Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen.

Over the past two decades, therapeutic antibodies have emerged as a rapidly expanding domain within the field of biologics. In silico tools that can streamline the process of antibody discovery and optimization are critical to support a pipeline that is growing more numerous and complex every year. High-quality structural information remains critical for the antibody optimization process, but antibody-antigen complex structures are often unavailable and in silico antibody docking methods are still unreliable. In this study, DeepAb, a deep learning model for predicting antibody Fv structure directly from sequence, was used in conjunction with single-point experimental deep mutational scanning (DMS) enrichment data to design 200 potentially optimized variants of an anti-hen egg lysozyme (HEL) antibody. We sought to determine whether DeepAb-designed variants containing combinations of beneficial mutations from the DMS exhibit enhanced thermostability and whether this optimization affected their developability profile. The 200 variants were produced through a robust high-throughput method and tested for thermal and colloidal stability (Tonset, Tm, Tagg), affinity (KD) relative to the parental antibody, and for developability parameters (nonspecific binding, aggregation propensity, self-association). Of the designed clones, 91% and 94% exhibited increased thermal and colloidal stability and affinity, respectively. Of these, 10% showed a significantly increased affinity for HEL (5- to 21-fold increase) and thermostability (>2.5C increase in Tm1), with most clones retaining the favorable developability profile of the parental antibody. Additional in silico tests suggest that these methods would enrich for binding affinity even without first collecting experimental DMS measurements. These data open the possibility of in silico antibody optimization without the need to predict the antibody-antigen interface, which is notoriously difficult in the absence of crystal structures.

Engineering high affinity antigen-binders: Beyond conventional antibodies.

For decades, antibodies have remained the archetypal binding proteins that can be rapidly produced with high affinity and specificity against virtually any target. A conventional antibody is still considered the prototype of a binding molecule. It is therefore not surprising that antibodies are routinely used in basic scientific and biomedical research, analytical workflows, molecular diagnostics etc. and represent the fastest growing sector in the field of biotechnology. However, several limitations associated with conventional antibodies, including stringent requirement of animal immunizations, mammalian cells for expression, issues on stability and aggregation, bulkier size and the overall time and cost of production has propelled evolution of concepts along alternative antigen binders. Rapidly evolving protein engineering approaches and high throughput screening platforms have further complemented the development of myriads of classes of non-conventional protein binders including antibody derived as well as non-antibody based molecular scaffolds. These non-canonical binders are finding use across disciplines of which diagnostics and therapeutics are the most noteworthy.

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.

Accurate structure prediction of biomolecular interactions with AlphaFold 3.

The introduction of AlphaFold 21 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design2-6. Here we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues. The new AlphaFold model demonstrates substantially improved accuracy over many previous specialized tools: far greater accuracy for protein-ligand interactions compared with state-of-the-art docking tools, much higher accuracy for protein-nucleic acid interactions compared with nucleic-acid-specific predictors and substantially higher antibody-antigen prediction accuracy compared with AlphaFold-Multimer v.2.37,8. Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework.

Self-assembled nanonization of fatty acid-conjugated vaccine antigen for enhanced thermal stability.

The aim of this study was to evaluate the enhanced thermal stability and physicochemical properties of fattigated vaccine antigens. High molecular weight influenza hemagglutinin (Heg) was used as a model antigen because of low heat stability requiring cold chamber. Heg was conjugated with long-chain oleic acid (C18) and short-chain 3-decenoic acid (C10) to prepare fattigated Heg. Circular dichroism analysis revealed no significant changes in the three-dimensional structure post-conjugation. In the liquid state, the fattigated Heg was self-assembled into nanoparticles (NPs) due to its amphiphilic nature, with sizes of 136.27 ± 12.78 nm for oleic acid-conjugated Heg (HOC) and 88.73 ± 3.27 nm for 3-decenoic acid-conjugated Heg (HDC). Accelerated thermal stability studies at 60 °C for 7 days demonstrated that fattigated Heg exhibited higher thermal stability than Heg in various liquid or solid states. The longer-chained HOC showed better thermal stability than HDC in the liquid state, attributed to increased hydrophobic interactions during self-assembly. In bio-mimicking liquid states at 37 °C, HOC exhibited higher thermal stability than Heg. Furthermore, solid-state HOC with cryoprotectants (trehalose, mannitol, and Tween® 80) had significantly increased thermal stability due to reduced exposure of protein surface area via nanonization behavior. The current fattigation platform could be a promising strategy for developing thermostable nano vaccines of heat-labile vaccine antigens.

Planting a chemical flag on antigens.

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

Nanocarriers of antigen proteins for vaccine delivery.

Nanoformulations in vaccinology provide antigen stability and enhanced immunogenicity, in addition to providing targeted delivery and controlled release. In the last years, much research has been focused on vaccine development using virus-like particles, liposomes, emulsions, polymeric, lipid, and inorganic nanoparticles. Importantly, nanoparticle interactions with innate and adaptive immune systems must be clearly understood to guide the rational development of nanovaccines. This review provides a recap and updates on different aspects advocating nanoparticles as promising antigen carriers and immune cell activators for vaccination. Moreover, it offers a discussion of how the physicochemical properties of nanoparticles are modified to target specific cells and improve vaccine efficacy.

The mechanism of epigallocatechin-3-gallate inhibiting the antigenicity of ß-lactoglobulin under pH 6.2, 7.4 and 8.2: Multi-spectroscopy and molecular simulation methods.

The antigenicity of ß-lactoglobulin (ß-LG) can be influenced by pH values and reduced by epigallocatechin-3-gallate (EGCG). However, a detailed mechanism concerning EGCG decreasing the antigenicity of ß-LG at different pH levels lacks clarity. Here, we explore the inhibition mechanism of EGCG on the antigenicity of ß-LG at pH 6.2, 7.4 and 8.2 using enzyme-linked immunosorbent assay, multi-spectroscopy, mass spectrometry and molecular simulations. The results of Fourier transform infrared spectroscopy (FTIR) and circular dichroism (CD) elucidate that the noncovalent binding of EGCG with ß-LG induces variations in the secondary structure and conformations of ß-LG. Moreover, EGCG inhibits the antigenicity of ß-LG the most at pH 7.4 (98.30 %), followed by pH 6.2 (73.18 %) and pH 8.2 (36.24 %). The inhibitory difference is attributed to the disparity in the number of epitopes involved in the interacting regions of EGCG and ß-LG. Our findings suggest that manipulating pH conditions may enhance the effectiveness of antigenic inhibitors, with the potential for further application in the food industry.

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.

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/).

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.

[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.

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.

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.

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.

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.