymers with designed proportions of different components to regulate their properties. Among these polymers, C-25, with a C7AMA:BMA ratio at 1.5:1 and PEG wt % of 25%, was screened as the most effective nanovaccine carrier with enhanced ability to induce mouse bone marrow-derived dendritic cell (BMDC) maturation. Additionally, RNA-sequencing (RNA-Seq) analysis revealed that C-25 could activate dendritic cells (DCs) through multisignaling pathways to trigger potent immune effects. Then, the screened C-25 was used to encapsulate the model peptide antigen, OVA257-280, to form nanovaccine C-25/OVA257-280. It was found that the C-25/OVA257-280 nanovaccine could effectively facilitate DC maturation and antigen cross-presentation without any other additional adjuvant and exhibited excellent prophylactic efficacy in the B16F10-OVA tumor model. Moreover, in combination with antiprogrammed cell death protein-ligand 1 (anti-PD-L1), the C-25/OVA257-280 nanovaccine could significantly delay the growth of pre-existing tumors. Therefore, this work developed a minimalist nanovaccine with a simple formulation and high efficiency in activating tumor-specific immune responses, showing great potential for further application in cancer immunotherapy.

A method for predicting linear and conformational B-cell epitopes in an antigen from its primary sequence.

B-cell is an essential component of the immune system that plays a vital role in providing the immune response against any pathogenic infection by producing antibodies. Existing methods either predict linear or conformational B-cell epitopes in an antigen. In this study, a single method was developed for predicting both types (linear/conformational) of B-cell epitopes. The dataset used in this study contains 3875 B-cell epitopes and 3996 non-B-cell epitopes, where B-cell epitopes consist of both linear and conformational B-cell epitopes. Our primary analysis indicates that certain residues (like Asp, Glu, Lys, and Asn) are more prominent in B-cell epitopes. We developed machine-learning based methods using different types of sequence composition and achieved the highest AUROC of 0.80 using dipeptide composition. In addition, models were developed on selected features, but no further improvement was observed. Our similarity-based method implemented using BLAST shows a high probability of correct prediction with poor sensitivity. Finally, we developed a hybrid model that combines alignment-free (dipeptide based random forest model) and alignment-based (BLAST-based similarity) models. Our hybrid model attained a maximum AUROC of 0.83 with an MCC of 0.49 on the independent dataset. Our hybrid model performs better than existing methods on an independent dataset used in this study. All models were trained and tested on 80 % of the data using a cross-validation technique, and the final model was evaluated on 20 % of the data, called an independent or validation dataset. A webserver and standalone package named "CLBTope" has been developed for predicting, designing, and scanning B-cell epitopes in an antigen sequence available at (https://webs.iiitd.edu.in/raghava/clbtope/).

[Synthesis of carbendazim artificial antigens and preparation of murine polyclonal antibodies].

Objective To synthesize carbendazim artificial antigens, prepare carbendazim polyclonal antibodies and identify their characteristics. Methods Active carboxyl groups were introduced to prepare the carbendazim haptens by the mixed anhydride method. The artificial antigens and coating antigens were obtained by coupling the small molecule haptens with carriers of bovine serum albumin (BSA) and ovalbumin (OVA). Sodium dodecyl sulfate polycrylamide gel electropheresis (SDS-PAGE) was used to identify carbendazim artificial antigens. Mice were immunized with the prepared artificial antigens to obtain polyclonal antibodies against carbendazim, and the antibody titers and specificity were identified by indirect ELISA. Results Carbendazim artificial antigens were successfully prepared. The titer of polyclonal antibody was above 1:12 800 and the half-maximal inhibitory concentration ( IC50) of the antibody was 0.107 µg/mL. The cross-reactivity rates with both benomyl and thiabendazole were less than 1%. Conclusion Polyclonal antibodies with high sensitivity and high specificity were successfully prepared, laying the foundation for the establishment of a rapid detection method for carbendazim residues.

Engineered antigen-binding fragments for enhanced crystallization of antibody:antigen complexes.

The atomic-resolution structural information that X-ray crystallography can provide on the binding interface between a Fab and its cognate antigen is highly valuable for understanding the mechanism of interaction. However, many Fab:antigen complexes are recalcitrant to crystallization, making the endeavor a considerable effort with no guarantee of success. Consequently, there have been significant steps taken to increase the likelihood of Fab:antigen complex crystallization by altering the Fab framework. In this investigation, we applied the surface entropy reduction strategy coupled with phage-display technology to identify a set of surface substitutions that improve the propensity of a human Fab framework to crystallize. In addition, we showed that combining these surface substitutions with previously reported Crystal Kappa and elbow substitutions results in an extraordinary improvement in Fab and Fab:antigen complex crystallizability, revealing a strong synergistic relationship between these sets of substitutions. Through comprehensive Fab and Fab:antigen complex crystallization screenings followed by structure determination and analysis, we defined the roles that each of these substitutions play in facilitating crystallization and how they complement each other in the process.

Deciphering the antigen specificities of antibodies by clustering their complementarity determining region sequences.

IMPORTANCE: Determining antigen and epitope specificity is an essential step in the discovery of therapeutic antibodies as well as in the analysis adaptive immune responses to disease or vaccination. Despite extensive efforts, deciphering antigen specificity solely from BCR amino acid sequence remains a challenging task, requiring a combination of experimental and computational approaches. Here, we describe and experimentally validate a simple and straightforward approach for grouping antibodies that share antigen and epitope specificities based on their CDR sequence similarity. This approach allows us to identify the specificities of a large number of antibodies whose antigen targets are unknown, using a small fraction of antibodies with well-annotated binding specificities.

Polymer-Protein Nanovaccine Synthesized via Reactive Self-Assembly with Potential Application in Cancer Immunotherapy: Physicochemical and Biological Characterization In Vitro and In Vivo.

Nanovaccines composed of polymeric nanocarriers and protein-based antigens have attracted much attention in recent years because of their enormous potential in the prevention and treatment of diseases such as viral infections and cancer. While surface-conjugated protein antigens are known to be more immunoactive than encapsulated antigens, current surface conjugation methods often result in low and insufficient protein loading. Herein, reactive self-assembly is used to prepare nanovaccine from poly(ε-caprolactone) (PCL) and ovalbumin (OVA)-a model antigen. A rapid thiol-disulfide exchange reaction between PCL with pendant pyridyl disulfide groups and thiolated OVA results in the formation of nanoparticles with narrow size distribution. High OVA loading (≈70-80 wt%) is achieved, and the native secondary structure of OVA is preserved. Compared to free OVA, the nanovaccine is much superior in enhancing antigen uptake by bone marrow-derived dendritic cells (BMDCs), promoting BMDC maturation and antigen presentation via the MHC I pathway, persisting at the injection site and draining lymph nodes, activating both Th1 and Th2 T cell immunity, and ultimately, resisting tumor challenge in mice. This is the first demonstration of reactive self-assembly for the construction of a polymer-protein nanovaccine with clear potential in advancing cancer immunotherapy.

Arginine cluster introduction on framework region in anti-lysozyme antibody improved association rate constant by changing conformational diversity of CDR loops.

Antibodies are used for many therapeutic and biotechnological purposes. Because the affinity of an antibody to the antigen is critical for clinical efficacy of pharmaceuticals, many affinity maturation strategies have been developed. Although we previously reported an affinity maturation strategy in which the association rate of the antibody toward its antigen is improved by introducing a cluster of arginine residues into the framework region of the antibody, the detailed molecular mechanism responsible for this improvement has been unknown. In this study, we introduced five arginine residues into an anti-hen egg white lysozyme antibody (HyHEL10) Fab fragment to create the R5-mutant and comprehensively characterized the interaction between antibody and antigen using thermodynamic analysis, X-ray crystallography, and molecular dynamics (MD) simulations. Our results indicate that introduction of charged residues strongly enhanced the association rate, as previously reported, and the antibody-antigen complex structure was almost the same for the R5-mutant and wild-type Fabs. The MD simulations indicate that the mutation increased conformational diversity in complementarity-determining region loops and thereby enhanced the association rate. These observations provide the molecular basis of affinity maturation by R5 mutation.

Identification of a common epitope in knottins and phospholipases D present in Loxosceles sp venom by a monoclonal antibody.

In the Americas and specially in Brazil, the Loxosceles intermedia, Loxosceles gaucho and Loxosceles laeta are the three most medically relevant brown spider species, and whose bites can lead to the condition known as loxoscelism. Here, we report the development of a tool capable of identifying a common epitope amongst Loxosceles sp. venom's toxins. A murine monoclonal antibody (LmAb12) and its recombinant fragments (scFv12P and diabody12P) have been produced and characterized. This antibody and its recombinant constructs were able to recognize proteins of Loxosceles spider venoms with specificity. The scFv12P variant was also able to detect low concentrations of Loxosceles venom in a competitive ELISA assay, displaying potential as a venom identification tool. The primary antigenic target of LmAb12 is a knottin, a venom neurotoxin, that has a shared identity of 100 % between the L. intermedia and L. gaucho species and high similarity to L. laeta. Furthermore, we observed LmAb12 was able to partially inhibit in vitro hemolysis, a cellular event typically induced by the Loxosceles sp. venoms. Such behavior might be due to LmAb12 cross-reactivity between the antigenic target of LmAb12 and the venom's dermonecrotic toxins, the PLDs, or even the existence of synergism between these two toxins.

One-pot preparation of mannan-coated antigen nanoparticles using human serum albumin as a matrix for tolerance induction.

Nanoparticles (NPs) for allergen immunotherapy have garnered attention for their high efficiency and safety compared with naked antigen proteins. In this work, we present mannan-coated protein NPs, incorporating antigen proteins for antigen-specific tolerance induction. The heat-induced formation of protein NPs is a one-pot preparation method and can be applied to various proteins. Here, the NPs were formed spontaneously via heat denaturation of three component proteins: an antigen protein, human serum albumin (HSA) as a matrix protein, and mannoprotein (MAN) as a targeting ligand for dendritic cells (DCs). HSA is non-immunogenic, therefore suitable as a matrix protein, while MAN coats the surface of the NP. We applied this method to various antigen proteins and found that the self-disperse after heat denaturation was a requirement for incorporation into the NPs. We also established that the NPs could target DCs, and the incorporation of rapamycin into the NPs enhanced the induction of a tolerogenic phenotype of DC. The MAN coating provided steric hindrance and heat denaturation destroyed recognition structures, successfully preventing anti-antigen antibody binding, indicating the NPs may avoid anaphylaxis induction. The MAN-coated NPs proposed here, prepared by a simple method, have the potential for effective and safe allergies treatment for various antigens.

A Proximity-Induced Fluorogenic Reaction Triggered by Antibody-Antigen Interactions with Adjacent Epitopes.

Proximity-induced chemical reactions are site-specific and rapid by taking advantage of their high affinity and highly selective interactions with the template. However, reactions induced solely by antibody-antigen interactions have not been developed. Herein, we propose a biepitopic antigen-templated chemical reaction (BATER) as a novel template reaction. In BATER, reactive functional groups are conjugated to two antibodies that interact with two epitopes of the same antigen to accelerate the reaction. We developed a method for visualizing the progress of BATER using fluorogenic click chemistry for optimal antibody selection and linker design. The reaction is accelerated in the presence of a specific antigen in a linker length-dependent manner. The choice of the antibody epitope is important for a rapid reaction. This design will lead to various applications of BATER in living systems.

Analysis of the Adsorbed Vaccine Formulations Using Water Proton Nuclear Magnetic Resonance-Comparison with Optical Analytics.

PURPOSE: To evaluate wNMR, an emerging noninvasive analytical technology, for characterizing aluminum-adjuvanted vaccine formulations. METHODS: wNMR stands for water proton nuclear magnetic resonance. In this work, wNMR and optical techniques (laser diffraction and laser scattering) were used to characterize vaccine formulations containing different antigen loads adsorbed onto AlPO4 adjuvant microparticles, including the fully dispersed state and the sedimentation process. All wNMR measurements were done noninvasively on sealed vials containing the adsorbed vaccine suspensions, while the optical techniques require transferring the adsorbed vaccine suspensions out of the original vial into specialized cuvette/tube for analysis. For analyzing fully dispersed suspensions, optical techniques also require sample dilution. RESULTS: wNMR outperformed laser diffraction in differentiating high- and low-dose formulations of the same vaccine, while wNMR and laser scattering achieved comparable results on vaccine sedimentation kinetics and the compactness of fully settled vaccines. CONCLUSION: wNMR could be used to analyze aluminum-adjuvanted formulations and to differentiate between formulations containing different antigen loads adsorbed onto aluminum adjuvant microparticles. The results demonstrate the capability of wNMR to characterize antigen-adjuvant complexes and to noninvasively inspect finished vaccine products.

SEPPA-mAb: spatial epitope prediction of protein antigens for mAbs.

Identifying the exact epitope positions for a monoclonal antibody (mAb) is of critical importance yet highly challenging to the Ab design of biomedical research. Based on previous versions of SEPPA 3.0, we present SEPPA-mAb for the above purpose with high accuracy and low false positive rate (FPR), suitable for both experimental and modelled structures. In practice, SEPPA-mAb appended a fingerprints-based patch model to SEPPA 3.0, considering the structural and physic-chemical complementarity between a possible epitope patch and the complementarity-determining region of mAb and trained on 860 representative antigen-antibody complexes. On independent testing of 193 antigen-antibody pairs, SEPPA-mAb achieved an accuracy of 0.873 with an FPR of 0.097 in classifying epitope and non-epitope residues under the default threshold, while docking-based methods gave the best AUC of 0.691, and the top epitope prediction tool gave AUC of 0.730 with balanced accuracy of 0.635. A study on 36 independent HIV glycoproteins displayed a high accuracy of 0.918 and a low FPR of 0.058. Further testing illustrated outstanding robustness on new antigens and modelled antibodies. Being the first online tool predicting mAb-specific epitopes, SEPPA-mAb may help to discover new epitopes and design better mAbs for therapeutic and diagnostic purposes. SEPPA-mAb can be accessed at http://www.badd-cao.net/seppa-mab/.

epiTCR: a highly sensitive predictor for TCR-peptide binding.

MOTIVATION: Predicting the binding between T-cell receptor (TCR) and peptide presented by human leucocyte antigen molecule is a highly challenging task and a key bottleneck in the development of immunotherapy. Existing prediction tools, despite exhibiting good performance on the datasets they were built with, suffer from low true positive rates when used to predict epitopes capable of eliciting T-cell responses in patients. Therefore, an improved tool for TCR-peptide prediction built upon a large dataset combining existing publicly available data is still needed. RESULTS: We collected data from five public databases (IEDB, TBAdb, VDJdb, McPAS-TCR, and 10X) to form a dataset of >3 million TCR-peptide pairs, 3.27% of which were binding interactions. We proposed epiTCR, a Random Forest-based method dedicated to predicting the TCR-peptide interactions. epiTCR used simple input of TCR CDR3ß sequences and antigen sequences, which are encoded by flattened BLOSUM62. epiTCR performed with area under the curve (0.98) and higher sensitivity (0.94) than other existing tools (NetTCR, Imrex, ATM-TCR, and pMTnet), while maintaining comparable prediction specificity (0.9). We identified seven epitopes that contributed to 98.67% of false positives predicted by epiTCR and exerted similar effects on other tools. We also demonstrated a considerable influence of peptide sequences on prediction, highlighting the need for more diverse peptides in a more balanced dataset. In conclusion, epiTCR is among the most well-performing tools, thanks to the use of combined data from public sources and its use will contribute to the quest in identifying neoantigens for precision cancer immunotherapy. AVAILABILITY AND IMPLEMENTATION: epiTCR is available on GitHub (https://github.com/ddiem-ri-4D/epiTCR).

Vaccination of TLR7/8 Agonist-Conjugated Antigen Nanoparticles for Cancer Immunotherapy.

Nanovaccine-based immunotherapy can initiate strong immune responses and establish a long-term immune memory to prevent tumor invasion and recurrence. Herein, the assembly of redox-responsive antigen nanoparticles (NPs) conjugated with imidazoquinoline-based TLR7/8 agonists for lymph node-targeted immune activation is reported, which can potentiate tumor therapy and prevention. Antigen NPs are assembled via the templating of zeolitic imidazolate framework-8 NPs to cross-link ovalbumin with disulfide bonds, which enables the NPs with redox-responsiveness for improved antigen cross-presentation and dendritic cell activation. The formulated nanovaccines promote the lymphatic co-delivery of antigens and agonists, which can trigger immune responses of cytotoxic T lymphocytes and strong immunological memory. Notably, nanovaccines demonstrate their superiority for tumor prevention owing to the elicited robust antitumor immunity. The reported strategy provides a rational design of nanovaccines for enhanced cancer immunotherapy.

Comparison of Synthetic Methods and Identification of Several Artificial Antigens of Deoxynivalenol.

The purpose of this experiment was to study the design and modification of hapten molecules and artificial antigen molecules of deoxynivalenol (DON), and to compare the preparation and identification methods of four artificial antigens. According to the characteristics of the molecular structure of DON, four artificial antigen coupling methods were designed-namely, N,N'-carbonyldiimidazole (CDI), 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride (EDC), isobutyl chloroformate (IBCF), and N-hydroxysuccinimide (NHS)-to prepare artificial antigens and detection antigens. Through ultraviolet (UV), infrared (IR), and SDS-polyacrylamide gel electrophoresis (SDS-PAGE), along with other physical and chemical identification methods and animal immunisation, the best artificial antigen coupling method was screened. The results showed that the CDI method achieved the best effect among the synthesis methods. The titre of anti-DON polyclonal antibody (pAb) produced by animal immunisation reached 1: (6.4 × 103). The half inhibitory concentration (IC50) was 47.75 ng/mL, the cross-reaction rate with 3-acetyldeoxynivalenol (3-AcDON) was slightly higher at 35.3%, and there was no cross-reaction with other compounds; therefore, four artificial antigens were successfully prepared by using the molecular structure of DON. Through identification, the CDI method was screened as the best artificial antigen synthesis method, with the highest DON pAb titre, the best sensitivity, and the strongest specificity. This will lay a solid antigenic foundation for the preparation of better anti-DON monoclonal antibodies (mAbs) in the future.

LBCE-XGB: A XGBoost Model for Predicting Linear B-Cell Epitopes Based on BERT Embeddings.

Accurately detecting linear B-cell epitopes (BCEs) makes great sense in vaccine design, immunodiagnostic test, antibody production, disease prevention and treatment. Wet-lab experiments for determining linear BCEs are both expensive and laborious, which are not able to meet the recognition needs of modern massive protein sequence data. Instead, computational methods can efficiently identify linear BCEs with low cost. Although several computational methods are available, the performance is still not satisfactory. Thus, we propose a new method, LBCE-XGB, to forecast linear BCEs based on XGBoost algorithm. To represent the biological information concealed in peptide sequences, the embeddings of the residues were obtained from a pre-trained domain-specific BERT model. In addition, the other five types of attributes comprising amino acid composition, amino acid antigenicity scale were also extracted. The best feature combination was determined according to the cross-validation results. Against the models developed by other deep learning and machine learning algorithms, LBCE-XGB achieves the top performance with an AUROC of 0.845 for fivefold cross-validation. The results on the independent test set show that our model attains an AUROC of 0.838 which is substantially higher than other state-of-the-art methods. The outcomes indicate that the representations of BERT could be an effective feature in predicting linear BCEs and we believe that LBCE-XGB could be a useful medium for detecting linear B cell epitopes with high accuracy and low cost.