Diversity in the CDR3 region of V(H) is sufficient for most antibody specificities

Data-driven analyses of human antibody variable domain germlines: pairings, sequences and structural features

The Observed Antibody Space provides the most abundant collection of annotated paired antibody variable domain sequences, thus offering a unique platform for the systematic investigation of the factors governing the pairing of antibody heavy and light chains. By examining a range of characteristics, including amino acid conservation, structural features, charge distribution, and interface residue identity, we challenge the prevailing assumption that pairing is random. Our findings indicate that specific physicochemical properties of single amino acid residues may influence the compatibility and affinity of heavy and light chain combinations. Further structural analyses based on antibody Fv fragments deposited in the Protein Data Bank (PDB) provide insights into the underlying structural features driving these pairing preferences, including a novel definition for the residues constituting the VH-VL interface, based on a collection of over 3500 structures. These results have significant implications for understanding antibody assembly and may guide the rational design of therapeutic antibodies with desired properties. Moreover, we provide a complete description and reference characterizing the various human germlines.

From bioinformatics to clinical application: A new strategy in CRP detection with peptide aptamer

C-Reactive protein (CRP) is a key biomarker for evaluating inflammation levels and estimating cardiovascular risk. However, current CRP detection methods rely on monoclonal antibodies (mAb), which possess shortcomings such as a lengthy preparation cycle, high cost, and poor repeatability. To address these challenges, we explored the potential of peptide aptamers as an alternative to mAb for CRP detection. Using some bioinformatics approaches, we designed and optimized peptide aptamers, selecting the dominant peptide aptamer C9m (KWRWRFRLSR) through experimental validation for its specific recognition of CRP. We then established a sandwich ELISA detection system combining C9m with CRP mAb. This system demonstrated a detection limit of 22.275 ng/mL CRP and exhibited excellent specificity, with no cross-reactivity observed with human serum albumin or γ-globulin. The method also showed high reproducibility, with intra- and inter-assay coefficients of variation (CV) less than 15 %, meeting laboratory testing standards. Furthermore, comparison with clinically used immunoturbidimetry revealed high consistency (r = 0.9891).

Penicillin-like mimotopes from autodisplayed Fv-antibody library inhibiting β-lactamase activity

A penicillin-like mimotope was screened from an Fv-antibody library which had the inhibition activity of β-lactamase. Fv-antibody indicated the variable region (VH) of the immunoglobulin G, which includes three complementarity determining regions (CDRs). The Fv-antibody library was then prepared by randomizing the complementarity determining region 3 (CDR3), and it was expressed on the outer membrane of E. coli. The penicillin-like mimotopes were screened from the Fv-antibody library using magnetic beads with an immobilized monoclonal anti-penicillin antibody. The screened mimotopes were expressed as soluble Fv-antibodies and were also synthesized into peptides (11-mer). The binding affinity (KD) of the expressed Fv-antibodies and synthesized peptides was estimated using SPR measurements. The β-lactamase inhibition activity of the Fv-antibodies and synthetic peptides was estimated using colorimetry based on the formation of penicilloic acid. The penicillin-like mimotopes of the expressed Fv-antibodies and synthesized peptides were demonstrated to have β-lactamase inhibition activity in the bacterial lysates. Finally, the docking analysis of β-lactamase and the screened CDR3 sequences demonstrated that the screened CDR3 sequences were specifically bound to the binding sites of β-lactamase.

Public antibodies: convergent signatures in human humoral immunity against pathogens

The human humoral immune system has evolved to recognize a vast array of pathogenic threats. This ability is primarily driven by the immense diversity of antibodies generated by gene rearrangement during B cell development. However, different people often produce strikingly similar antibodies when exposed to the same antigen-known as public antibodies. Public antibodies not only reflect the immune system's ability to consistently select for optimal B cells but can also serve as signatures of the humoral responses triggered by infection and vaccination. In this Minireview, we examine and compare public antibody identification methods, including the identification criteria used based on V(D)J gene usage and similarity in the complementarity-determining region three sequences, and explore the molecular features of public antibodies elicited against common pathogens, including viruses, protozoa, and bacteria. Finally, we discuss the evolutionary significance and potential applications of public antibodies in informing the design of germline-targeting vaccines, predicting escape mutations in emerging viruses, and providing insights into the process of affinity maturation. The ongoing discovery of public antibodies in response to emerging pathogens holds the potential to improve pandemic preparedness, accelerate vaccine design efforts, and deepen our understanding of human B cell biology.

Comparison of sequence- and structure-based antibody clustering approaches on simulated repertoire sequencing data

Repertoire sequencing allows us to investigate the antibody-mediated immune response. The clustering of sequences is a crucial step in the data analysis pipeline, aiding in the identification of functionally related antibodies. The conventional clustering approach of clonotyping relies on sequence information, particularly CDRH3 sequence identity and V/J gene usage, to group sequences into clonotypes. It has been suggested that the limitations of sequence-based approaches to identify sequence-dissimilar but functionally converged antibodies can be overcome by using structure information to group antibodies. Recent advances have made structure-based methods feasible on a repertoire level. However, so far, their performance has only been evaluated on single-antigen sets of antibodies. A comprehensive comparison of the benefits and limitations of structure-based tools on realistic and diverse repertoire data is missing. Here, we aim to explore the promise of structure-based clustering algorithms to replace or augment the standard sequence-based approach, specifically by identifying low-sequence identity groups. Two methods, SAAB+ and SPACE2, are evaluated against clonotyping. We curated a dataset of well-annotated pairs of antibodies that show high overlap in epitope residues and thus bind the same region within their respective antigen. This set of antibodies was introduced into a simulated repertoire to compare the performance of clustering approaches on a diverse antibody set. Our analysis reveals that structure-based methods do group more antibodies together compared to clonotyping. However, it also highlights the limitations associated with the need for same-length CDR regions by SPACE2. This work thoroughly compares the utility of different clustering methods and provides insights into what further steps are required to effectively use antibody structural information to group immune repertoire data.

Transmembrane protease serine 2 (TMPRSS2) inhibitors screened from an Fv-antibody library for preventing SARS-CoV-2 infection

Fv-antibodies targeting the transmembrane protease serine 2 (TMPRSS2) were screened from an Fv-antibody library for inhibiting SARS-CoV-2 infection. Fv-antibodies were derived from the variable region of heavy-chain immunoglobulin G (IgG), which consisted of three complementarity-determining regions (CDRs) and frame regions (FRs). The Fv-antibody library was prepared through site-directed mutagenesis of CDR3 region. The proteolytic cleavage site (S2' site) of TMPRSS2 on the spike protein (SP) of SARS-CoV-2 was used as a screening probe for the library. Two Fv-antibodies were screened and subsequently expressed as soluble recombinant proteins. The binding affinities of the expressed Fv-antibodies were estimated using a surface plasmon resonance (SPR) biosensor. The two expressed Fv-antibodies specifically bound to the active site of TMPRSS2 which interacts with S2' site in the proprotein convertase (PPC) region. The neutralizing activities of the two expressed Fv-antibodies were demonstrated using a cell-based infection assay with pseudo-viruses that expressed the SP of four types of SARS-CoV-2 variants: Wu-1 (D614), Delta (B.1.617.2), Omicron (BA.2), and Omicron (BA.4/5). Additionally, a docking simulation was performed to analyze the interaction between the screened Fv-antibodies and the active sites of TMPRSS2.

Identification of chicken-derived antibodies targeting the Candida albicans Als3 protein

Candida albicans is a major opportunistic pathogen, responsible for nearly half of clinical candidemia cases. The rising prevalence of azole-resistant Candida species represents a significant clinical challenge, underscoring the urgent need for alternative therapeutic strategies. Monoclonal antibody-based therapies have emerged as a promising and cost-effective approach to combating Candida infections. Agglutinin-like sequence protein 3 (Als3), a key cell surface protein of C. albicans, plays a pivotal role in adherence and biofilm formation, both of which are essential for its pathogenesis. In this study, recombinant Als3 protein was purified and utilized to immunize chickens, resulting in the production of Als3-specific immunoglobulin Y (IgY) antibodies. Two single-chain variable fragment (scFv) antibody libraries were subsequently constructed using phage display technology, yielding transformant counts of 5.3 × 107 and 2.8 × 107, respectively. Phage-based enzyme-linked immunosorbent assay (ELISA) revealed enhanced signals following bio-panning, enabling the identification and sequence validation of three scFv antibodies. These scFv antibodies exhibited strong binding activities to Als3, as confirmed through ELISA and western blot analyses. Binding affinities were determined to be ~ 10⁻⁸ M via serial titration ELISA and competitive ELISA. Additionally, the selected scFv antibodies specifically recognized endogenous Als3 protein in C. albicans, as demonstrated by western blot and cell-based ELISA assays. In conclusion, this study successfully generated and characterized high-affinity scFv antibodies targeting Als3, which exhibited exceptional specificity and binding activity. These findings highlight their potential as promising immunotherapeutic candidates for the treatment of C. albicans infections. KEY POINTS: • The Als3 protein of C. albicans is a critical biomarker and therapeutic target • Chicken-derived scFv antibodies against Als3 were developed via phage display • The scFv antibodies showed strong binding to endogenous Als3 in C. albicans.

Screening of deoxyribonuclease I inhibitors from autodisplayed Fv-antibody library

Deoxyribonuclease (DNase) I inhibitors have been developed based on proteins, nucleotides and synthetic compounds. In this work, amino acid sequences with the activity of DNase I inhibitor were screened from an Fv-antibody library expressed on the outer membrane of Escherichia coli. The Fv-antibody indicated the heavy chain variable region (VH) of immunoglobulin G (IgG) and the Fv-antibody library was generated with a randomized complementarity-determining region 3 (CDR3). From the Fv-antibody library, two clones were screened for their binding affinity to DNase I and expressed as soluble recombinant proteins as well as peptides. The binding affinity (KD) to DNase I was estimated for the expressed Fv-antibodies (73.4 nM for Fv-1 and 89.0 nM for Fv-19) and synthesized peptides (279.2 nM for Peptide-1 and 243.2 nM for Peptide-19) using SPR biosensor. The inhibitory activity (IC50) of the expressed Fv-antibodies (550.0 nM for Fv-1 and 660.2 nM for Fv-19) and synthetic peptides (864.5 nM for Peptide-1 and 974.6 nM for Peptide-19) was measured using agarose-gel assay and TaqMan-like fluorescence assay. These IC50 values indicated that both expressed Fv-antibodies and synthesized peptides exerted an effective inhibitory activity against DNase I. The interaction between the screened inhibitors and DNase I was analyzed by docking simulation.

New insights into antibody structure with implications for specificity, variable region restriction and isotype choice

The mystery surrounding the mechanisms by which antibody diversity is generated was largely settled in the 1970s by the discoveries of variable gene rearrangements and somatic hypermutation. This led to the paradigm that immunoglobulins are composed of two independent domains - variable and constant - that confer specificity and effector functions, respectively. However, since these early discoveries, there have been a series of observations of communication between the variable and constant domains that affects the overall antibody structure, which suggests that immunoglobulins have a more complex, interconnected functionality than previously thought. Another unresolved issue has been the genesis of 'restricted' antibody responses, characterized by the use of only a few variable region gene segments, despite the enormous potential combinatorial diversity. In this Perspective, we place recent findings related to immunoglobulin structure and function in the context of these immunologically important, historically unsolved problems to propose a new model for how antibody specificity is achieved without autoreactivity.

Convergent and clonotype-enriched mutations in the light chain drive affinity maturation of a public antibody

Public antibodies that recognize conserved epitopes are critical for vaccine development, and identifying somatic hypermutations (SHMs) that enhance antigen affinity in these public responses is key to guiding vaccine design for better protection. We propose that affinity-enhancing SHMs are selectively enriched in public antibody clonotypes, surpassing the background frequency seen in antibodies carrying the same V genes, but with different epitope specificities. Employing a human IGHV4-59/IGKV3-20 public antibody as a model, we compare SHM signatures in antibodies also using these V genes, but recognizing other epitopes. Critically, this comparison identified clonotype-enriched mutations in the light chain. Our analyses also show that these SHMs, in combination, enhance binding to a previously uncharacterized viral epitope, with antibody responses to it increasing after multiple vaccinations. Our findings offer a framework for identifying affinity-enhancing SHMs in public antibodies based on convergence and clonotype-enrichment and can help guide vaccine design aimed to elicit public antibodies.

NI-3201 Is a Bispecific Antibody Mediating PD-L1-Dependent CD28 Co-stimulation on T Cells for Enhanced Tumor Control

Despite advances in cancer immunotherapy, such as targeting the PD-1/PD-L1 axis, a substantial number of patients harbor tumors that are resistant or relapse. Selective engagement of T-cell co-stimulatory molecules with bispecific antibodies may offer novel therapeutic options by enhancing signal 1-driven activation occurring via T-cell receptor engagement. In this study, we report the development and preclinical characterization of NI-3201, a PD-L1×CD28 bispecific antibody generated on the κλ-body platform that was designed to promote T-cell activity and antitumor function through a dual mechanism of action. We confirmed that NI-3201 blocks the PD-L1/PD-1 immune checkpoint pathway and conditionally provides T-cell co-stimulation via CD28 (signal 2) when engaging PD-L1+ tumors or immune cells. In systems with signal 1-primed T cells, NI-3201 enhanced potent effector functionality: in vitro through antigen-specific recall assays with cytomegalovirus-specific T cells and in vivo by inducing tumor regression and immunologic memory in tumor-associated antigen-expressing MC38 syngeneic mouse models. When T-cell engagers were used to provide synthetic signal 1, the combination with NI-3201 resulted in synergistic T cell-dependent cytotoxicity and potent antitumor activity in two humanized mouse tumor models. Nonhuman primate safety assessments showed favorable tolerability and pharmacokinetics at pharmacologically active doses. Quantitative systems pharmacology modeling predicted that NI-3201 exposure results in antitumor activity in patients, but this remains to be investigated. Overall, this study suggests that by combining PD-L1 blockade with safe and effective CD28 co-stimulation, NI-3201 has the potential to improve cancer immunotherapy outcomes, and the clinical development of NI-3201 for PD-L1+ solid tumors is planned.

Multiomics Reveals the Immunologic Features and the Immune Checkpoint Blockade Potential of Colorectal Medullary Carcinoma

PURPOSE

Colorectal medullary carcinoma (MeC) is extensive lymphocyte infiltration and is associated with an active immune response. However, studies to comprehensively explore the immune landscape and efficacy of immune checkpoint blockade (ICB) therapy in MeC are limited.

EXPERIMENTAL DESIGN

We screened 47 cases of MeC from the Harbin Medical University Cancer Hospital cohort. The immunologic characteristics of MeC were analyzed by targeted exon sequencing, NanoString nCounter gene expression sequencing, IHC, multiplexed immunofluorescence, and T-cell antigen receptor sequencing. An additional 47 patients with MeC who received ICB therapy were included in the retrospective analysis to verify the efficacy of immunotherapy.

RESULTS

Genomically, MeC tends to have a higher proportion of mismatch repair protein deficiency/microsatellite instability (MSI), ARID1A mutation, and ASCL2 amplification. Gene expression shows enriched immune response-related pathways while downregulating oncogenic pathways, such as glycolysis, epithelial-mesenchymal transition, and Wnt/β-catenin signaling. Further immune characterization showed that MeC showed advantages in antigen presentation, co-stimulatory molecules, effector molecules, immune checkpoints, and immune cell abundance. More importantly, both MSI and microsatellite-stable type MeC showed a similar state of high infiltration of immune cells, even better than MSI non-MeC. MeC infiltrated massive highly clonal immune cells, especially intraepithelial CD8+ T cells. In the retrospective cohort, there were 30 patients with MeC who received ICB therapy and achieved complete or partial response with an objective response rate of 63.8%, especially including 16 patients with microsatellite-stable colorectal cancer.

CONCLUSIONS

MeC is a pathologic subtype with an active immune response and is a promising group for ICB therapy. This heightened immune response was not limited to the patients' microsatellite status.

Chicken-Derived Single-Chain Variable Fragments Targeting Monkeypox Virus A29L Protein

Monkeypox (mpox), a zoonotic disease, has rapidly spread globally, prompting the WHO to declare it a public health emergency. The long incubation period, early symptoms resembling respiratory infections, and diagnostic challenges hinder timely epidemic control and accurate clinical diagnosis. The monkeypox virus (MPXV) encodes the A29L protein, which binds to cellular heparan sulfate to facilitate infection and serves as a target for treatment and diagnostics. Thus, developing effective diagnostic tools and treatments is critically important. In this study, we expressed and purified Escherichia coli-derived A29L protein, which was used for chicken immunization to generate specific polyclonal IgY antibodies. The results demonstrated a successful elicitation of a humoral immune response. Subsequently, two single-chain variable fragments (scFv) antibody libraries were constructed using phage display technology, comprising 2.6 × 108 and 3.8 × 108 transformants. After bio-panning, phage-based ELISA indicated the enrichment of specific clones. Three scFv-expressing clones, including cA29LS1, cA29LS5, and cA29LS13, were selected from 13 randomly chosen clones and classified based on nucleotide sequence analysis. Their binding activities were evaluated through ELISA and Western blot, followed by purification for affinity determination via competitive ELISA. Among the selected clones, cA29LS5 demonstrated the highest binding affinity (1.3 × 10⁻⁶ M), followed by cA29LS1 (5.3 × 10⁻⁶ M). Additionally, both IgY and all three clones demonstrated binding activity to cell-derived and commercially purchased A29L proteins, as confirmed by Western blot and ELISA. Overall, these findings suggested that the IgY and scFv antibodies developed hold promise as potential diagnostic and therapeutic agents against MPXV infections.

Persistent splenic-derived IgMs preferentially recognize factor VIII A2 and C2 domain epitopes but do not alter antibody production

BACKGROUND

The most significant treatment complication for patients with hemophilia A is the development of neutralizing immunoglobins (Igs) G, termed inhibitors, against factor VIII (FVIII), which prevent FVIII replacement therapy. Low titers of FVIII-specific IgMs have been identified in hemophilia A patients with and without inhibitors, as well as in healthy individuals. However, the duration and influence of IgMs on the immune response to FVIII remains unclear.

OBJECTIVES

To characterize the binding interactions of persistently secreted FVIII-specific IgMs in hemophilia A mice and assess their effect on IgG antibody development.

METHODS

Splenic-derived monoclonal antibodies (mAbs) from immunized FVIII knockout mice were isolated and purified using hybridoma technology. Binding interactions were assessed utilizing a novel fluid-phase enzyme-linked immunosorbent assay and computational modeling with High Ambiguity-Driven protein-protein DOCKing to account for weak IgM binding.

RESULTS

Sixteen porcine cross-reactive and noninhibitory FVIII-specific IgM mAbs were identified. RNA sequencing of FVIII-specific IgMs revealed 13 unique variable, diversity, and joining (VDJ)/variable and joining (VJ) sequences indicating derivation from 13 unique B cell clones. The IgMs demonstrated polyclonal and polyreactive binding to FVIII in vitro and in silico. Molecular docking studies with reconstructed IgM variable, diversity, and joining/variable and joining regions identified frequent IgM interactions with amino acid residues K376, T381, K437, R2215, or K2249 within the FVIII A2 and C2 domains. Injections of individual IgMs prior to FVIII exposure and co-injection of FVIII/IgM immune complexes did not affect de novo FVIII antibody production.

CONCLUSION

Persistent FVIII-specific IgMs are polyclonal but preferentially bind the A2 and C2 domains. FVIII/IgM immune complex formation does not significantly alter inhibitor development.

A computational approach to matching multiple sclerosis-related, IGH CDR3s with a MBP epitope

In multiple sclerosis (MS), T-cell receptors (TCRs) and antibodies specifically target the main structural proteins of myelin, including myelin basic protein (MBP), especially a specific, canonical, immunoglobulin (IG)-targeted MBP epitope. Efficient computational analyses to diagnose or monitor autoimmune conditions, which could have broad applicability in clinical trials or in diagnoses, remains a challenge. As such, we considered the possibility that focusing on the immunoglobin heavy chain (IGH) complementarity determining region-3 (CDR3) amino acid sequences could support the development of an efficient, convenient, and user-friendly approach to detecting or assessing IGH targets in MS. Thus, we applied a chemical complementarity scoring algorithm, extensively benchmarked in many cancer settings, to assess the combined electrostatic and hydrophobic attractiveness of large numbers of (individual patient) IGH CDR3s and the canonical IG MBP epitope. Samples and controls were filtered to only include CDR3s above a baseline chemical complementarity score. Then, the frequency of each unique IGH CDR3 (with the minimum MBP epitope complementarity) in the MS samples was compared to the same parameter for the control sample. Specifically, a greater number of high frequency IGH CDR3s, with chemically complementary to the canonical MBP epitope, was detected in 47 out of 48 MS-control comparisons, in most cases representing a p < 0.0001. With continued development, this approach has the potential to lead to a user-friendly computational screening tool for patients at risk for developing MS. Additional results indicate that the methodology could also be applied to antigen epitope discovery.

Unveiling inverted D genes and D-D fusions in human antibody repertoires unlocks novel antibody diversity

Antibodies, essential components of adaptive immunity, derive their remarkable diversity primarily from V(D)J gene rearrangements, particularly within the heavy chain complementarity-determining region 3 (CDR-H3) where D genes play a major role. Traditionally, D genes were thought to recombine only in the forward direction, despite having identical recombination signal sequences (12 base pair spacers) at both ends. This observation led us to question whether these symmetrical sequences might enable bidirectional recombination. We identified 25 unique inverted D genes (InvDs) in both naive and memory B cells from antibody repertoires of 13 healthy donors. These InvDs utilize all three reading frames during translation, producing distinct amino acid profiles enriched in histidine, proline, and lysine in CDR-H3s of antibodies with potential functional diversity. Notably, our analysis revealed a broader range of D-D fusions, including D-D, D-InvD, InvD-D, and InvD-InvD configurations, opening new perspectives for antibody engineering and therapeutic development.

Amino acids characterization based on frequency and interaction analysis in human antigen-antibody complexes from Thera-SAbDab

BackgroundAntibodies are composed of light and heavy chains, both of which have constant and variable regions. The diversity, specific binding ability and therapeutic potential of antibodies are determined by hypervariable loops called complementarity-determining regions (CDRs), with the other regions being the framework regions.ObjectiveTo investigate the key amino acid patterns in various antibody regions in the human therapeutic antigen-antibody (Ag-Ab) complexes collected from the Thera-SAbDab database.MethodThe study focuses on identifying the amino acid frequency, diversity index in CDRs, paratope-epitope amino acid interactions, amino acid bond formation frequency, and bond types among selected therapeutic Ag-Ab complexes.ResultsThe results revealed that Ser is highly distributed in the overall light chain CDRs while Gly is highly distributed in the heavy chain CDRs. CDR profiling analysis indicated that the average amino acid diversity in heavy chain CDRs is 60% to 70%, while in the light chain, it is 50% to 60%. Aromatic residues such as Tyr, Trp and Phe are the top contributors to these paratope-epitope interactions in the light and heavy chains. Moreover, we examined the frequency of amino acids in light and heavy chains of Ag-Ab complexes. Importantly, the outcome of this study leverages the in depth analysis on single residues, dipeptides, and tripeptides for the therapeutic Ag-Ab complexes.ConclusionWe conclude that the amino acid frequency and interaction analysis centered on therapeutic Ag-Ab complexes will benefit antibody engineering parameters such as antibody design, optimization, affinity maturation, and overall antibody development.

SARS-CoV-2 vaccine based on ferritin complexes with screened immunogenic sequences from the Fv-antibody library

In this study, the vaccine against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was developed using ferritin complexes with the immunogenic sequences screened against the SARS-CoV-2 spike protein (SP) from the Fv-antibody library. The Fv-antibody library was prepared on the outer membrane of E. coli by the expression of the VH region of immunoglobulin G (IgG) with a randomized complementarity-determining region 3 (CDR3). Four Fv-antibodies to the receptor-binding domain (RBD) were screened from the Fv-antibody library, which had a comparable binding constant (KD) between SARS-CoV-2 SP and the angiotensin-converting enzyme 2 (ACE2) receptor. The binding sites of screened Fv-antibodies on the RBD were analyzed using a docking analysis, and these binding sites were used as immunogenic sequences for the vaccine. The four immunogenic sequences were modified and co-expressed as a part of ferritin which was assembled into a ferritin complex. After the vaccination of ferritin complexes to mice, the anti-sera were analyzed to have a high enough titer. Additionally, the immune responses were found to be activated by vaccination, such as the expression of IgG subclasses and the increased level of cytokines. The neutralizing activity of the anti-sera was estimated using a cell-based infection assay based on pseudo-virus expressing the SP of SARS-CoV-2 variants.

An allelic atlas of immunoglobulin heavy chain variable regions reveals antibody binding epitope preference resilient to SARS-CoV-2 mutation escape

Background

Although immunoglobulin (Ig) alleles play a pivotal role in the antibody response to pathogens, research to understand their role in the humoral immune response is still limited.

Methods

We retrieved the germline sequences for the IGHV from the IMGT database to illustrate the amino acid polymorphism present within germline sequences of IGHV genes. We aassembled the sequences of IgM and IgD repertoire from 130 people to investigate the genetic variations in the population. A dataset comprising 10,643 SARS-CoV-2 spike-specific antibodies, obtained from COV-AbDab, was compiled to assess the impact of SARS-CoV-2 infection on allelic gene utilization. Binding affinity and neutralizing activity were determined using bio-layer interferometry and pseudovirus neutralization assays. Primary docking was performed using ZDOCK (3.0.2) to generate the initial conformation of the antigen-antibody complex, followed by simulations of the complete conformations using Rosetta SnugDock software. The original and simulated structural conformations were visualized and presented using ChimeraX (v1.5).

Results

We present an allelic atlas of immunoglobulin heavy chain (IgH) variable regions, illustrating the diversity of allelic variants across 33 IGHV family germline sequences by sequencing the IgH repertoire of in the population. Our comprehensive analysis of SARS-CoV-2 spike-specific antibodies revealed the preferential use of specific Ig alleles among these antibodies. We observed an association between Ig alleles and antibody binding epitopes. Different allelic genotypes binding to the same RBD epitope on the spike show different neutralizing potency and breadth. We found that antibodies carrying the IGHV1-69*02 allele tended to bind to the RBD E2.2 epitope. The antibodies carrying G50 and L55 amino acid residues exhibit potential enhancements in binding affinity and neutralizing potency to SARS-CoV-2 variants containing the L452R mutation on RBD, whereas R50 and F55 amino acid residues tend to have reduced binding affinity and neutralizing potency. IGHV2-5*02 antibodies using the D56 allele bind to the RBD D2 epitope with greater binding and neutralizing potency due to the interaction between D56 on HCDR2 and K444 on RBD of most Omicron subvariants. In contrast, IGHV2-5*01 antibodies using the N56 allele show increased binding resistance to the K444T mutation on RBD.

Discussion

This study provides valuable insights into humoral immune responses from the perspective of Ig alleles and population genetics. These findings underscore the importance of Ig alleles in vaccine design and therapeutic antibody development.

Stereotyped B-Cell Receptor Immunoglobulins in B-Cell Lymphomas

Thorough examination of clonotypic B-cell receptor immunoglobulin (BcR IG) gene rearrangement sequences in patients with mature B-cell malignancies has revealed significant repertoire restrictions, leading to the identification of subsets of patients expressing highly similar, stereotyped BcR IG. This discovery strongly suggests selection by common epitopes or classes of structurally similar epitopes in the development of these tumors. Initially observed in chronic lymphocytic leukemia (CLL), where the stereotyped fraction accounts for a substantial fraction of patients, stereotyped BcR IGs have also been identified in other mature B-cell malignancies, including mantle cell lymphoma (MCL) and splenic marginal zone lymphoma (SMZL).Further comparisons across different entities have indicated that stereotyped IGs are predominantly "disease-biased," indicating distinct immune pathogenetic trajectories. Notably, accumulating evidence suggests that molecular subclassification of mature B-cell malignancies based on BcR IG stereotypy holds biological and clinical relevance. Particularly in CLL, patients belonging to the same subset due to the expression of a specific stereotyped BcR IG exhibit consistent biological backgrounds and clinical courses, especially for major and extensively studied subsets. Therefore, robust assignment to stereotyped subsets may aid in uncovering mechanisms underlying disease initiation and progression, as well as refining patient risk stratification. In this chapter, we offer an overview of recent studies on BcR IG stereotypy in mature B-cell malignancies and delineate past and present methodological approaches utilized for the identification of stereotyped BcR IG.

Lowering the affinity of single-chain monovalent BBB shuttle scFc-scFv8D3 prolongs its half-life and increases brain concentration

Monoclonal antibody therapeutics is a massively growing field. Progress in providing monoclonal antibody therapeutics to treat brain disorders is complicated, due to the impermeability of the blood-brain barrier (BBB) to large macromolecular structures. To date, the most successful approach for delivering antibody therapeutics to the brain is by targeting the transferrin receptor (TfR) using anti-TfR BBB shuttles, with the 8D3 antibody being one of the most extensively studied in the field. The strategy of fine-tuning TfR binding affinity has shown promise, with previous results showing an improved brain delivery of bivalent 8D3-BBB constructs. In the current study, a fine-tuning TfR affinity strategy has been employed to improve single-chain variable fragment (scFv) 8D3 (scFv8D3) affinity mutants. Initially, in silico protein-protein docking analysis was performed to identify amino acids (AAs) likely to contribute to 8D3s TfR binding affinity. Mutating the identified AAs resulted in decreased TfR binding affinity, increased blood half-life and increased brain concentration. As monovalent BBB shuttles are seemingly superior for delivering antibodies at therapeutically relevant doses, our findings and approach may be relevant for optimizing brain delivery.

Reading the repertoire: Progress in adaptive immune receptor analysis using machine learning

The adaptive immune system holds invaluable information on past and present immune responses in the form of B and T cell receptor sequences, but we are limited in our ability to decode this information. Machine learning approaches are under active investigation for a range of tasks relevant to understanding and manipulating the adaptive immune receptor repertoire, including matching receptors to the antigens they bind, generating antibodies or T cell receptors for use as therapeutics, and diagnosing disease based on patient repertoires. Progress on these tasks has the potential to substantially improve the development of vaccines, therapeutics, and diagnostics, as well as advance our understanding of fundamental immunological principles. We outline key challenges for the field, highlighting the need for software benchmarking, targeted large-scale data generation, and coordinated research efforts.

Screened Fv-Antibodies against the Angiotensin-Converting Enzyme 2 (ACE2) Receptor Neutralizing the Infection of SARS-CoV-2

For the prevention of SARS-CoV-2 infection, four Fv-antibodies with binding affinity for the ACE2 receptor were screened from an Fv-antibody library. The screened Fv-antibodies were expressed as soluble proteins and estimated to have a high binding affinity, comparable to that between SARS-CoV-2 and the ACE2 receptor. The interaction between the Fv-antibodies and the ACE2 receptor was analyzed using docking simulation, and the significant binding affinity of the screened Fv-antibodies was attributed to the homology in amino acid sequence with the ACE2 receptor. The neutralizing activities of the Fv-antibodies were demonstrated using a cell-based infection assay based on four pseudo-virus types with SARS-CoV-2 variant spike proteins (Wild-type D614, Delta B.1.617.2, and Omicron BA.2, and Omicron BA.4/5).

Multiple instance learning method based on convolutional neural network and self-attention for early cancer detection

Early cancer detection using T-cell receptor sequencing (TCR-seq) and multiple instances learning methods has shown significant effectiveness. We introduce a multiple instance learning method based on convolutional neural networks and self-attention (MICA). First, MICA preprocesses TCR-seq using word vectors and then extracts features using convolutional neural networks. Second, MICA uses an enhanced self-attention mechanism to extract relational features of instances. Finally, MICA can extract the crucial TCR-seq. After cross-validation, MICA achieves an area under the curve (AUC) of 0.911 and 0.946 on the lung and thyroid cancer datasets, which are 7.1% and 2.1% higher than other methods, respectively.

Determining key residues of engineered scFv antibody variants with improved MMP-9 binding using deep sequencing and machine learning

Given the crucial role of specific matrix metalloproteinases (MMPs) in the extracellular matrix, an imbalance in the regulation of activation of matrix metalloproteinase-9 (MMP-9) zymogen and inhibition of the enzyme can result in various diseases, such as cancer, neurodegenerative, and gynecological diseases. Thus, developing novel therapeutics that target MMP-9 with single-chain antibody fragments (scFvs) is a promising approach. We used fluorescent-activated cell sorting (FACS) to screen a synthetic scFv antibody library displayed on yeast for enhanced binding to MMP-9. The screened scFv mutants demonstrated improved binding to MMP-9 compared to the natural inhibitor of MMPs, tissue inhibitor of metalloproteinases (TIMPs). To identify the molecular determinants of these engineered scFv variants that affect binding to MMP-9, we used next-generation DNA sequencing and computational protein structure analysis. Additionally, a deep-learning language model was trained on the screened scFv library of variants to predict the binding affinities of scFv variants based on their CDR-H3 sequences.

A clinical-information-free method for early diagnosis of lung cancer from the patients with pulmonary nodules based on backpropagation neural network model

Lung cancer is the main cause of cancer-related deaths worldwide. Due to lack of obvious clinical symptoms in the early stage of the lung cancer, it is hard to distinguish between malignancy and pulmonary nodules. Understanding the immune responses in the early stage of malignant lung cancer patients may provide new insights for diagnosis. Here, using high-through-put sequencing, we obtained the TCRβ repertoires in the peripheral blood of 100 patients with Stage I lung cancer and 99 patients with benign pulmonary nodules. Our analysis revealed that the usage frequencies of TRBV, TRBJ genes, and V-J pairs and TCR diversities indicated by D50s, Shannon indexes, Simpson indexes, and the frequencies of the largest TCR clone in the malignant samples were significantly different from those in the benign samples. Furthermore, reduced TCR diversities were correlated with the size of pulmonary nodules. Moreover, we built a backpropagation neural network model with no clinical information to identify lung cancer cases from patients with pulmonary nodules using 15 characteristic TCR clones. Based on the model, we have created a web server named "Lung Cancer Prediction" (LCP), which can be accessed at http://i.uestc.edu.cn/LCP/index.html.

Single-cell RNA sequencing and immune repertoire analysis revealed dynamic immune characteristics associated with peripheral blood during sepsis

Sepsis is a potentially fatal condition arising from an abnormal immune response to an infection, which can result in organ failure and even death. To explore the mechanism underlying the dysregulated immune response during sepsis and identify potential therapeutic targets, single-cell RNA sequencing (scRNA-seq) and immune repertoire analysis were conducted to depict the cellular landscape of peripheral blood cells in septic mice. We observed significant alterations in the number and proportion of peripheral blood cell populations driven by sepsis. By combining single-cell gene expression profiles and B cell receptor (BCR) repertoire analysis, we discerned that infection inflicted serious damage on the antigen presentation ability of B cells and the diversity of BCR in a short time. In addition, we found that the cecal ligation and puncture procedure in mice inhibited the communication signals of CD4+ and CD8+ T cells and decreased the interactions between B cells and other cells. Our study provides detailed insights into the dynamic changes in the biological characteristics of peripheral blood cells driven by sepsis and provides important advances in our understanding of immune disorders during sepsis.

Development of a new genotype-phenotype linked antibody screening system

Antibodies are powerful tools for the therapy and diagnosis of various diseases. In addition to conventional hybridoma-based screening, recombinant antibody-based screening has become a common choice; however, its application is hampered by two factors: (1) screening starts after Ig gene cloning and recombinant antibody production only, and (2) the antibody is composed of paired chains, heavy and light, commonly expressed by two independent expression vectors. Here, we introduce a method for the rapid screening of recombinant monoclonal antibodies by establishing a Golden Gate-based dual-expression vector and in-vivo expression of membrane-bound antibodies. Using this system, we demonstrate the rapid isolation of influenza cross-reactive antibodies with high affinity from immunized mice within 7 days. This system is particularly useful for isolating therapeutic or diagnostic antibodies, for example during foreseen pandemics.

Immunisation of chickens with inactivated and/or infectious H9N2 avian influenza virus leads to differential immune B-cell repertoire development

Avian influenza viruses (AIVs) are a major economic burden to the poultry industry and pose serious zoonotic risks, with human infections being reported every year. To date, the vaccination of birds remains the most important method for the prevention and control of AIV outbreaks. Most national vaccination strategies against AIV infection use whole virus-inactivated vaccines, which predominantly trigger a systemic antibody-mediated immune response. There are currently no studies that have examined the antibody repertoire of birds that were infected with and/or vaccinated against AIV. To this end, we evaluate the changes in the H9N2-specific IgM and IgY repertoires in chickens subjected to vaccination(s) and/or infectious challenge. We show that a large proportion of the IgM and IgY clones were shared across multiple individuals, and these public clonal responses are dependent on both the immunisation status of the birds and the specific tissue that was examined. Furthermore, the analysis revealed specific clonal expansions that are restricted to particular H9N2 immunisation regimes. These results indicate that both the nature and number of immunisations are important drivers of the antibody responses and repertoire profiles in chickens following H9N2 antigenic stimulation. We discuss how the repertoire biology of avian B-cell responses may affect the success of AIV vaccination in chickens, in particular the implications of public versus private clonal selection.

Rapid affinity optimization of an anti-TREM2 clinical lead antibody by cross-lineage immune repertoire mining

We describe a process for rapid antibody affinity optimization by repertoire mining to identify clones across B cell clonal lineages based on convergent immune responses where antigen-specific clones with the same heavy (VH) and light chain germline segment pairs, or parallel lineages, bind a single epitope on the antigen. We use this convergence framework to mine unique and distinct VH lineages from rat anti-triggering receptor on myeloid cells 2 (TREM2) antibody repertoire datasets with high diversity in the third complementarity-determining loop region (CDR H3) to further affinity-optimize a high-affinity agonistic anti-TREM2 antibody while retaining critical functional properties. Structural analyses confirm a nearly identical binding mode of anti-TREM2 variants with subtle but significant structural differences in the binding interface. Parallel lineage repertoire mining is uniquely tailored to rationally explore the large CDR H3 sequence space in antibody repertoires and can be easily and generally applied to antibodies discovered in vivo.

Predictability of antigen binding based on short motifs in the antibody CDRH3

Adaptive immune receptors, such as antibodies and T-cell receptors, recognize foreign threats with exquisite specificity. A major challenge in adaptive immunology is discovering the rules governing immune receptor-antigen binding in order to predict the antigen binding status of previously unseen immune receptors. Many studies assume that the antigen binding status of an immune receptor may be determined by the presence of a short motif in the complementarity determining region 3 (CDR3), disregarding other amino acids. To test this assumption, we present a method to discover short motifs which show high precision in predicting antigen binding and generalize well to unseen simulated and experimental data. Our analysis of a mutagenesis-based antibody dataset reveals 11 336 position-specific, mostly gapped motifs of 3-5 amino acids that retain high precision on independently generated experimental data. Using a subset of only 178 motifs, a simple classifier was made that on the independently generated dataset outperformed a deep learning model proposed specifically for such datasets. In conclusion, our findings support the notion that for some antibodies, antigen binding may be largely determined by a short CDR3 motif. As more experimental data emerge, our methodology could serve as a foundation for in-depth investigations into antigen binding signals.

Epidermal Growth Factor Receptor (EGFR) Inhibitors Screened from Autodisplayed Fv-Antibody Library

Inhibitors of the epithermal growth factor receptor (EGFR) were screened from an autodisplayed Fv-antibody library using an anti-EGF antibody. The Fv-antibody library was expressed on the outer membrane of Escherichia coli, which corresponds to the heavy chain VH region of immunoglobulin G. The library was constructed by randomizing the CDR3 region of expressed VH regions (11 amino acid residues) by site-directed mutagenesis. Using an anti-EGF antibody as a screening probe, amino acid sequences (CDR3 region) with antibody binding affinity were screened from the Fv-antibody library. These amino acid sequences were considered to have similar chemical properties to EGF, which can bind to EGFR. Two autodisplayed clones with Fv-antibodies against EGFR were screened from the Fv-antibody library, and the screened Fv-antibodies were expressed as soluble proteins. The binding affinity (KD) was estimated using an SPR biosensor, and the inhibitory activity of expressed Fv-antibodies was observed for PANC-1 pancreatic tumor cells and T98G glioblastoma cells using Western blot analysis of proteins in the EGFR-mediated signaling pathway. The viability of PANC-1 and T98G cells was observed to decrease via the inhibitory activity of expressed Fv-antibodies. Finally, interactions between Fv-antibodies and EGFR were analyzed by using molecular docking simulations.

Bacterial cell surface characterization by phage display coupled to high-throughput sequencing

The remarkable capacity of bacteria to adapt in response to selective pressures drives antimicrobial resistance. Pseudomonas aeruginosa illustrates this point, establishing chronic infections during which it evolves to survive antimicrobials and evade host defenses. Many adaptive changes occur on the P. aeruginosa cell surface but methods to identify these are limited. Here we combine phage display with high-throughput DNA sequencing to create a high throughput, multiplexed technology for surveying bacterial cell surfaces, Phage-seq. By applying phage display panning to hundreds of bacterial genotypes and analyzing the dynamics of the phage display selection process, we capture important biological information about cell surfaces. This approach also yields camelid single-domain antibodies that recognize key P. aeruginosa virulence factors on live cells. These antibodies have numerous potential applications in diagnostics and therapeutics. We propose that Phage-seq establishes a powerful paradigm for studying the bacterial cell surface by identifying and profiling many surface features in parallel.

De novo generation of SARS-CoV-2 antibody CDRH3 with a pre-trained generative large language model

Artificial Intelligence (AI) techniques have made great advances in assisting antibody design. However, antibody design still heavily relies on isolating antigen-specific antibodies from serum, which is a resource-intensive and time-consuming process. To address this issue, we propose a Pre-trained Antibody generative large Language Model (PALM-H3) for the de novo generation of artificial antibodies heavy chain complementarity-determining region 3 (CDRH3) with desired antigen-binding specificity, reducing the reliance on natural antibodies. We also build a high-precision model antigen-antibody binder (A2binder) that pairs antigen epitope sequences with antibody sequences to predict binding specificity and affinity. PALM-H3-generated antibodies exhibit binding ability to SARS-CoV-2 antigens, including the emerging XBB variant, as confirmed through in-silico analysis and in-vitro assays. The in-vitro assays validate that PALM-H3-generated antibodies achieve high binding affinity and potent neutralization capability against spike proteins of SARS-CoV-2 wild-type, Alpha, Delta, and the emerging XBB variant. Meanwhile, A2binder demonstrates exceptional predictive performance on binding specificity for various epitopes and variants. Furthermore, by incorporating the attention mechanism inherent in the Roformer architecture into the PALM-H3 model, we improve its interpretability, providing crucial insights into the fundamental principles of antibody design.

Bat humoral immunity and its role in viral pathogenesis, transmission, and zoonosis

Bats harbor viruses that can cause severe disease and death in humans including filoviruses (e.g., Ebola virus), henipaviruses (e.g., Hendra virus), and coronaviruses (e.g., SARS-CoV). Bats often tolerate these viruses without noticeable adverse immunological effects or succumbing to disease. Previous studies have largely focused on the role of the bat's innate immune response to control viral pathogenesis, but little is known about bat adaptive immunity. A key component of adaptive immunity is the humoral response, comprised of antibodies that can specifically recognize viral antigens with high affinity. The antibody genes within the 1,400 known bat species are highly diverse, and these genetic differences help shape fundamental aspects of the antibody repertoire, including starting diversity and viral antigen recognition. Whether antibodies in bats protect, mediate viral clearance, and prevent transmission within bat populations is poorly defined. Furthermore, it is unclear how neutralizing activity and Fc-mediated effector functions contribute to bat immunity. Although bats have canonical Fc genes (e.g., mu, gamma, alpha, and epsilon), the copy number and sequences of their Fc genes differ from those of humans and mice. The function of bat antibodies targeting viral antigens has been speculated based on sequencing data and polyclonal sera, but functional and biochemical data of monoclonal antibodies are lacking. In this review, we summarize current knowledge of bat humoral immunity, including variation between species, their potential protective role(s) against viral transmission and replication, and address how these antibodies may contribute to population dynamics within bats communities. A deeper understanding of bat adaptive immunity will provide insight into immune control of transmission and replication for emerging viruses with the potential for zoonotic spillover.

BertTCR: a Bert-based deep learning framework for predicting cancer-related immune status based on T cell receptor repertoire

The T cell receptor (TCR) repertoire is pivotal to the human immune system, and understanding its nuances can significantly enhance our ability to forecast cancer-related immune responses. However, existing methods often overlook the intra- and inter-sequence interactions of T cell receptors (TCRs), limiting the development of sequence-based cancer-related immune status predictions. To address this challenge, we propose BertTCR, an innovative deep learning framework designed to predict cancer-related immune status using TCRs. BertTCR combines a pre-trained protein large language model with deep learning architectures, enabling it to extract deeper contextual information from TCRs. Compared to three state-of-the-art sequence-based methods, BertTCR improves the AUC on an external validation set for thyroid cancer detection by 21 percentage points. Additionally, this model was trained on over 2000 publicly available TCR libraries covering 17 types of cancer and healthy samples, and it has been validated on multiple public external datasets for its ability to distinguish cancer patients from healthy individuals. Furthermore, BertTCR can accurately classify various cancer types and healthy individuals. Overall, BertTCR is the advancing method for cancer-related immune status forecasting based on TCRs, offering promising potential for a wide range of immune status prediction tasks.

Cmai: Predicting Antigen-Antibody Interactions from Massive Sequencing Data

The interaction between antigens and antibodies (B cell receptors, BCRs) is the key step underlying the function of the humoral immune system in various biological contexts. The capability to profile the landscape of antigen-binding affinity of a vast number of BCRs will provide a powerful tool to reveal novel insights at unprecedented levels and will yield powerful tools for translational development. However, current experimental approaches for profiling antibody-antigen interactions are costly and time-consuming, and can only achieve low-to-mid throughput. On the other hand, bioinformatics tools in the field of antibody informatics mostly focus on optimization of antibodies given known binding antigens, which is a very different research question and of limited scope. In this work, we developed an innovative Artificial Intelligence tool, Cmai, to address the prediction of the binding between antibodies and antigens that can be scaled to high-throughput sequencing data. Cmai achieved an AUROC of 0.91 in our validation cohort. We devised a biomarker metric based on the output from Cmai applied to high-throughput BCR sequencing data. We found that, during immune-related adverse events (irAEs) caused by immune-checkpoint inhibitor (ICI) treatment, the humoral immunity is preferentially responsive to intracellular antigens from the organs affected by the irAEs. In contrast, extracellular antigens on malignant tumor cells are inducing B cell infiltrations, and the infiltrating B cells have a greater tendency to co-localize with tumor cells expressing these antigens. We further found that the abundance of tumor antigen-targeting antibodies is predictive of ICI treatment response. Overall, Cmai and our biomarker approach filled in a gap that is not addressed by current antibody optimization works nor works such as AlphaFold3 that predict the structures of complexes of proteins that are known to bind.

rAbDesFlow: a novel workflow for computational recombinant antibody design for healthcare engineering

Recombinant antibodies (rAbs) have emerged as a promising solution to tackle antigen specificity, enhancement of immunogenic potential and versatile functionalization to treat human diseases. The development of single chain variable fragments has helped accelerate treatment in cancers and viral infections, due to their favorable pharmacokinetics and human compatibility. However, designing rAbs is traditionally viewed as a genetic engineering problem, with phage display and cell free systems playing a major role in sequence selection for gene synthesis. The process of antibody engineering involves complex and time-consuming laboratory techniques, which demand substantial resources and expertise. The success rate of obtaining desired antibody candidates through experimental approaches can be modest, necessitating iterative cycles of selection and optimization. With ongoing advancements in technology, in silico design of diverse antibody libraries, screening and identification of potential candidates for in vitro validation can be accelerated. To meet this need, we have developed rAbDesFlow, a unified computational workflow for recombinant antibody engineering with open-source programs and tools for ease of implementation. The workflow encompasses five computational modules to perform antigen selection, antibody library generation, antigen and antibody structure modeling, antigen-antibody interaction modeling, structure analysis, and consensus ranking of potential antibody sequences for synthesis and experimental validation. The proposed workflow has been demonstrated through design of rAbs for the ovarian cancer antigen Mucin-16 (CA-125). This approach can serve as a blueprint for designing similar engineered molecules targeting other biomarkers, allowing for a simplified adaptation to different cancer types or disease-specific antigens.

Identification of heparin-binding amino acid residues in antibody HS4C3 with the potential to design antibodies against heparan sulfate domains

Heparan sulfate (HS) is a linear polysaccharide with high structural and functional diversity. Detection and localization of HS in tissues can be performed using single chain variable fragment (scFv) antibodies. Although several anti-HS antibodies recognizing different sulfation motifs have been identified, little is known about their interaction with HS. In this study the interaction between the scFv antibody HS4C3 and heparin was investigated. Heparin-binding lysine and arginine residues were identified using a protect and label methodology. Site-directed mutagenesis was applied to further identify critical heparin-binding lysine/arginine residues using immunohistochemical and biochemical assays. In addition, computational docking of a heparin tetrasaccharide towards a 3-D homology model of HS4C3 was applied to identify potential heparin-binding sites. Of the 12 lysine and 15 arginine residues within the HS4C3 antibody, 6 and 9, respectively, were identified as heparin-binding. Most of these residues are located within one of the complementarity determining regions (CDR) or in their proximity. All basic amino acid residues in the CDR3 region of the heavy chain were involved in binding. Computational docking showed a heparin tetrasaccharide close to these regions. Mutagenesis of heparin-binding residues reduced or altered reactivity towards HS and heparin. Identification of heparin-binding arginine and lysine residues in HS4C3 allows for better understanding of the interaction with HS and creates a framework to rationally design antibodies targeting specific HS motifs.

Structural, Biophysical, and Computational Studies of a Murine Light Chain Dimer

Antibodies are widely used in medicinal and scientific research due to their ability to bind to a specific antigen. Most often, antibodies are composed of heavy and light chain domains. Under physiological conditions, light chains are produced in excess, as compared to the heavy chain. It is now known that light chains are not silent partners of the heavy chain and can modulate the immune response independently. In this work, the first crystal structure of a light chain dimer originating from mice is described. It represents the light chain dimer of 6A8, a monoclonal antibody specific to the allergen Der f 1. Building on the unexpected occurrence of this kind of dimer, we have demonstrated that this light chain is stable in solution alone. Moreover, enzyme-linked immunosorbent assays (ELISA) have revealed that, when the light chain is not partnered to its corresponding heavy chain, it interacts non-specifically with a wide range of proteins. Computational studies were used to provide insight on the role of the 6A8 heavy chain domain in the specific binding to Der f 1. Overall, this work demonstrates and supports the ongoing notion that light chains can function by themselves and are not silent partners of heavy chains.

Elucidating key determinants of engineered scFv antibody in MMP-9 binding using high throughput screening and machine learning

An imbalance in matrix metalloproteinase-9 (MMP-9) regulation can lead to numerous diseases, including neurological disorders, cancer, and pre-term labor. Engineering single-chain antibody fragments (scFvs) Targeting MMP-9 to develop novel therapeutics for such diseases is desirable. We screened a synthetic scFv antibody library displayed on the yeast surface for binding improvement to MMP-9 using FACS (fluorescent-activated cell sorting). The scFv antibody clones isolated after FACS showed improvement in binding to MMP-9 compared to the endogenous inhibitor. To understand molecular determinants of binding between engineered scFv antibody variants and MMP-9, next-generation DNA sequencing, and computational protein structure analysis were used. Additionally, a deep-learning language model was trained on the synthetic library to predict the binding of scFv variants using their CDR-H3 sequences.

Immunoglobulin and T cell receptor repertoire changes induced by a prototype vaccine against Chagas disease in naïve rhesus macaques

BACKGROUND

A vaccine against Trypanosoma cruzi, the agent of Chagas disease, would be an excellent additional tool for disease control. A recombinant vaccine based on Tc24 and TSA1 parasite antigens was found to be safe and immunogenic in naïve macaques.

METHODS

We used RNA-sequencing and performed a transcriptomic analysis of PBMC responses to vaccination of naïve macaques after each vaccine dose, to shed light on the immunogenicity of this vaccine and guide the optimization of doses and formulation. We identified differentially expressed genes and pathways and characterized immunoglobulin and T cell receptor repertoires.

RESULTS

RNA-sequencing analysis indicated a clear transcriptomic response of PBMCs after three vaccine doses, with the up-regulation of several immune cell activation pathways and a broad non-polarized immune profile. Analysis of the IgG repertoire showed that it had a rapid turnover with novel IgGs produced following each vaccine dose, while the TCR repertoire presented several persisting clones that were expanded after each vaccine dose.

CONCLUSIONS

These data suggest that three vaccine doses may be needed for optimum immunogenicity and support the further evaluation of the protective efficacy of this vaccine.

Inferring B Cell Phylogenies from Paired H and L Chain BCR Sequences with Dowser

Abs are vital to human immune responses and are composed of genetically variable H and L chains. These structures are initially expressed as BCRs. BCR diversity is shaped through somatic hypermutation and selection during immune responses. This evolutionary process produces B cell clones, cells that descend from a common ancestor but differ by mutations. Phylogenetic trees inferred from BCR sequences can reconstruct the history of mutations within a clone. Until recently, BCR sequencing technologies separated H and L chains, but advancements in single-cell sequencing now pair H and L chains from individual cells. However, it is unclear how these separate genes should be combined to infer B cell phylogenies. In this study, we investigated strategies for using paired H and L chain sequences to build phylogenetic trees. We found that incorporating L chains significantly improved tree accuracy and reproducibility across all methods tested. This improvement was greater than the difference between tree-building methods and persisted even when mixing bulk and single-cell sequencing data. However, we also found that many phylogenetic methods estimated significantly biased branch lengths when some L chains were missing, such as when mixing single-cell and bulk BCR data. This bias was eliminated using maximum likelihood methods with separate branch lengths for H and L chain gene partitions. Thus, we recommend using maximum likelihood methods with separate H and L chain partitions, especially when mixing data types. We implemented these methods in the R package Dowser: https://dowser.readthedocs.io.

Development of a new affinity maturation protocol for the construction of an internalizing anti-nucleolin antibody library

Over the last decades, monoclonal antibodies have substantially improved the treatment of several conditions. The continuous search for novel therapeutic targets and improvements in antibody's structure, demands for a constant optimization of their development. In this regard, modulation of an antibody's affinity to its target has been largely explored and culminated in the discovery and optimization of a variety of molecules. It involves the creation of antibody libraries and selection against the target of interest. In this work, we aimed at developing a novel protocol to be used for the affinity maturation of an antibody previously developed by our group. An antibody library was constructed using an in vivo random mutagenesis approach that, to our knowledge, has not been used before for antibody development. Then, a cell-based phage display selection protocol was designed to allow the fast and simple screening of antibody clones capable of being internalized by target cells. Next generation sequencing coupled with computer analysis provided an extensive characterization of the created library and post-selection pool, that can be used as a guide for future antibody development. With a single selection step, an enrichment in the mutated antibody library, given by a decrease in almost 50% in sequence diversity, was achieved, and structural information useful in the study of the antibody-target interaction in the future was obtained.

Proteomics Platform Reveals Broad-Spectrum Nanobodies for SARS-CoV-2 Variant Neutralization

The ongoing evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the emergence of different variants of concerns with immune evasion that have been prevalent over the past three years. Nanobodies, the functional variable regions of camelid heavy-chain-only antibodies, have garnered interest in developing neutralizing antibodies due to their smaller size, structural stability, ease of production, high affinity, and low immunogenicity, among other characteristics. In this work, we describe an integrated proteomics platform for the high-throughput screening of nanobodies against different SARS-CoV-2 spike variants. To demonstrate this platform, we immunized a camel with subunit 1 (S1) of the wild-type spike protein and constructed a nanobody phage library. The binding and neutralizing activities of the nanobodies against 72 spike variants were then measured, resulting in the identification of two nanobodies (C-282 and C-39) with broad neutralizing activity against six non-Omicron variants (D614G, Alpha, Beta, Gamma, Delta, Kappa) and five Omicron variants (BA.1-5). Their neutralizing capability was validated using in vitro pseudovirus-based neutralization assays. All these results demonstrate the utility of our proteomics platform to identify new nanobodies with broad neutralizing capability and to develop a treatment for patients with SARS-CoV-2 variant infection in the future.

Computational Modeling and Characterization of Peptides Derived from Nanobody Complementary-Determining Region 2 (CDR2) Targeting Active-State Conformation of the β2-Adrenergic Receptor (β2AR)

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 β2-adrenergic receptor (β2AR) conformation. Sequences of conformationally selective Nbs favoring the agonist-occupied β2AR were initially analyzed by the informational spectrum method (ISM). The derived NDPs in complex with β2AR 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 β2AR:P4 complex (ΔG = -6.8 ± 0.8 kcal/mol or a Ki = 16.5 μM at 310 K) and mapped the β2AR: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 β2AR in the cAMP assay, and (iv) cannot reduce the efficacy and potency of the isoproterenol-mediated β2AR/β-arrestin-2 interaction in the BRET2-based recruitment assay. In summary, the CDR2-NDP, P4, binds preferentially to agonist-activated β2AR and disrupts Gαs-mediated signaling.

Loss of TET2 increases B-1 cell number and IgM production while limiting CDR3 diversity

Recent studies have demonstrated a role for Ten-Eleven Translocation-2 (TET2), an epigenetic modulator, in regulating germinal center formation and plasma cell differentiation in B-2 cells, yet the role of TET2 in regulating B-1 cells is largely unknown. Here, B-1 cell subset numbers, IgM production, and gene expression were analyzed in mice with global knockout of TET2 compared to wildtype (WT) controls. Results revealed that TET2-KO mice had elevated numbers of B-1a and B-1b cells in their primary niche, the peritoneal cavity, as well as in the bone marrow (B-1a) and spleen (B-1b). Consistent with this finding, circulating IgM, but not IgG, was elevated in TET2-KO mice compared to WT. Analysis of bulk RNASeq of sort purified peritoneal B-1a and B-1b cells revealed reduced expression of heavy and light chain immunoglobulin genes, predominantly in B-1a cells from TET2-KO mice compared to WT controls. As expected, the expression of IgM transcripts was the most abundant isotype in B-1 cells. Yet, only in B-1a cells there was a significant increase in the proportion of IgM transcripts in TET2-KO mice compared to WT. Analysis of the CDR3 of the BCR revealed an increased abundance of replicated CDR3 sequences in B-1 cells from TET2-KO mice, which was more clearly pronounced in B-1a compared to B-1b cells. V-D-J usage and circos plot analysis of V-J combinations showed enhanced usage of VH11 and VH12 pairings. Taken together, our study is the first to demonstrate that global loss of TET2 increases B-1 cell number and IgM production and reduces CDR3 diversity, which could impact many biological processes and disease states that are regulated by IgM.

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.

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.

Precision engineering for localization, validation, and modification of allergenic epitopes

The allergen-IgE interaction is essential for the genesis of allergic responses, yet investigation of the molecular basis of these interactions is in its infancy. Precision engineering has unveiled the molecular features of allergen-antibody interactions at the atomic level. High-resolution technologies, including x-ray crystallography, nuclear magnetic resonance spectroscopy, and cryo-electron microscopy, determine allergen-antibody structures. X-ray crystallography of an allergen-antibody complex localizes in detail amino acid residues and interactions that define the epitope-paratope interface. Multiple structures involving murine IgG mAbs have recently been resolved. The number of amino acids forming the epitope broadly correlates with the epitope area. The production of human IgE mAbs from B cells of allergic subjects is an exciting recent development that has for the first time enabled an actual IgE epitope to be defined. The biologic activity of defined IgE epitopes can be validated in vivo in animal models or by measuring mediator release from engineered basophilic cell lines. Finally, gene-editing approaches using the Clustered Regularly Interspaced Short Palindromic Repeats technology to either remove allergen genes or make targeted epitope engineering at the source are on the horizon. This review presents an overview of the identification and validation of allergenic epitopes by precision engineering.