Phage Display Revealed the Complex Structure of the Epitope of the Monoclonal Antibody 10H10.

The annual number of reported human cases of flavivirus infections continues to increase. Measures taken by local healthcare systems and international organizations are not fully successful. In this regard, new approaches to treatment and prevention of flavivirus infections are relevant. One promising approach is to use monoclonal antibody preparations. The mouse mAb 10H10 is capable of interacting with viruses belonging to the genus Orthoflavivirus which are pathogenic to humans. ELISA and molecular modeling data can indicate that mAb 10H10 recognizes the fusion loop region of E protein. The KD of interaction between the mAb 10H10 and recombinant analogs of the E protein of the tick-borne encephalitis (TBEV), Zika (ZIKV) and dengue (DENV) viruses range from 1.5 to 4 nM. The aim of this study was to map the epitope of this antibody using phage display technology. After three rounds of biopanning, 60 individual phage clones were chosen. The amino acid sequences of the selected peptides were conveniently divided into five groups. Based on the selected peptides, bacteriophages were obtained carrying peptides on the surfaces of the pIII and pVIII proteins, which were tested for binding to the antibody in ELISA. Thus, the epitope of the mAb 10H10 is the highly conserved region 98-DRGWGNXXGLFGK-110 of the flavivirus E protein. The structures of the complexes of the identified peptides with the antibody paratope are proposed using the molecular docking and dynamics methods.

Direct Mapping of Polyclonal Epitopes in Serum by HDX-MS.

Elucidating the interactions that drive antigen recognition is central to understanding antibody-mediated protection and is vital for the rational design of immunogens. Often, structural knowledge of epitopes targeted by antibodies is derived from isolated studies of monoclonal antibodies, for which numerous structural techniques exist. In contrast, there are very few approaches capable of mapping the full scope of antigen surfaces targeted by polyclonal sera through the course of a natural antibody response. Here, we develop an approach using immobilized antigen coupled to hydrogen/deuterium exchange with mass spectrometry (HDX-MS) to probe epitope targeting in the context of the fully native serum environment. Using the well-characterized Staphylococcal enterotoxin B (SEB) as a model system, we show that complex combinations of epitopes can be detected and subtle differences across different antisera can be discerned. This work reveals new insight into how neutralizing antibodies and antisera target SEB and, more importantly, establishes a novel method for directly mapping the epitope landscape of polyclonal sera.

Integrating machine learning to advance epitope mapping.

Identifying epitopes, or the segments of a protein that bind to antibodies, is critical for the development of a variety of immunotherapeutics and diagnostics. In vaccine design, the intent is to identify the minimal epitope of an antigen that can elicit an immune response and avoid off-target effects. For prognostics and diagnostics, the epitope-antibody interaction is exploited to measure antigens associated with disease outcomes. Experimental methods such as X-ray crystallography, cryo-electron microscopy, and peptide arrays are used widely to map epitopes but vary in accuracy, throughput, cost, and feasibility. By comparing machine learning epitope mapping tools, we discuss the importance of data selection, feature design, and algorithm choice in determining the specificity and prediction accuracy of an algorithm. This review discusses limitations of current methods and the potential for machine learning to deepen interpretation and increase feasibility of these methods. We also propose how machine learning can be employed to refine epitope prediction to address the apparent promiscuity of polyreactive antibodies and the challenge of defining conformational epitopes. We highlight the impact of machine learning on our current understanding of epitopes and its potential to guide the design of therapeutic interventions with more predictable outcomes.

Computational prediction of multiple antigen epitopes.

MOTIVATION: Identifying antigen epitopes is essential in medical applications, such as immunodiagnostic reagent discovery, vaccine design, and drug development. Computational approaches can complement low-throughput, time-consuming, and costly experimental determination of epitopes. Currently available prediction methods, however, have moderate success predicting epitopes, which limits their applicability. Epitope prediction is further complicated by the fact that multiple epitopes may be located on the same antigen and complete experimental data is often unavailable. RESULTS: Here, we introduce the antigen epitope prediction program ISPIPab that combines information from two feature-based methods and a docking-based method. We demonstrate that ISPIPab outperforms each of its individual classifiers as well as other state-of-the-art methods, including those designed specifically for epitope prediction. By combining the prediction algorithm with hierarchical clustering, we show that we can effectively capture epitopes that align with available experimental data while also revealing additional novel targets for future experimental investigations.

Structure of a Human Monoclonal Antibody in Complex with Outer Surface Protein C of the Lyme Disease Spirochete, Borreliella burgdorferi.

Lyme disease is a tick-borne, multisystem infection caused by the spirochete Borreliella burgdorferi. Although Abs have been implicated in the resolution of Lyme disease, the specific B cell epitopes targeted during human infections remain largely unknown. In this study, we characterized and defined the structural epitope of a patient-derived bactericidal monoclonal IgG (B11) against outer surface protein C (OspC), a homodimeric lipoprotein necessary for B. burgdorferi tick-mediated transmission and early-stage colonization of vertebrate hosts. High-resolution epitope mapping was accomplished through hydrogen deuterium exchange-mass spectrometry and X-ray crystallography. Structural analysis of B11 Fab-OspCA complexes revealed the B11 Fabs associated in a 1:1 stoichiometry with the lateral faces of OspCA homodimers such that the Abs are essentially positioned perpendicular to the spirochete's outer surface. B11's primary contacts reside within the membrane-proximal regions of α-helices 1 and 6 and adjacent loops 5 and 6 in one OspCA monomer. In addition, B11 spans the OspCA dimer interface, engaging opposing α-helix 1', α-helix 2', and loop 2-3' in the second OspCA monomer. The B11-OspCA structure is reminiscent of the recently solved mouse transmission blocking monoclonal IgG B5 in complex with OspCA, indicating a mode of engagement with OspC that is conserved across species. In conclusion, we provide a detailed insight into the interaction between a functional human Ab and an immunodominant Lyme disease Ag long considered an important vaccine candidate.

EpiScan: accurate high-throughput mapping of antibody-specific epitopes using sequence information.

The identification of antibody-specific epitopes on virus proteins is crucial for vaccine development and drug design. Nonetheless, traditional wet-lab approaches for the identification of epitopes are both costly and labor-intensive, underscoring the need for the development of efficient and cost-effective computational tools. Here, EpiScan, an attention-based deep learning framework for predicting antibody-specific epitopes, is presented. EpiScan adopts a multi-input and single-output strategy by designing independent blocks for different parts of antibodies, including variable heavy chain (VH), variable light chain (VL), complementary determining regions (CDRs), and framework regions (FRs). The block predictions are weighted and integrated for the prediction of potential epitopes. Using multiple experimental data samples, we show that EpiScan, which only uses antibody sequence information, can accurately map epitopes on specific antigen structures. The antibody-specific epitopes on the receptor binding domain (RBD) of SARS coronavirus 2 (SARS-CoV-2) were located by EpiScan, and the potentially valuable vaccine epitope was identified. EpiScan can expedite the epitope mapping process for high-throughput antibody sequencing data, supporting vaccine design and drug development. Availability: For the convenience of related wet-experimental researchers, the source code and web server of EpiScan are publicly available at https://github.com/gzBiomedical/EpiScan .

Immunopeptidomics Mapping of Listeria monocytogenes T Cell Epitopes in Mice.

Listeria monocytogenes is a foodborne intracellular bacterial model pathogen. Protective immunity against Listeria depends on an effective CD8+ T cell response, but very few T cell epitopes are known in mice as a common animal infection model for listeriosis. To identify epitopes, we screened for Listeria immunopeptides presented in the spleen of infected mice by mass spectrometry-based immunopeptidomics. We mapped more than 6000 mouse self-peptides presented on MHC class I molecules, including 12 high confident Listeria peptides from 12 different bacterial proteins. Bacterial immunopeptides with confirmed fragmentation spectra were further tested for their potential to activate CD8+ T cells, revealing VTYNYINI from the putative cell wall surface anchor family protein LMON_0576 as a novel bona fide peptide epitope. The epitope showed high biological potency in a prime boost model and can be used as a research tool to probe CD8+ T cell responses in the mouse models of Listeria infection. Together, our results demonstrate the power of immunopeptidomics for bacterial antigen identification.

Integrated structural proteomics and machine learning-guided mapping of a highly protective precision vaccine against mycoplasma pulmonis.

Mycoplasma pulmonis (M. pulmonis) is an emerging respiratory infection commonly linked to prostate cancer, and it is classified under the group of mycoplasmas. Improved management of mycoplasma infections is essential due to the frequent ineffectiveness of current antibiotic treatments in completely eliminating these pathogens from the host. The objective of this study is to design and construct effective and protective vaccines guided by structural proteomics and machine learning algorithms to provide protection against the M. pulmonis infection. Through a thorough examination of the entire proteome of M. pulmonis, four specific targets Membrane protein P80, Lipoprotein, Uncharacterized protein and GGDEF domain-containing protein have been identified as appropriate for designing a vaccine. The proteins underwent mapping of cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL) (IFN)-γ ±, and B-cell epitopes using artificial and recurrent neural networks. The design involved the creation of mRNA and peptide-based vaccine, which consisted of 8 CTL epitopes associated by GGS linkers, 7 HTL (IFN-positive) epitopes, and 8 B-cell epitopes joined by GPGPG linkers. The vaccine designed exhibit antigenic behavior, non-allergenic qualities, and exceptional physicochemical attributes. Structural modeling revealed that correct folding is crucial for optimal functioning. The coupling of the MEVC and Toll-like Receptors (TLR)1, TLR2, and TLR6 was examined through molecular docking experiments. This was followed by molecular simulation investigations, which included binding free energy estimations. The results indicated that the dynamics of the interaction were stable, and the binding was strong. In silico cloning and optimization analysis revealed an optimized sequence with a GC content of 49.776 % and a CAI of 0.982. The immunological simulation results showed strong immune responses, with elevated levels of active and plasma B-cells, regulatory T-cells, HTL, and CTL in both IgM+IgG and secondary immune responses. The antigen was completely cleared by the 50th day. This study lays the foundation for creating a potent and secure vaccine candidate to combat the newly identified M. pulmonis infection in people.

Peptide Antibodies: Current Status.

Peptide antibodies have become one of the most important classes of reagents in molecular biology and clinical diagnostics. For this reason, methods for their production and characterization continue to be developed, including basic peptide synthesis protocols, peptide-conjugate production and characterization, conformationally restricted peptides, immunization procedures, etc. Detailed mapping of peptide antibody epitopes has yielded important information on antibody-antigen interaction in general and specifically in relation to antibody cross-reactivity and theories of molecular mimicry. This information is essential for detailed understanding of paratope-epitope dynamics, design of antibodies for research, design of peptide-based vaccines, development of therapeutic peptide antibodies, and de novo design of antibodies with predetermined specificity.

Two-dimensional high-throughput on-cell screening of immunoglobulins against broad antigen repertoires.

Identifying high-affinity antibodies in human serum is challenging due to extremely low number of circulating B cells specific to the desired antigens. Delays caused by a lack of information on the immunogenic proteins of viral origin hamper the development of therapeutic antibodies. We propose an efficient approach allowing for enrichment of high-affinity antibodies against pathogen proteins with simultaneous epitope mapping, even in the absence of structural information about the pathogenic immunogens. To screen therapeutic antibodies from blood of recovered donors, only pathogen transcriptome is required to design an antigen polypeptide library, representing pathogen proteins, exposed on the bacteriophage surface. We developed a two-dimensional screening approach enriching lentiviral immunoglobulin libraries from the convalescent or vaccinated donors against bacteriophage library expressing the overlapping set of polypeptides covering the spike protein of SARS-CoV-2. This platform is suitable for pathogen-specific immunoglobulin enrichment and allows high-throughput selection of therapeutic human antibodies.

B-Cell Epitope Prediction for Antipeptide Paratopes with the HAPTIC2/HEPTAD User Toolkit (HUT).

B-cell epitope prediction is key to developing peptide-based vaccines and immunodiagnostics along with antibodies for prophylactic, therapeutic and/or diagnostic use. This entails estimating paratope binding affinity for variable-length peptidic sequences subject to constraints on both paratope accessibility and antigen conformational flexibility, as described herein for the HAPTIC2/HEPTAD User Toolkit (HUT). HUT comprises the Heuristic Affinity Prediction Tool for Immune Complexes 2 (HAPTIC2), the HAPTIC2-like Epitope Prediction Tool for Antigen with Disulfide (HEPTAD) and the HAPTIC2/HEPTAD Input Preprocessor (HIP). HIP enables tagging of residues (e.g., in hydrophobic blobs, ordered regions and glycosylation motifs) for exclusion from downstream analyses by HAPTIC2 and HEPTAD. HAPTIC2 estimates paratope binding affinity for disulfide-free disordered peptidic antigens (by analogy between flexible-ligand docking and protein folding), from terms attributed to compaction (in view of sequence length, charge and temperature-dependent polyproline-II helical propensity), collapse (disfavored by residue bulkiness) and contact (with glycine and proline regarded as polar residues that hydrogen bond with paratopes). HEPTAD analyzes antigen sequences that each contain two cysteine residues for which the impact of disulfide pairing is estimated as a correction to the free-energy penalty of compaction. All of HUT is freely accessible online ( https://freeshell.de/~badong/hut.htm ).

Identification and Validation of Peptides Specifying SARS-CoV-2 B-Cell Epitopes Eliciting Neutralizing Antibodies.

Vaccination is an effective means of inducing immune protection to prevent transmissible diseases. During the Covid-19 pandemic, immunizations using traditional and novel vaccine platforms such as the inactivated SARSCo-V-2 vaccine, adenoviral-vectored, and nucleic acid-based mRNA vaccines have been relatively successful in controlling the rates of infection and hospitalizations. Nevertheless, the danger posed by the emergence of SARS-CoV-2 variants would set the stage for the design of next generation vaccines. To overcome the lack of efficacy of current vaccines against emerging SARS-CoV-2 variants, new vaccines must be able to overcome the reduced effectiveness of the current vaccines. Since the current Covid-19 vaccines are dependent on the whole S-protein of Wuhan strain as the antigen, mutations have rendered the current Covid-19 vaccines less effective against variants of concern (VoCs). Instead of using the whole S-protein, peptide-based epitopes could be predicted using immunoinformatic approaches, simulation of the 3D structures, overlapping peptides covering the whole length of the S-protein or peptide arrays based on synthetic peptide combinatorial libraries comprising peptides recognizable by monoclonal antibodies. B-cell epitopes were predicted, and immunogenicity of peptides was validated in mice by immunizing mice with peptides conjugated to keyhole limpet hemocyanin (KLH) mixed with Montanide 51 as an adjuvant. The immunogenicity of epitopes that could elicit peptide specific IgGs was determined by peptide-based ELISA. Neutralizing activities were determined by cPass and pseudovirus-based neutralization assays.

Characterization of Peptide Antibodies by Epitope Mapping Using Resin-Bound and Soluble Peptides.

Characterization of peptide antibodies through identification of their target epitopes is of utmost importance, as information about epitopes provide important knowledge, among others, for discovery and development of new therapeutics, vaccines, and diagnostics.This chapter describes a strategy for mapping of continuous peptide antibody epitopes using resin-bound and soluble peptides. The approach combines three different types of peptide sets for full characterization of peptide antibodies; (i) overlapping peptides, used to locate antigenic regions; (ii) truncated peptides, used to identify the minimal peptide length required for antibody binding; and (iii) substituted peptides, used to identify the key residues important for antibody binding and to determine the specific contribution of key residues. For initial screening, resin-bound peptides are used for epitope estimation, while soluble peptides subsequently are used for final epitope characterization and identification of critical hot spot residues. The combination of resin-bound peptides and soluble peptides for epitope mapping provides a time-saving and straightforward approach for characterization of antibodies recognizing continuous epitopes, which applies to peptide antibodies and occasionally antibodies directed to larger proteins as well.

Mammalian cell display with automated oligo design and library assembly allows for rapid residue level conformational epitope mapping.

Precise epitope determination of therapeutic antibodies is of great value as it allows for further comprehension of mechanism of action, therapeutic responsiveness prediction, avoidance of unwanted cross reactivity, and vaccine design. The golden standard for discontinuous epitope determination is the laborious X-ray crystallography method. Here, we present a combinatorial method for rapid mapping of discontinuous epitopes by mammalian antigen display, eliminating the need for protein expression and purification. The method is facilitated by automated workflows and tailored software for antigen analysis and oligonucleotide design. These oligos are used in automated mutagenesis to generate an antigen receptor library displayed on mammalian cells for direct binding analysis by flow cytometry. Through automated analysis of 33930 primers an optimized single condition cloning reaction was defined allowing for mutation of all surface-exposed residues of the receptor binding domain of SARS-CoV-2. All variants were functionally expressed, and two reference binders validated the method. Furthermore, epitopes of three novel therapeutic antibodies were successfully determined followed by evaluation of binding also towards SARS-CoV-2 Omicron BA.2. We find the method to be highly relevant for rapid construction of antigen libraries and determination of antibody epitopes, especially for the development of therapeutic interventions against novel pathogens.

Identification of novel anti-CD16a antibody clones for the development of effective natural killer cell engagers.

Natural killer (NK) cells are key players in human innate immunity. Cell engager antibody formats that recruit and activate NK cells more effectively have emerged as a promising immunotherapy approach to target cancer cells through more effective antibody-dependent cell-mediated cytotoxicity (ADCC). Monoclonal antibody drugs with ADCC activity have shown clinical benefit and improved outcomes for patients with certain types of cancer. CD16a, a Fc gamma III receptor, is the major component that is responsible for the ADCC activity of NK cells. Screening AvantGen's yeast displayed human antibody libraries led to the isolation of 2 antibody clones, #1A2 and #2-2A2, that selectively recognize both isoforms (F and V) of CD16a on primary NK cells with high affinity, yet minimally (#1A2) or do not (#2-2A2) cross-react with both allelotypes of CD16b (NA1 and NA2) expressed by neutrophils. Epitope mapping studies revealed that they bind to an epitope dependent on residue Y158 of CD16a, since mutation of Y158 to the corresponding CD16b residue H158 completely abolishes binding to CD16a. When formatted as bispecific antibodies targeting CD16a and a tumor-associated antigen (TAA, e.g. CD19), they exhibit specific binding to NK cells and induce potent NK cell activation upon encountering tumor cells, resulting in effective tumor cell killing. Notably, these bispecific antibody engagers stimulate NK cell cytokine release during co-culture with target cells, resulting in target cell cytotoxicity. These anti-CD16a antibody clones are promising candidates for combination with any TAA of interest, offering the potential for novel NK cell engager-based cancer therapeutics that are minimally affected by the high concentrations of human IgG in the circulation.

B cell epitope mapping: The journey to better vaccines and therapeutic antibodies.

B-cell epitope mapping is an approach that can identify and characterise specific antigen binding sites of B-cell receptors and secreted antibodies. The ability to determine the antigenic clusters of amino acids bound by B-cell clones provides unprecedented detail that will aid in developing novel and effective vaccine targets and therapeutic antibodies for various diseases. Here, we discuss conventional approaches and emerging techniques that are used to map B-cell epitopes.

Epitope Mapping of an Anti-Mouse CCR8 Monoclonal Antibody C8Mab-2 Using Flow Cytometry.

The C-C motif chemokine receptor 8 (CCR8) is highly and selectively expressed in regulatory T (Treg) cells and is associated with tumor progression. The massive accumulation of Treg cells into tumors suppresses the effector function of CD8+ cells against tumor cells. Therefore, selective depletion of Treg cells using anti-CCR8 monoclonal antibodies (mAbs) reinvigorates antitumor immune responses and improves responses to cancer immunotherapy. Previously, we developed an anti-mouse CCR8 (mCCR8) mAb, C8Mab-2, using the Cell-Based Immunization and Screening method. In this study, the binding epitope of C8Mab-2 was investigated using flow cytometry. The mCCR8 extracellular domain-substituted mutant analysis showed that C8Mab-2 recognizes the N-terminal region (1-33 amino acids) of mCCR8. Next, 1×alanine (or glycine) scanning and 2×alanine (or glycine) scanning were conducted in the N-terminal region. The results revealed that the 17-DFFTAP-22 sequence is important for the recognition by C8Mab-2, and Thr20 is a central amino acid of the epitope. These results revealed the involvement of the N-terminus of mCCR8 in the recognition by C8Mab-2.

In Silico Tools for Predicting Novel Epitopes.

Identifying antigens within a pathogen is a critical task to develop effective vaccines and diagnostic methods, as well as understanding the evolution and adaptation to host immune responses. Historically, antigenicity was studied with experiments that evaluate the immune response against selected fragments of pathogens. Using this approach, the scientific community has gathered abundant information regarding which pathogenic fragments are immunogenic. The systematic collection of this data has enabled unraveling many of the fundamental rules underlying the properties defining epitopes and immunogenicity, and has resulted in the creation of a large panel of immunologically relevant predictive (in silico) tools. The development and application of such tools have proven to accelerate the identification of novel epitopes within biomedical applications reducing experimental costs. This chapter introduces some basic concepts about MHC presentation, T cell and B cell epitopes, the experimental efforts to determine those, and focuses on state-of-the-art methods for epitope prediction, highlighting their strengths and limitations, and catering instructions for their rational use.

Peptide libraries: from epitope mapping to in-depth high-throughput analysis.

Peptide arrays are a valuable instrument in the characterization of protein-protein interactions (PPIs) and immunogenic regions. New methods were developed to exploit the high-throughput potential of peptide arrays to obtain more in-depth information, replacing traditional resource-intensive experiments. Here, we discuss the recent advances in peptide-array-based technologies and the remaining challenges.

FASTMAP-a flexible and scalable immunopeptidomics pipeline for HLA- and antigen-specific T-cell epitope mapping based on artificial antigen-presenting cells.

The study of peptide repertoires presented by major histocompatibility complex (MHC) molecules and the identification of potential T-cell epitopes contribute to a multitude of immunopeptidome-based treatment approaches. Epitope mapping is essential for the development of promising epitope-based approaches in vaccination as well as for innovative therapeutics for autoimmune diseases, infectious diseases, and cancer. It also plays a critical role in the immunogenicity assessment of protein therapeutics with regard to safety and efficacy concerns. The main challenge emerges from the highly polymorphic nature of the human leukocyte antigen (HLA) molecules leading to the requirement of a peptide mapping strategy for a single HLA allele. As many autoimmune diseases are linked to at least one specific antigen, we established FASTMAP, an innovative strategy to transiently co-transfect a single HLA allele combined with a disease-specific antigen into a human cell line. This approach allows the specific identification of HLA-bound peptides using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Using FASTMAP, we found a comparable spectrum of endogenous peptides presented by the most frequently expressed HLA alleles in the world's population compared to what has been described in literature. To ensure a reliable peptide mapping workflow, we combined the HLA alleles with well-known human model antigens like coagulation factor VIII, acetylcholine receptor subunit alpha, protein structures of the SARS-CoV-2 virus, and myelin basic protein. Using these model antigens, we have been able to identify a broad range of peptides that are in line with already published and in silico predicted T-cell epitopes of the specific HLA/model antigen combination. The transient co-expression of a single affinity-tagged MHC molecule combined with a disease-specific antigen in a human cell line in our FASTMAP pipeline provides the opportunity to identify potential T-cell epitopes/endogenously processed MHC-bound peptides in a very cost-effective, fast, and customizable system with high-throughput potential.