Large-scale data mining of four billion human antibody variable regions reveals convergence between therapeutic and natural antibodies that constrains search space for biologics drug discovery.

The naïve human antibody repertoire has theoretical access to an estimated > 1015 antibodies. Identifying subsets of this prohibitively large space where therapeutically relevant antibodies may be found is useful for development of these agents. It was previously demonstrated that, despite the immense sequence space, different individuals can produce the same antibodies. It was also shown that therapeutic antibodies, which typically follow seemingly unnatural development processes, can arise independently naturally. To check for biases in how the sequence space is explored, we data mined public repositories to identify 220 bioprojects with a combined seven billion reads. Of these, we created a subset of human bioprojects that we make available as the AbNGS database (https://naturalantibody.com/ngs/). AbNGS contains 135 bioprojects with four billion productive human heavy variable region sequences and 385 million unique complementarity-determining region (CDR)-H3s. We find that 270,000 (0.07% of 385 million) unique CDR-H3s are highly public in that they occur in at least five of 135 bioprojects. Of 700 unique therapeutic CDR-H3, a total of 6% has direct matches in the small set of 270,000. This observation extends to a match between CDR-H3 and V-gene call as well. Thus, the subspace of shared ('public') CDR-H3s shows utility for serving as a starting point for therapeutic antibody design.

Understanding the Specific Implications of Amino Acids in the Antibody Development.

As the demand for immunotherapy to treat and manage cancers, infectious diseases and other disorders grows, a comprehensive understanding of amino acids and their intricate role in antibody engineering has become a prime requirement. Naturally produced antibodies may not have the most suitable amino acids at the complementarity determining regions (CDR) and framework regions, for therapeutic purposes. Therefore, to enhance the binding affinity and therapeutic properties of an antibody, the specific impact of certain amino acids on the antibody's architecture must be thoroughly studied. In antibody engineering, it is crucial to identify the key amino acid residues that significantly contribute to improving antibody properties. Therapeutic antibodies with higher binding affinity and improved functionality can be achieved through modifications or substitutions with highly suitable amino acid residues. Here, we have indicated the frequency of amino acids and their association with the binding free energy in CDRs. The review also analyzes the experimental outcome of two studies that reveal the frequency of amino acids in CDRs and provides their significant correlation between the outcomes. Additionally, it discusses the various bond interactions within the antibody structure and antigen binding. A detailed understanding of these amino acid properties should assist in the analysis of antibody sequences and structures needed for designing and enhancing the overall performance of therapeutic antibodies.

T-cell receptor diversity and allergen sensitivity in childhood asthma and atopic dermatitis.

BACKGROUND: Childhood allergies of asthma and atopic dermatitis (AD) involve an overactive T-cell immune response triggered by allergens. However, the impact of T-cell receptor (TCR) repertoires on allergen sensitization and their role in mediating different phenotypes of asthma and AD in early childhood remains unclear. METHODS: A total of 78 children, comprising 26 with asthma alone, 26 with AD alone, and 26 healthy controls (HC), were enrolled. TCR repertoire profiles were determined using a unique molecular identifier system for next-generation sequencing. Integrative analyses of their associations with allergen-specific IgE levels and allergies were performed. RESULTS: The diversity in TCR alpha variable region (TRAV) genes of TCR repertoires and complementarity determining region 3 (CDR3) clonality in TRAV/TRBV (beta) genes were significantly higher in children with AD compared with those with asthma and HC (p < .05). Compared with HC, the expression of TRAV13-1 and TRAV4 genes was significantly higher in both asthma and AD (p < .05), with a significant positive correlation with mite-specific IgE levels (p < .01). In contrast, TRBV7-9 gene expression was significantly lower in both asthma and AD (p < .01), with this gene showing a significant negative correlation with mite-specific IgE levels (p < .01). Furthermore, significantly higher TRAV8-3 gene expression, positively correlated with food-specific IgE levels, was found in children with AD compared with those with asthma (p < .05). CONCLUSION: Integrated TCR repertoires analysis provides clinical insights into the diverse TCR genes linked to antigen specificity, offering potential for precision immunotherapy in childhood allergies.

Evaluation of the Inhibitory Potential of Synthetic Peptides Homologous to CDR3 Regions of a Monoclonal Antibody against Bothropic Venom Serine Proteases.

Snakebite accidents, neglected tropical diseases per the WHO, pose a significant public health threat due to their severity and frequency. Envenomation by Bothrops genus snakes leads to severe manifestations due to proteolytic enzymes. While the antibothropic serum produced by the Butantan Institute saves lives, its efficacy is limited as it fails to neutralize certain serine proteases. Hence, developing new-generation antivenoms, like monoclonal antibodies, is crucial. This study aimed to explore the inhibitory potential of synthetic peptides homologous to the CDR3 regions of a monoclonal antibody targeting a snake venom thrombin-like enzyme (SVTLE) from B. atrox venom. Five synthetic peptides were studied, all stable against hydrolysis by venoms and serine proteases. Impressively, four peptides demonstrated uncompetitive SVTLE inhibition, with Ki values ranging from 10-6 to 10-7 M. These findings underscore the potential of short peptides homologous to CDR3 regions in blocking snake venom toxins, suggesting their promise as the basis for new-generation antivenoms. Thus, this study offers potential advancements in combatting snakebites, addressing a critical public health challenge in tropical and subtropical regions.

An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries.

Antibodies are engineerable quantities in medicine. Learning antibody molecular recognition would enable the in silico design of high affinity binders against nearly any proteinaceous surface. Yet, publicly available experiment antibody sequence-binding datasets may not contain the mutagenic, antigenic, or antibody sequence diversity necessary for deep learning approaches to capture molecular recognition. In part, this is because limited experimental platforms exist for assessing quantitative and simultaneous sequence-function relationships for multiple antibodies. Here we present MAGMA-seq, an integrated technology that combines multiple antigens and multiple antibodies and determines quantitative biophysical parameters using deep sequencing. We demonstrate MAGMA-seq on two pooled libraries comprising mutants of nine different human antibodies spanning light chain gene usage, CDR H3 length, and antigenic targets. We demonstrate the comprehensive mapping of potential antibody development pathways, sequence-binding relationships for multiple antibodies simultaneously, and identification of paratope sequence determinants for binding recognition for broadly neutralizing antibodies (bnAbs). MAGMA-seq enables rapid and scalable antibody engineering of multiple lead candidates because it can measure binding for mutants of many given parental antibodies in a single experiment.

Local chicken breeds exhibit abundant TCR-V segments but similar repertoire diversity.

The thymus-derived lymphocytes of jawed vertebrates have four T-cell receptor (TCR) chains that play a significant role in immunity. As chickens have commercial value, their immune systems require a great deal of attention. Local chicken breeds are an essential part of poultry genetic resources in China. Here, we used high-throughput sequencing to analyze the TCRα and TCRß repertoires and their relative expression levels in the native chicken breeds Baier Buff, Longyou Partridge, Xiaoshan, and Xianju. We found that TCR Vα and TCR Vß were expressed and included 17, 19, 17, and six segments of the Vα2, Vα3, Vß1, and Vß2 subgroups, respectively. V-J pairing was biased; Jα11 was utilized by nearly all Vα segments and was the most commonly used. Breed-specific V segments and V-J pairings were detected as well. The results of the principal coordinate analysis (PCoA) as well as the V-J pairing and CDR3 diversity analyses suggested that the four local chicken breeds did not significantly differ in terms of TCR diversity. Hence, they expressed not significant differentiation, and they are rich genetic resources for the development and utilization of immune-related poultry breeding.

Human CD4-binding site antibody elicited by polyvalent DNA prime-protein boost vaccine neutralizes cross-clade tier-2-HIV strains.

The vaccine elicitation of HIV tier-2-neutralization antibodies has been a challenge. Here, we report the isolation and characterization of a CD4-binding site (CD4bs) specific monoclonal antibody, HmAb64, from a human volunteer immunized with a polyvalent DNA prime-protein boost HIV vaccine. HmAb64 is derived from heavy chain variable germline gene IGHV1-18 and light chain germline gene IGKV1-39. It has a third heavy chain complementarity-determining region (CDR H3) of 15 amino acids. On a cross-clade panel of 208 HIV-1 pseudo-virus strains, HmAb64 neutralized 20 (10%), including tier-2 strains from clades B, BC, C, and G. The cryo-EM structure of the antigen-binding fragment of HmAb64 in complex with a CNE40 SOSIP trimer revealed details of its recognition; HmAb64 uses both heavy and light CDR3s to recognize the CD4-binding loop, a critical component of the CD4bs. This study demonstrates that a gp120-based vaccine can elicit antibodies capable of tier 2-HIV neutralization.

Vaccine priming of rare HIV broadly neutralizing antibody precursors in nonhuman primates.

Germline-targeting immunogens hold promise for initiating the induction of broadly neutralizing antibodies (bnAbs) to HIV and other pathogens. However, antibody-antigen recognition is typically dominated by heavy chain complementarity determining region 3 (HCDR3) interactions, and vaccine priming of HCDR3-dominant bnAbs by germline-targeting immunogens has not been demonstrated in humans or outbred animals. In this work, immunization with N332-GT5, an HIV envelope trimer designed to target precursors of the HCDR3-dominant bnAb BG18, primed bnAb-precursor B cells in eight of eight rhesus macaques to substantial frequencies and with diverse lineages in germinal center and memory B cells. We confirmed bnAb-mimicking, HCDR3-dominant, trimer-binding interactions with cryo-electron microscopy. Our results demonstrate proof of principle for HCDR3-dominant bnAb-precursor priming in outbred animals and suggest that N332-GT5 holds promise for the induction of similar responses in humans.

Vaccination induces broadly neutralizing antibody precursors to HIV gp41.

A key barrier to the development of vaccines that induce broadly neutralizing antibodies (bnAbs) against human immunodeficiency virus (HIV) and other viruses of high antigenic diversity is the design of priming immunogens that induce rare bnAb-precursor B cells. The high neutralization breadth of the HIV bnAb 10E8 makes elicitation of 10E8-class bnAbs desirable; however, the recessed epitope within gp41 makes envelope trimers poor priming immunogens and requires that 10E8-class bnAbs possess a long heavy chain complementarity determining region 3 (HCDR3) with a specific binding motif. We developed germline-targeting epitope scaffolds with affinity for 10E8-class precursors and engineered nanoparticles for multivalent display. Scaffolds exhibited epitope structural mimicry and bound bnAb-precursor human naive B cells in ex vivo screens, protein nanoparticles induced bnAb-precursor responses in stringent mouse models and rhesus macaques, and mRNA-encoded nanoparticles triggered similar responses in mice. Thus, germline-targeting epitope scaffold nanoparticles can elicit rare bnAb-precursor B cells with predefined binding specificities and HCDR3 features.

Invariant γδTCR natural killer-like effector T cells in the naked mole-rat.

The naked mole-rat (Heterocephalus glaber) is a long-lived rodent species showing resistance to the development of cancer. Although naked mole-rats have been reported to lack natural killer (NK) cells, γδ T cell-based immunity has been suggested in this species, which could represent an important arm of the immune system for antitumor responses. Here, we investigate the biology of these unconventional T cells in peripheral tissues (blood, spleen) and thymus of the naked mole-rat at different ages by TCR repertoire profiling and single-cell gene expression analysis. Using our own TCR annotation in the naked mole-rat genome, we report that the γδ TCR repertoire is dominated by a public invariant Vγ4-2/Vδ1-4 TCR, containing the complementary-determining-region-3 (CDR3)γ CTYWDSNYAKKLF / CDR3δ CALWELRTGGITAQLVF that are likely generated by short-homology-repeat-driven DNA rearrangements. This invariant TCR is specifically found in γδ T cells expressing genes associated with NK cytotoxicity and is generated in both the thoracic and cervical thymus of the naked mole-rat until adult life. Our results indicate that invariant Vγ4-2/Vδ1-4 NK-like effector T cells in the naked mole-rat can contribute to tumor immunosurveillance by γδ TCR-mediated recognition of a common molecular signal.

Identification of apolipoprotein B-reactive CDR3 motifs allows tracking of atherosclerosis-related memory CD4+T cells in multiple donors.

Introduction: Atherosclerosis is a major pathological condition that underlies many cardiovascular diseases (CVDs). Its etiology involves breach of tolerance to self, leading to clonal expansion of autoreactive apolipoprotein B (APOB)-reactive CD4+T cells that correlates with clinical CVD. The T-cell receptor (TCR) sequences that mediate activation of APOB-specific CD4+T cells are unknown. Methods: In a previous study, we had profiled the hypervariable complementarity determining region 3 (CDR3) of CD4+T cells that respond to six immunodominant APOB epitopes in most donors. Here, we comprehensively analyze this dataset of 149,065 APOB-reactive and 199,211 non-reactive control CDR3s from six human leukocyte antigen-typed donors. Results: We identified 672 highly expanded (frequency threshold > 1.39E-03) clones that were significantly enriched in the APOB-reactive group as compared to the controls (log10 odds ratio ≥1, Fisher's test p < 0.01). Analysis of 114,755 naïve, 91,001 central memory (TCM) and 29,839 effector memory (TEM) CDR3 sequences from the same donors revealed that APOB+ clones can be traced to the complex repertoire of unenriched blood T cells. The fraction of APOB+ clones that overlapped with memory CDR3s ranged from 2.2% to 46% (average 16.4%). This was significantly higher than their overlap with the naïve pool, which ranged from 0.7% to 2% (average 1.36%). CDR3 motif analysis with the machine learning-based in-silico tool, GLIPHs (grouping of lymphocyte interactions by paratope hotspots), identified 532 APOB+ motifs. Analysis of naïve and memory CDR3 sequences with GLIPH revealed that ~40% (209 of 532) of these APOB+ motifs were enriched in the memory pool. Network analysis with Cytoscape revealed extensive sharing of the memory-affiliated APOB+ motifs across multiple donors. We identified six motifs that were present in TCM and TEM CDR3 sequences from >80% of the donors and were highly enriched in the APOB-reactive TCR repertoire. Discussion: The identified APOB-reactive expanded CD4+T cell clones and conserved motifs can be used to annotate and track human atherosclerosis-related autoreactive CD4+T cells and measure their clonal expansion.

[Biological characteristics and clinical significance of stereotyped B-cell receptor in chronic lymphocytic leukemia].

Chronic lymphocytic leukemia (CLL) is the most common leukemia in the Western adults, although the incidence of CLL is relatively low in Asian populations. However, with the aging population, the incidence of CLL is increasing in China. The interaction between CLL cells and the microenvironment plays a crucial role in the recognition of antigens by the B-cell receptor immunoglobulin (BCR IG). The mutational status of the immunoglobulin heavy variable region (IGHV) is a classical prognostic marker for CLL. Over 40% of CLL patients exhibit biased usage of IGHV and highly similar amino acid sequences in the heavy complementarity-determining region 3 (HCDR3), known as the BCR stereotypy. Different subgroups of stereotyped BCR exhibit distinct biological and clinical features. Among them, subset #2 with mutated IGHV and poor prognosis, as well as the subset #8 with a high risk of Richter transformation, have been recommended by the European Research Initiative on CLL to be included in clinical reports on IGHV mutational status. This review summarizes the definition, distribution, biological characteristics, and clinical significance of clonality patterns of the BCR in CLL.

Design and Evaluation of a Humanized Anti-EGFR huscFv Antibody Fragment in Combination With Metformin in A549 Cells In Vitro.

BACKGROUND/AIM: The epidermal growth factor receptor (EGFR) is over-expressed in several types of cancer, and monoclonal antibody therapy has been the strategy that has shown the best results. This study focused on the construction of a humanized single chain antibody (huscFv) directed against EGFR (HER1). MATERIALS AND METHODS: The CDR grafting method was used to incorporate murine complementarity determining regions (CDRs) of cetuximab into human sequences. A dot blot assay was used to examine the affinity of the huscFv secreted by HEK293T for EGFR. The inhibitory effect on the viability of A549 cells was evaluated using the WST-1 assay. RESULTS: The incorporation of murine CDRs of cetuximab into human sequences increased the degree of humanness by 16.4%. The increase in the humanization of scFv did not affect the affinity for EGFR. Metformin had a dose-dependent effect, with an IC50 of 46 mM, and in combination with huscFv, the cell viability decreased by 45% compared to the 15% demonstrated by huscFv alone. CONCLUSION: The CDR grafting technique is efficient for the humanization of scFv, maintaining its affinity for EGFR and demonstrating its inhibitory effect when combined with metformin in A549 cells.

Specific features of a scaffolding antibody light chain.

The antigen-binding sites in conventional antibodies are formed by hypervariable complementarity-determining regions (CDRs) from both heavy chains (HCs) and light chains (LCs). A deviation from this paradigm is found in a subset of bovine antibodies that bind antigens via an ultra-long CDR. The HCs bearing ultra-long CDRs pair with a restricted set of highly conserved LCs that convey stability to the antibody. Despite the importance of these LCs, their specific features remained unknown. Here, we show that the conserved bovine LC found in antibodies with ultra-long CDRs exhibits a distinct combination of favorable physicochemical properties such as good secretion from mammalian cells, strong dimerization, high stability, and resistance to aggregation. These physicochemical traits of the LCs arise from a combination of the specific sequences in the germline CDRs and a lambda LC framework. In addition to understanding the molecular architecture of antibodies with ultra-long CDRs, our findings reveal fundamental insights into LC characteristics that can guide the design of antibodies with improved properties.

Computational Modeling and Characterization of Peptides Derived from Nanobody Complementary-Determining Region 2 (CDR2) Targeting Active-State Conformation of the ß2-Adrenergic Receptor (ß2AR).

This study assessed the suitability of the complementarity-determining region 2 (CDR2) of the nanobody (Nb) as a template for the derivation of nanobody-derived peptides (NDPs) targeting active-state ß2-adrenergic receptor (ß2AR) conformation. Sequences of conformationally selective Nbs favoring the agonist-occupied ß2AR were initially analyzed by the informational spectrum method (ISM). The derived NDPs in complex with ß2AR were subjected to protein-peptide docking, molecular dynamics (MD) simulations, and metadynamics-based free-energy binding calculations. Computational analyses identified a 25-amino-acid-long CDR2-NDP of Nb71, designated P4, which exhibited the following binding free-energy for the formation of the ß2AR:P4 complex (ΔG = -6.8 ± 0.8 kcal/mol or a Ki = 16.5 µM at 310 K) and mapped the ß2AR:P4 amino acid interaction network. In vitro characterization showed that P4 (i) can cross the plasma membrane, (ii) reduces the maximum isoproterenol-induced cAMP level by approximately 40% and the isoproterenol potency by up to 20-fold at micromolar concentration, (iii) has a very low affinity to interact with unstimulated ß2AR in the cAMP assay, and (iv) cannot reduce the efficacy and potency of the isoproterenol-mediated ß2AR/ß-arrestin-2 interaction in the BRET2-based recruitment assay. In summary, the CDR2-NDP, P4, binds preferentially to agonist-activated ß2AR and disrupts Gαs-mediated signaling.

AbFlex: designing antibody complementarity determining regions with flexible CDR definition.

MOTIVATION: Antibodies are proteins that the immune system produces in response to foreign pathogens. Designing antibodies that specifically bind to antigens is a key step in developing antibody therapeutics. The complementarity determining regions (CDRs) of the antibody are mainly responsible for binding to the target antigen, and therefore must be designed to recognize the antigen. RESULTS: We develop an antibody design model, AbFlex, that exhibits state-of-the-art performance in terms of structure prediction accuracy and amino acid recovery rate. Furthermore, >38% of newly designed antibody models are estimated to have better binding energies for their antigens than wild types. The effectiveness of the model is attributed to two different strategies that are developed to overcome the difficulty associated with the scarcity of antibody-antigen complex structure data. One strategy is to use an equivariant graph neural network model that is more data-efficient. More importantly, a new data augmentation strategy based on the flexible definition of CDRs significantly increases the performance of the CDR prediction model. AVAILABILITY AND IMPLEMENTATION: The source code and implementation are available at https://github.com/wsjeon92/AbFlex.

Investigating the ability of deep learning-based structure prediction to extrapolate and/or enrich the set of antibody CDR canonical forms.

Deep learning models have been shown to accurately predict protein structure from sequence, allowing researchers to explore protein space from the structural viewpoint. In this paper we explore whether "novel" features, such as distinct loop conformations can arise from these predictions despite not being present in the training data. Here we have used ABodyBuilder2, a deep learning antibody structure predictor, to predict the structures of ~1.5M paired antibody sequences. We examined the predicted structures of the canonical CDR loops and found that most of these predictions fall into the already described CDR canonical form structural space. We also found a small number of "new" canonical clusters composed of heterogeneous sequences united by a common sequence motif and loop conformation. Analysis of these novel clusters showed their origins to be either shapes seen in the training data at very low frequency or shapes seen at high frequency but at a shorter sequence length. To evaluate explicitly the ability of ABodyBuilder2 to extrapolate, we retrained several models whilst withholding all antibody structures of a specific CDR loop length or canonical form. These "starved" models showed evidence of generalisation across CDRs of different lengths, but they did not extrapolate to loop conformations which were highly distinct from those present in the training data. However, the models were able to accurately predict a canonical form even if only a very small number of examples of that shape were in the training data. Our results suggest that deep learning protein structure prediction methods are unable to make completely out-of-domain predictions for CDR loops. However, in our analysis we also found that even minimal amounts of data of a structural shape allow the method to recover its original predictive abilities. We have made the ~1.5 M predicted structures used in this study available to download at https://doi.org/10.5281/zenodo.10280181.

Do antibody CDR loops change conformation upon binding?

Antibodies have increasingly been developed as drugs with over 100 now licensed in the US or EU. During development, it is often necessary to increase or reduce the affinity of an antibody and rational attempts to do so rely on having a structure of the antibody-antigen complex often obtained by modeling. The antigen-binding site consists primarily of six loops known as complementarity-determining regions (CDRs), and an open question has been whether these loops change their conformation when they bind to an antigen. Existing surveys of antibody-antigen complex structures have only examined CDR conformational change in case studies or small-scale surveys. With an increasing number of antibodies where both free and complexed structures have been deposited in the Protein Data Bank, a large-scale survey of CDR conformational change during binding is now possible. To this end, we built a dataset, AbAgDb, that currently includes 177 antibodies with high-quality CDRs, each of which has at least one bound and one unbound structure. We analyzed the conformational change of the Cα backbone of each CDR upon binding and found that, in most cases, the CDRs (other than CDR-H3) show minimal movement, while 70.6% and 87% of CDR-H3s showed global Cα RMSD ≤ 1.0Å and ≤ 2.0Å, respectively. We also compared bound CDR conformations with the conformational space of unbound CDRs and found most of the bound conformations are included in the unbound conformational space. In future, our results will contribute to developing insights into antibodies and new methods for modeling and docking.

Structure- and machine learning-guided engineering demonstrate that a non-canonical disulfide in an anti-PD-1 rabbit antibody does not impede antibody developability.

Rabbits produce robust antibody responses and have unique features in their antibody repertoire that make them an attractive alternative to rodents for in vivo discovery. However, the frequent occurrence of a non-canonical disulfide bond between complementarity-determining region (CDR) H1 (C35a) and CDRH2 (C50) is often seen as a liability for therapeutic antibody development, despite limited reports of its effect on antibody binding, function, and stability. Here, we describe the discovery and humanization of a human-mouse cross-reactive anti-programmed cell death 1 (PD-1) monoclonal rabbit antibody, termed h1340.CC, which possesses this non-canonical disulfide bond. Initial removal of the non-canonical disulfide resulted in a loss of PD-1 affinity and cross-reactivity, which led us to explore protein engineering approaches to recover these. First, guided by the sequence of a related clone and the crystal structure of h1340.CC in complex with PD-1, we generated variant h1340.SA.LV with a potency and cross-reactivity similar to h1340.CC, but only partially recovered affinity. Side-by-side developability assessment of both h1340.CC and h1340.SA.LV indicate that they possess similar, favorable properties. Next, and prompted by recent developments in machine learning (ML)-guided protein engineering, we used an unbiased ML- and structure-guided approach to rapidly and efficiently generate a different variant with recovered affinity. Our case study thus indicates that, while the non-canonical inter-CDR disulfide bond found in rabbit antibodies does not necessarily constitute an obstacle to therapeutic antibody development, combining structure- and ML-guided approaches can provide a fast and efficient way to improve antibody properties and remove potential liabilities.

Extraction of the CDRH3 sequence of the mouse antibody repertoire selected upon influenza virus infection by subtraction of the background antibody repertoire.

Historically, antibody reactivity to pathogens and vaccine antigens has been evaluated using serological measurements of antigen-specific antibodies. However, it is difficult to evaluate all antibodies that contribute to various functions in a single assay, such as the measurement of the neutralizing antibody titer. Bulk antibody repertoire analysis using next-generation sequencing is a comprehensive method for analyzing the overall antibody response; however, it is unreliable for estimating antigen-specific antibodies due to individual variation. To address this issue, we propose a method to subtract the background signal from the repertoire of data of interest. In this study, we analyzed changes in antibody diversity and inferred the heavy-chain complementarity-determining region 3 (CDRH3) sequences of antibody clones that were selected upon influenza virus infection in a mouse model using bulk repertoire analysis. A decrease in the diversity of the antibody repertoire was observed upon viral infection, along with an increase in neutralizing antibody titers. Using kernel density estimation of sequences in a high-dimensional sequence space with background signal subtraction, we identified several clusters of CDRH3 sequences induced upon influenza virus infection. Most of these repertoires were detected more frequently in infected mice than in uninfected control mice, suggesting that infection-specific antibody sequences can be extracted using this method. Such an accurate extraction of antigen- or infection-specific repertoire information will be a useful tool for vaccine evaluation in the future. IMPORTANCE: As specific interactions between antigens and cell-surface antibodies trigger the proliferation of B-cell clones, the frequency of each antibody sequence in the samples reflects the size of each clonal population. Nevertheless, it is extremely difficult to extract antigen-specific antibody sequences from the comprehensive bulk antibody sequences obtained from blood samples due to repertoire bias influenced by exposure to dietary antigens and other infectious agents. This issue can be addressed by subtracting the background noise from the post-immunization or post-infection repertoire data. In the present study, we propose a method to quantify repertoire data from comprehensive repertoire data. This method allowed subtraction of the background repertoire, resulting in more accurate extraction of expanded antibody repertoires upon influenza virus infection. This accurate extraction of antigen- or infection-specific repertoire information is a useful tool for vaccine evaluation.