Germline-encoded amino acid-binding motifs drive immunodominant public antibody responses

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.

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.

Enzyme-activating B-cell receptors boost antigen presentation to pathogenic T cells in gluten-sensitive autoimmunity

Autoantibodies against the enzyme transglutaminase 3 (TG3) are characteristic to the gluten-sensitive skin disorder dermatitis herpetiformis (DH), which is an extraintestinal manifestation of celiac disease. We here demonstrate that TG3-specific B cells can activate gluten-specific CD4+ T cells through B-cell receptor (BCR)-mediated internalization of TG3-gluten enzyme-substrate complexes. Stereotypic anti-TG3 antibodies using IGHV2-5/IGKV4-1 gene segments enhance the catalytic activity of TG3, and this effect translates into increased gluten presentation to T cells when such antibodies are expressed as BCRs. The crystal structure of TG3 bound to an IGHV2-5/IGKV4-1 Fab shows that antibody binding to a β-sheet in the catalytic core domain causes the enzyme to adopt the active conformation. This mechanism explains the production of stereotypic anti-TG3 autoantibodies in DH and highlights a role for TG3-specific B cells as antigen-presenting cells for gluten-specific T cells. Similar boosting effects of autoreactive BCRs could be relevant for other autoimmune diseases, including rheumatoid arthritis.

Broadly neutralizing antibodies targeting pandemic GII.4 variants or seven GII genotypes of human norovirus

Human norovirus causes more than 700 million illnesses annually. Extensive genetic diversity and a paucity of information on conserved neutralizing epitopes pose major obstacles to the design of broadly protective norovirus immunogens. Here, we used high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS)-driven proteomics to quantitatively characterize the circulating serum IgG repertoire before and after immunization with an experimental monovalent norovirus GII.4 VP1 capsid-encoding adenoviral vaccine. Two participants were specifically selected on the basis of the breadth of serum neutralization responses either across GII.4 variants (participant A) or across GII genotypes (participant B). In participant A, vaccination back-boosted highly abundant serum antibody clonotypes targeting epitopes conserved among rapidly evolving GII.4 variants spanning from a strain identified in 1987 to a strain identified in 2019. In participant B, we identified a recall response consisting of broadly neutralizing monoclonal antibodies with remarkable cross-GII ligand-binding blockade (blocking ≥ seven GII genotypes) and virus neutralization breadth. The cocrystal structure of one of these antibodies, VX22, in complex with the VP1 capsid protruding (P) domain revealed a highly conserved epitope (residues 479 to 484 and 509 to 513) within two lateral loops of the P1 subdomain. Antibody evolutionary trajectory analysis further revealed that VX22 had originally evolved from an early heterologous infection, likely by a GII.12 strain. Together, our study demonstrates that norovirus human monoclonal antibodies with broad GII.4 potency and cross-GII breadth can be boosted in serum after immunization with an adenoviral vector-based vaccine, findings that may guide the design of immunogens for broadly protective norovirus vaccines.

Atomically accurate de novo design of antibodies with RFdiffusion

Despite the central role that antibodies play in modern medicine, there is currently no method to design novel antibodies that bind a specific epitope entirely in silico. Instead, antibody discovery currently relies on animal immunization or random library screening approaches. Here, we demonstrate that combining computational protein design using a fine-tuned RFdiffusion network alongside yeast display screening enables the generation of antibody variable heavy chains (VHHs) and single chain variable fragments (scFvs) that bind user-specified epitopes with atomic-level precision. To verify this, we experimentally characterized VHH binders to four disease-relevant epitopes using multiple orthogonal biophysical methods, including cryo-EM, which confirmed the proper Ig fold and binding pose of designed VHHs targeting influenza hemagglutinin and Clostridium difficile toxin B (TcdB). For the influenza-targeting VHH, high-resolution structural data further confirmed the accuracy of CDR loop conformations. While initial computational designs exhibit modest affinity, affinity maturation using OrthoRep enables production of single-digit nanomolar binders that maintain the intended epitope selectivity. We further demonstrate the de novo design of single-chain variable fragments (scFvs), creating binders to TcdB and a Phox2b peptide-MHC complex by combining designed heavy and light chain CDRs. Cryo-EM structural data confirmed the proper Ig fold and binding pose for two distinct TcdB scFvs, with high-resolution data for one design additionally verifying the atomically accurate conformations of all six CDR loops. Our approach establishes a framework for the rational computational design, screening, isolation, and characterization of fully de novo antibodies with atomic-level precision in both structure and epitope targeting.

Epstein-Barr virus BALF0/1 subverts the Caveolin and ERAD pathways to target B cell receptor complexes for degradation

Epstein-Barr virus (EBV) establishes persistent infection, causes infectious mononucleosis, is a major trigger for multiple sclerosis and contributes to multiple cancers. Yet, knowledge remains incomplete about how the virus remodels host B cells to support lytic replication. We previously identified that EBV lytic replication results in selective depletion of plasma membrane (PM) B cell receptor (BCR) complexes, composed of immunoglobulin and the CD79A and CD79B signaling chains. Here, we used proteomic and biochemical approaches to identify that the EBV early lytic protein BALF0/1 is responsible for EBV lytic cycle BCR degradation. Mechanistically, an immunoglobulin heavy chain (HC) cytoplasmic tail KVK motif was required for ubiquitin-mediated BCR degradation, while CD79A and CD79B were dispensable. BALF0/1 subverted caveolin-mediated endocytosis to internalize PM BCR complexes and to deliver them to the endoplasmic reticulum. BALF0/1 stimulated immunoglobulin HC cytoplasmic tail ubiquitination, which together with the ATPase valosin-containing protein/p97 drove ER-associated degradation of BCR complexes by cytoplasmic proteasomes. BALF0/1 knockout reduced the viral load of secreted EBV particles from B cells that expressed a monoclonal antibody against EBV glycoprotein 350 but not a control anti-influenza hemagglutinin antibody and increased viral particle immunoglobulin incorporation. Consistent with downmodulation of PM BCR, BALF0/1 overexpression reduced viability of a diffuse large B cell lymphoma cell line whose survival is dependent upon BCR signaling. Collectively, our results suggest that EBV BALF0/1 downmodulates immunoglobulin upon lytic reactivation to block BCR signaling and support virion release, but await the development of suitable models to test its roles in EBV reactivation in vivo.

Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning

Machine learning (ML) has shown great potential in the adaptive immune receptor repertoire (AIRR) field. However, there is a lack of large-scale ground-truth experimental AIRR data suitable for AIRR-ML-based disease diagnostics and therapeutics discovery. Simulated ground-truth AIRR data are required to complement the development and benchmarking of robust and interpretable AIRR-ML methods where experimental data is currently inaccessible or insufficient. The challenge for simulated data to be useful is incorporating key features observed in experimental repertoires. These features, such as antigen or disease-associated immune information, cause AIRR-ML problems to be challenging. Here, we introduce LIgO, a software suite, which simulates AIRR data for the development and benchmarking of AIRR-ML methods. LIgO incorporates different types of immune information both on the receptor and the repertoire level and preserves native-like generation probability distribution. Additionally, LIgO assists users in determining the computational feasibility of their simulations. We show two examples where LIgO supports the development and validation of AIRR-ML methods: (i) how individuals carrying out-of-distribution immune information impacts receptor-level prediction performance and (ii) how immune information co-occurring in the same AIRs impacts the performance of conventional receptor-level encoding and repertoire-level classification approaches. LIgO guides the advancement and assessment of interpretable AIRR-ML methods.

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.

Structural trends in antibody-antigen binding interfaces: a computational analysis of 1833 experimentally determined 3D structures

Antibodies are attractive therapeutic candidates due to their ability to bind cognate antigens with high affinity and specificity. Still, the underlying molecular rules governing the antibody-antigen interface remain poorly understood, making in silico antibody design inherently difficult and keeping the discovery and design of novel antibodies a costly and laborious process. This study investigates the characteristics of antibody-antigen binding interfaces through a computational analysis of more than 850,000 atom-atom contacts from the largest reported set of antibody-antigen complexes with 1833 nonredundant, experimentally determined structures. The analysis compares binding characteristics of conventional antibodies and single-domain antibodies (sdAbs) targeting both protein- and peptide antigens. We find clear patterns in the number antibody-antigen contacts and amino acid frequencies in the paratope. The direct comparison of sdAbs and conventional antibodies helps elucidate the mechanisms employed by sdAbs to compensate for their smaller size and the fact that they harbor only half the number of complementarity-determining regions compared to conventional antibodies. Furthermore, we pinpoint antibody interface hotspot residues that are often found at the binding interface and the amino acid frequencies at these positions. These findings have direct potential applications in antibody engineering and the design of improved antibody libraries.

Induction of IGHV3-53 public antibodies with broadly neutralising activity against SARS-CoV-2 including Omicron subvariants in a Delta breakthrough infection case

BACKGROUND

Emergence of SARS-CoV-2 variants that escape neutralising antibodies hampers the development of vaccines and therapeutic antibodies against SARS-CoV-2. IGHV3-53/3-66-derived public antibodies, which are generally specific to the prototype virus and are frequently induced in infected or vaccinated individuals, show minimal affinity maturation and high potency against prototype SARS-CoV-2.

METHODS

Monoclonal antibodies isolated from a Delta breakthrough infection case were analysed for cross-neutralising activities against SARS-CoV-2 variants. The broadly neutralising antibody K4-66 was further analysed in a hamster model, and the effect of somatic hypermutations was assessed using the inferred germline precursor.

FINDINGS

Antibodies derived from IGHV3-53/3-66 showed broader neutralising activity than antibodies derived from IGHV1-69 and other IGHV genes. IGHV3-53/3-66 antibodies neutralised the Delta variant better than the IGHV1-69 antibodies, suggesting that the IGHV3-53/3-66 antibodies were further maturated by Delta breakthrough infection. One IGHV3-53/3-66 antibody, K4-66, neutralised all Omicron subvariants tested, including EG.5.1, BA.2.86, and JN.1, and decreased the viral load in the lungs of hamsters infected with Omicron subvariant XBB.1.5. The importance of somatic hypermutations was demonstrated by the loss of neutralising activity of the inferred germline precursor of K4-66 against Beta and Omicron variants.

INTERPRETATION

Broadly neutralising IGHV3-53/3-66 antibodies have potential as a target for the development of effective vaccines and therapeutic antibodies against newly emerging SARS-CoV-2 variants.

FUNDING

This work was supported by grants from AMED (JP23ym0126048, JP22ym0126048, JP21ym0126048, JP23wm0125002, JP233fa627001, JP223fa627009, JP24jf0126002, and JP22fk0108572), and the JSPS (JP21H02970, JK23K20041, and JPJSCCA20240006).

Engineering immunogens that select for specific mutations in HIV broadly neutralizing antibodies

Vaccine development targeting rapidly evolving pathogens such as HIV-1 requires induction of broadly neutralizing antibodies (bnAbs) with conserved paratopes and mutations, and in some cases, the same Ig-heavy chains. The current trial-and-error search for immunogen modifications that improve selection for specific bnAb mutations is imprecise. Here, to precisely engineer bnAb boosting immunogens, we use molecular dynamics simulations to examine encounter states that form when antibodies collide with the HIV-1 Envelope (Env). By mapping how bnAbs use encounter states to find their bound states, we identify Env mutations predicted to select for specific antibody mutations in two HIV-1 bnAb B cell lineages. The Env mutations encode antibody affinity gains and select for desired antibody mutations in vivo. These results demonstrate proof-of-concept that Env immunogens can be designed to directly select for specific antibody mutations at residue-level precision by vaccination, thus demonstrating the feasibility of sequential bnAb-inducing HIV-1 vaccine design.

The Janus (dual) model of immunoglobulin isotype evolution: Conservation and plasticity are the defining paradigms

The study of antibodies in jawed vertebrates (gnathostomes) provides every immunologist with a bird's eye view of how human immunoglobulins (Igs) came into existence and subsequently evolved into their present forms. It is a fascinating Darwinian history of conservation on the one hand and flexibility on the other, exemplified by the Ig heavy chain (H) isotypes IgM and IgD/W, respectively. The cartilaginous fish (e.g., sharks) Igs provide a glimpse of "how everything got off the ground," while the amphibians (e.g., the model Xenopus) reveal how the adaptive immune system made an about face with the emergence of Ig isotype switching and IgG-like structure/function. The evolution of mucosal Igs is a captivating account of malleability, convergence, and conservation, and a call to arms for future study! In between there are spellbinding chronicles of antibody evolution in each class of vertebrates and rather incredible stories of how antibodies can adapt to occupy niches, for example, single-domain variable regions, cold-adapted Igs, convergent mechanisms to dampen antibody function, provision of mucosal defense, and many more. The purpose here is not to provide an encyclopedic examination of antibody evolution, but rather to hit the high points and entice readers to appreciate how things "came to be."

Engineering a SARS-CoV-2 Vaccine Targeting the Receptor-Binding Domain Cryptic-Face via Immunofocusing

The receptor-binding domain (RBD) of the SARS-CoV-2 spike protein is the main target of neutralizing antibodies. Although they are infrequently elicited during infection or vaccination, antibodies that bind to the conformation-specific cryptic face of the RBD display remarkable breadth of binding and neutralization across Sarbecoviruses. Here, we employed the immunofocusing technique PMD (protect, modify, deprotect) to create RBD immunogens (PMD-RBD) specifically designed to focus the antibody response toward the cryptic-face epitope recognized by the broadly neutralizing antibody S2X259. Immunization with PMD-RBD antigens induced robust binding titers and broad neutralizing activity against homologous and heterologous Sarbecovirus strains. A serum-depletion assay provided direct evidence that PMD successfully skewed the polyclonal antibody response toward the cryptic face of the RBD. Our work demonstrates the ability of PMD to overcome immunodominance and refocus humoral immunity, with implications for the development of broader and more resilient vaccines against current and emerging viruses with pandemic potential.

Antibody sequence determinants of viral antigen specificity

Throughout life, humans experience repeated exposure to viral antigens through infection and vaccination, resulting in the generation of diverse, antigen-specific antibody repertoires. A paramount feature of antibodies that enables their critical contributions in counteracting recurrent and novel pathogens, and consequently fostering their utility as valuable targets for therapeutic and vaccine development, is the exquisite specificity displayed against their target antigens. Yet, there is still limited understanding of the determinants of antibody-antigen specificity, particularly as a function of antibody sequence. In recent years, experimental characterization of antibody repertoires has led to novel insights into fundamental properties of antibody sequences but has been largely decoupled from at-scale antigen specificity analysis. Here, using the LIBRA-seq technology, we generated a large data set mapping antibody sequence to antigen specificity for thousands of B cells, by screening the repertoires of a set of healthy individuals against 20 viral antigens representing diverse pathogens of biomedical significance. Analysis uncovered virus-specific patterns in variable gene usage, gene pairing, somatic hypermutation, as well as the presence of convergent antiviral signatures across multiple individuals, including the presence of public antibody clonotypes. Notably, our results showed that, for B-cell receptors originating from different individuals but leveraging an identical combination of heavy and light chain variable genes, there is a specific CDRH3 identity threshold above which B cells appear to exclusively share the same antigen specificity. This finding provides a quantifiable measure of the relationship between antibody sequence and antigen specificity and further defines experimentally grounded criteria for defining public antibody clonality.IMPORTANCEThe B-cell compartment of the humoral immune system plays a critical role in the generation of antibodies upon new and repeated pathogen exposure. This study provides an unprecedented level of detail on the molecular characteristics of antibody repertoires that are specific to each of the different target pathogens studied here and provides empirical evidence in support of a 70% CDRH3 amino acid identity threshold in pairs of B cells encoded by identical IGHV:IGL(K)V genes, as a means of defining public clonality and therefore predicting B-cell antigen specificity in different individuals. This is of exceptional importance when leveraging public clonality as a method to annotate B-cell receptor data otherwise lacking antigen specificity information. Understanding the fundamental rules of antibody-antigen interactions can lead to transformative new approaches for the development of antibody therapeutics and vaccines against current and emerging viruses.

A public, cross-reactive glycoprotein epitope confounds Ebola virus serology

Ebola disease (EBOD) in humans is a severe disease caused by at least four related viruses in the genus Orthoebolavirus, most often by the eponymous Ebola virus. Due to human-to-human transmission and incomplete success in treating cases despite promising therapeutic development, EBOD is a high priority in public health research. Yet despite almost 50 years since EBOD was first described, the sources of these viruses remain undefined and much remains to be understood about the disease epidemiology and virus emergence and spread. One important approach to improve our understanding is detection of antibodies that can reveal past human infections. However, serosurveys routinely describe seroprevalences that imply infection rates much higher than those clinically observed. Proposed hypotheses to explain this difference include existence of common but less pathogenic strains or relatives of these viruses, misidentification of EBOD as something else, and a higher proportion of subclinical infections than currently appreciated. The work presented here maps B-cell epitopes in the spike protein of Ebola virus and describes a single epitope that is cross-reactive with an antigen seemingly unrelated to orthoebolaviruses. Antibodies against this epitope appear to explain most of the unexpected reactivity towards the spike, arguing against common but unidentified infections in the population. Importantly, antibodies of cross-reactive donors from within and outside the known EBOD geographic range bound the same epitope. In light of this finding, it is plausible that epitope mapping enables broadly applicable specificity improvements in the field of serology.

The Observed T Cell Receptor Space database enables paired-chain repertoire mining, coherence analysis, and language modeling

T cell activation is governed through T cell receptors (TCRs), heterodimers of two sequence-variable chains (often an α and β chain) that synergistically recognize antigen fragments presented on cell surfaces. Despite this, there only exist repositories dedicated to collecting single-chain, not paired-chain, TCR sequence data. We addressed this gap by creating the Observed TCR Space (OTS) database, a source of consistently processed and annotated, full-length, paired-chain TCR sequences. Currently, OTS contains 5.35 million redundant (1.63 million non-redundant), predominantly human sequences from across 50 studies and at least 75 individuals. Using OTS, we identify pairing biases, public TCRs, and distinct chain coherence patterns relative to antibodies. We also release a paired-chain TCR language model, providing paired embedding representations and a method for residue in-filling conditional on the partner chain. OTS will be updated as a central community resource and is freely downloadable and available as a web application.

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.

Systems vaccinology studies - achievements and future potential

Human immune responses to vaccination are variable both within and between populations. Systems vaccinology, which is the application of multi-omics technologies to vaccine studies, seeks to understand such variation and predict responses to optimise vaccine strategies. Here, we outline new approaches to systems vaccinology, focusing on the incorporation of additional cohorts, endpoints and technologies.

Phage Immunoprecipitation and Sequencing-a Versatile Technique for Mapping the Antibody Reactome

Characterizing the antibody reactome for circulating antibodies provide insight into pathogen exposure, allergies, and autoimmune diseases. This is important for biomarker discovery, clinical diagnosis, and prognosis of disease progression, as well as population-level insights into the immune system. The emerging technology phage display immunoprecipitation and sequencing (PhIP-seq) is a high-throughput method for identifying antigens/epitopes of the antibody reactome. In PhIP-seq, libraries with sequences of defined lengths and overlapping segments are bioinformatically designed using naturally occurring proteins and cloned into phage genomes to be displayed on the surface. These libraries are used in immunoprecipitation experiments of circulating antibodies. This can be done with parallel samples from multiple sources, and the DNA inserts from the bound phages are barcoded and subjected to next-generation sequencing for hit determination. PhIP-seq is a powerful technique for characterizing the antibody reactome that has undergone rapid advances in recent years. In this review, we comprehensively describe the history of PhIP-seq and discuss recent advances in library design and applications.

Resolving haplotype variation and complex genetic architecture in the human immunoglobulin kappa chain locus in individuals of diverse ancestry

Immunoglobulins (IGs), critical components of the human immune system, are composed of heavy and light protein chains encoded at three genomic loci. The IG Kappa (IGK) chain locus consists of two large, inverted segmental duplications. The complexity of the IG loci has hindered use of standard high-throughput methods for characterizing genetic variation within these regions. To overcome these limitations, we use long-read sequencing to create haplotype-resolved IGK assemblies in an ancestrally diverse cohort (n = 36), representing the first comprehensive description of IGK haplotype variation. We identify extensive locus polymorphism, including novel single nucleotide variants (SNVs) and novel structural variants harboring functional IGKV genes. Among 47 functional IGKV genes, we identify 145 alleles, 67 of which were not previously curated. We report inter-population differences in allele frequencies for 10 IGKV genes, including alleles unique to specific populations within this dataset. We identify haplotypes carrying signatures of gene conversion that associate with SNV enrichment in the IGK distal region, and a haplotype with an inversion spanning the proximal and distal regions. These data provide a critical resource of curated genomic reference information from diverse ancestries, laying a foundation for advancing our understanding of population-level genetic variation in the IGK locus.

Multimodal Affinity-Modulated Efficient Separation of Lysozyme with a Hierarchical MXene@MOF Hybrid Framework

The development of abiotic protein affinity adsorbents remains challenging for the accurate acquisition and analysis of specific protein species. Inspired by bacterial cell walls, a hierarchical hybrid framework is fabricated through the oriented growth of an Fe-based metal organic framework (MOF) on V2C MXene for the efficient separation of lysozyme (Lys). After directed evolution of adsorptive materials, the MXene@MOF composite rich in hydroxyl groups (termed as MX@MOF-DH) is found exerting exceptional affinity for Lys. Benefiting from hydrogen-bonding, coordination, and electrostatic interaction-mediated multimodal and multivalent affinity, MX@MOF-DH reveals rapid adsorption rate (5 min), superb enrichment factor (83.1), and favorable binding capacity (609.7 mg g-1), which outperforms other latest adsorbents. Moreover, femtomolar sensitivity is achieved even in the presence of high-abundant interfering proteins, as confirmed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometer analysis. This work not only provides an efficient approach for selective enrichment of lysozyme but also paves an avenue to construct the protein affinity reagents for specific biological medicine and analysis applications.

Computational mining of B cell receptor repertoires reveals antigen-specific and convergent responses to Ebola vaccination

Outbreaks of Ebolaviruses, such as Sudanvirus (SUDV) in Uganda in 2022, demonstrate that species other than the Zaire ebolavirus (EBOV), which is currently the sole virus represented in current licensed vaccines, remain a major threat to global health. There is a pressing need to develop effective pan-species vaccines and novel monoclonal antibody-based therapeutics for Ebolavirus disease. In response to recent outbreaks, the two dose, heterologous Ad26.ZEBOV/MVA-BN-Filo vaccine regimen was developed and was tested in a large phase II clinical trial (EBL2001) as part of the EBOVAC2 consortium. Here, we perform bulk sequencing of the variable heavy chain (VH) of B cell receptors (BCR) in forty participants from the EBL2001 trial in order to characterize the BCR repertoire in response to vaccination with Ad26.ZEBOV/MVA-BN-Filo. We develop a comprehensive database, EBOV-AbDab, of publicly available Ebolavirus-specific antibody sequences. We then use our database to predict the antigen-specific component of the vaccinee repertoires. Our results show striking convergence in VH germline gene usage across participants following the MVA-BN-Filo dose, and provide further evidence of the role of IGHV3-15 and IGHV3-13 antibodies in the B cell response to Ebolavirus glycoprotein. Furthermore, we found that previously described Ebola-specific mAb sequences present in EBOV-AbDab were sufficient to describe at least one of the ten most expanded BCR clonotypes in more than two thirds of our cohort of vaccinees following the boost, providing proof of principle for the utility of computational mining of immune repertoires.

Engineering a SARS-CoV-2 vaccine targeting the RBD cryptic-face via immunofocusing

The receptor-binding domain (RBD) of the SARS-CoV-2 spike protein is the main target of neutralizing antibodies. Although they are infrequently elicited during infection or vaccination, antibodies that bind to the conformation-specific cryptic face of the RBD display remarkable breadth of binding and neutralization across Sarbecoviruses. Here, we employed the immunofocusing technique PMD (protect, modify, deprotect) to create RBD immunogens (PMD-RBD) specifically designed to focus the antibody response towards the cryptic-face epitope recognized by the broadly neutralizing antibody S2X259. Immunization with PMD-RBD antigens induced robust binding titers and broad neutralizing activity against homologous and heterologous Sarbecovirus strains. A serum-depletion assay provided direct evidence that PMD successfully skewed the polyclonal antibody response towards the cryptic face of the RBD. Our work demonstrates the ability of PMD to overcome immunodominance and refocus humoral immunity, with implications for the development of broader and more resilient vaccines against current and emerging viruses with pandemic potential.

Linguistics-based formalization of the antibody language as a basis for antibody language models

Apparent parallels between natural language and antibody sequences have led to a surge in deep language models applied to antibody sequences for predicting cognate antigen recognition. However, a linguistic formal definition of antibody language does not exist, and insight into how antibody language models capture antibody-specific binding features remains largely uninterpretable. Here we describe how a linguistic formalization of the antibody language, by characterizing its tokens and grammar, could address current challenges in antibody language model rule mining.

Selective activation of naïve B cells with unique epitope specificity shapes autoantibody formation in celiac disease

Many antibody responses induced by infection, vaccination or autoimmunity show signs of convergence across individuals with epitope-dependent selection of particular variable region gene segments and complementarity determining region 3 properties. However, not much is known about the relationship between antigen-specific effector cells and antigen-specific precursors present in the naïve B-cell repertoire. Here, we sought to address this relationship in the context of celiac disease, where there is a stereotyped autoantibody response against the enzyme transglutaminase 2 (TG2). By generating TG2-specific monoclonal antibodies from both duodenal plasma cells and circulating naïve B cells, we demonstrate a discord between the naïve TG2-specific repertoire and the cells that are selected for autoantibody production. Hence, the naïve repertoire does not fully reflect the epitope preference and gene usage observed for memory B cells and plasma cells. Instead, distinct naïve B cells that target particular TG2 epitopes appear to be selectively activated at the expense of TG2-binding B cells targeting other epitopes.

Potent HPIV3-neutralizing IGHV5-51 Antibodies Identified from Multiple Individuals Show L Chain and CDRH3 Promiscuity

Human parainfluenza virus 3 (HPIV3) is a widespread pathogen causing severe and lethal respiratory illness in at-risk populations. Effective countermeasures are in various stages of development; however, licensed therapeutic and prophylactic options are not available. The fusion glycoprotein (HPIV3 F), responsible for facilitating viral entry into host cells, is a major target of neutralizing Abs that inhibit infection. Although several neutralizing Abs against a small number of HPIV3 F epitopes have been identified to date, relatively little is known about the Ab response to HPIV3 compared with other pathogens, such as influenza virus and SARS-CoV-2. In this study, we aimed to characterize a set of HPIV3-specific Abs identified in multiple individuals for genetic signatures, epitope specificity, neutralization potential, and publicness. We identified 12 potently neutralizing Abs targeting three nonoverlapping epitopes on HPIV3 F. Among these, six Abs identified from two different individuals used Ig heavy variable gene IGHV 5-51, with five of the six Abs targeting the same epitope. However, despite the use of the same H chain variable (VH) gene, these Abs used multiple different L chain variable genes (VL) and diverse H chain CDR 3 (CDRH3) sequences. Together, these results provide further information about the genetic and functional characteristics of HPIV3-neutralizing Abs and suggest the existence of a reproducible VH-dependent Ab response associated with VL and CDRH3 promiscuity. Understanding sites of HPIV3 F vulnerability and the genetic and molecular characteristics of Abs targeting these sites will help guide efforts for effective vaccine and therapeutic development.

Adaptive immune receptor germline gene variation

Recognition of antigens by T cell receptors (TCRs) and B cell receptors (BCRs) is a key step in lymphocyte activation. T and B cells mediate adaptive immune responses, which protect us against infections and provide immunological memory, and also, in some instances, drive pathogenic responses in autoimmune diseases. TCRs and BCRs are encoded within loci that are known to be genetically diverse. However, the extent and functional impact of this variation, both in humans and model animals used in immunological research, remain largely unknown. Experimental and genetic evidence has demonstrated that the complementarity determining regions 1 and 2 (HCDR1 and HCDR2), encoded by the variable (V) region of TCRs and BCRs, also often make critical contacts with the targeted antigen. Thus, knowledge about allelic variation in the genes encoding TCRs and BCRs is critically important for understanding adaptive immune responses in outbred populations and to define responder and non-responder phenotypes.

Innovations and trends in antibody repertoire analysis

Monoclonal antibodies have revolutionized the treatment of human diseases, which has made them the fastest-growing class of therapeutics, with global sales expected to reach $346.6 billion USD by 2028. Advances in antibody engineering and development have led to the creation of increasingly sophisticated antibody-based therapeutics (e.g. bispecific antibodies and chimeric antigen receptor T cells). However, approaches for antibody discovery have remained comparatively grounded in conventional yet reliable in vitro assays. Breakthrough developments in high-throughput single B-cell sequencing and immunoglobulin proteomic serology, however, have enabled the identification of high-affinity antibodies directly from endogenous B cells or circulating immunoglobulin produced in vivo. Moreover, advances in artificial intelligence offer vast potential for antibody discovery and design with large-scale repertoire datasets positioned as the optimal source of training data for such applications. We highlight advances and recent trends in how these technologies are being applied to antibody repertoire analysis.

Efficient encoding of large antigenic spaces by epitope prioritization with Dolphyn

We investigate a relatively underexplored component of the gut-immune axis by profiling the antibody response to gut phages using Phage Immunoprecipitation Sequencing (PhIP-Seq). To cover large antigenic spaces, we develop Dolphyn, a method that uses machine learning to select peptides from protein sets and compresses the proteome through epitope-stitching. Dolphyn compresses the size of a peptide library by 78% compared to traditional tiling, increasing the antibody-reactive peptides from 10% to 31%. We find that the immune system develops antibodies to human gut bacteria-infecting viruses, particularly E.coli-infecting Myoviridae. Cost-effective PhIP-Seq libraries designed with Dolphyn enable the assessment of a wider range of proteins in a single experiment, thus facilitating the study of the gut-immune axis.

Bringing immunofocusing into focus

Immunofocusing is a strategy to create immunogens that redirect humoral immune responses towards a targeted epitope and away from non-desirable epitopes. Immunofocusing methods often aim to develop "universal" vaccines that provide broad protection against highly variant viruses such as influenza virus, human immunodeficiency virus (HIV-1), and most recently, severe acute respiratory syndrome coronavirus (SARS-CoV-2). We use existing examples to illustrate five main immunofocusing strategies-cross-strain boosting, mosaic display, protein dissection, epitope scaffolding, and epitope masking. We also discuss obstacles for immunofocusing like immune imprinting. A thorough understanding, advancement, and application of the methods we outline here will enable the design of high-resolution vaccines that protect against future viral outbreaks.

Contextualising the developability risk of antibodies with lambda light chains using enhanced therapeutic antibody profiling

Antibodies with lambda light chains (λ-antibodies) are generally considered to be less developable than those with kappa light chains (κ-antibodies). Though this hypothesis has not been formally established, it has led to substantial systematic biases in drug discovery pipelines and thus contributed to kappa dominance amongst clinical-stage therapeutics. However, the identification of increasing numbers of epitopes preferentially engaged by λ-antibodies shows there is a functional cost to neglecting to consider them as potential lead candidates. Here, we update our Therapeutic Antibody Profiler (TAP) tool to use the latest data and machine learning-based structure prediction, and apply it to evaluate developability risk profiles for κ-antibodies and λ-antibodies based on their surface physicochemical properties. We find that while human λ-antibodies on average have a higher risk of developability issues than κ-antibodies, a sizeable proportion are assigned lower-risk profiles by TAP and should represent more tractable candidates for therapeutic development. Through a comparative analysis of the low- and high-risk populations, we highlight opportunities for strategic design that TAP suggests would enrich for more developable λ-antibodies. Overall, we provide context to the differing developability of κ- and λ-antibodies, enabling a rational approach to incorporate more diversity into the initial pool of immunotherapeutic candidates.

The Patent and Literature Antibody Database (PLAbDab): an evolving reference set of functionally diverse, literature-annotated antibody sequences and structures

Antibodies are key proteins of the adaptive immune system, and there exists a large body of academic literature and patents dedicated to their study and concomitant conversion into therapeutics, diagnostics, or reagents. These documents often contain extensive functional characterisations of the sets of antibodies they describe. However, leveraging these heterogeneous reports, for example to offer insights into the properties of query antibodies of interest, is currently challenging as there is no central repository through which this wide corpus can be mined by sequence or structure. Here, we present PLAbDab (the Patent and Literature Antibody Database), a self-updating repository containing over 150,000 paired antibody sequences and 3D structural models, of which over 65 000 are unique. We describe the methods used to extract, filter, pair, and model the antibodies in PLAbDab, and showcase how PLAbDab can be searched by sequence, structure, or keyword. PLAbDab uses include annotating query antibodies with potential antigen information from similar entries, analysing structural models of existing antibodies to identify modifications that could improve their properties, and facilitating the compilation of bespoke datasets of antibody sequences/structures that bind to a specific antigen. PLAbDab is freely available via Github (https://github.com/oxpig/PLAbDab) and as a searchable webserver (https://opig.stats.ox.ac.uk/webapps/plabdab/).

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.

Pan-viral serology uncovers distinct virome patterns as risk predictors of hepatocellular carcinoma and intrahepatic cholangiocarcinoma

This study evaluates the pan-serological profiles of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA) compared to several diseased and non-diseased control populations to identify risk factors and biomarkers of liver cancer. We used phage immunoprecipitation sequencing, an anti-viral antibody screening method using a synthetic-phage-displayed human virome epitope library, to screen patient serum samples for exposure to over 1,280 strains of pathogenic and non-pathogenic viruses. Using machine learning methods to develop an HCC or iCCA viral score, we discovered that both viral scores were positively associated with several liver function markers in two separate at-risk populations independent of viral hepatitis status. The HCC score predicted all-cause mortality over 8 years in patients with chronic liver disease at risk of HCC, while the viral hepatitis status was not predictive of survival. These results suggest that non-hepatitis viral infections may contribute to HCC and iCCA development and could be biomarkers in at-risk populations.

SARS-CoV-2 antibodies recognize 23 distinct epitopic sites on the receptor binding domain

The COVID-19 pandemic and SARS-CoV-2 variants have dramatically illustrated the need for a better understanding of antigen (epitope)-antibody (paratope) interactions. To gain insight into the immunogenic characteristics of epitopic sites (ES), we systematically investigated the structures of 340 Abs and 83 nanobodies (Nbs) complexed with the Receptor Binding Domain (RBD) of the SARS-CoV-2 spike protein. We identified 23 distinct ES on the RBD surface and determined the frequencies of amino acid usage in the corresponding CDR paratopes. We describe a clustering method for analysis of ES similarities that reveals binding motifs of the paratopes and that provides insights for vaccine design and therapies for SARS-CoV-2, as well as a broader understanding of the structural basis of Ab-protein antigen (Ag) interactions.

Improved computational epitope profiling using structural models identifies a broader diversity of antibodies that bind to the same epitope

The function of an antibody is intrinsically linked to the epitope it engages. Clonal clustering methods, based on sequence identity, are commonly used to group antibodies that will bind to the same epitope. However, such methods neglect the fact that antibodies with highly diverse sequences can exhibit similar binding site geometries and engage common epitopes. In a previous study, we described SPACE1, a method that structurally clustered antibodies in order to predict their epitopes. This methodology was limited by the inaccuracies and incomplete coverage of template-based modeling. In addition, it was only benchmarked at the level of domain-consistency on one virus class. Here, we present SPACE2, which uses the latest machine learning-based structure prediction technology combined with a novel clustering protocol, and benchmark it on binding data that have epitope-level resolution. On six diverse sets of antigen-specific antibodies, we demonstrate that SPACE2 accurately clusters antibodies that engage common epitopes and achieves far higher dataset coverage than clonal clustering and SPACE1. Furthermore, we show that the functionally consistent structural clusters identified by SPACE2 are even more diverse in sequence, genetic lineage, and species origin than those found by SPACE1. These results reiterate that structural data improve our ability to identify antibodies that bind to the same epitope, adding information to sequence-based methods, especially in datasets of antibodies from diverse sources. SPACE2 is openly available on GitHub (https://github.com/oxpig/SPACE2).

Using interpretable machine learning to extend heterogeneous antibody-virus datasets

A central challenge in biology is to use existing measurements to predict the outcomes of future experiments. For the rapidly evolving influenza virus, variants examined in one study will often have little to no overlap with other studies, making it difficult to discern patterns or unify datasets. We develop a computational framework that predicts how an antibody or serum would inhibit any variant from any other study. We validate this method using hemagglutination inhibition data from seven studies and predict 2,000,000 new values ± uncertainties. Our analysis quantifies the transferability between vaccination and infection studies in humans and ferrets, shows that serum potency is negatively correlated with breadth, and provides a tool for pandemic preparedness. In essence, this approach enables a shift in perspective when analyzing data from "what you see is what you get" into "what anyone sees is what everyone gets."

Germline-encoded specificities and the predictability of the B cell response

Antibodies result from the competition of B cell lineages evolving under selection for improved antigen recognition, a process known as affinity maturation. High-affinity antibodies to pathogens such as HIV, influenza, and SARS-CoV-2 are frequently reported to arise from B cells whose receptors, the precursors to antibodies, are encoded by particular immunoglobulin alleles. This raises the possibility that the presence of particular germline alleles in the B cell repertoire is a major determinant of the quality of the antibody response. Alternatively, initial differences in germline alleles' propensities to form high-affinity receptors might be overcome by chance events during affinity maturation. We first investigate these scenarios in simulations: when germline-encoded fitness differences are large relative to the rate and effect size variation of somatic mutations, the same germline alleles persistently dominate the response of different individuals. In contrast, if germline-encoded advantages can be easily overcome by subsequent mutations, allele usage becomes increasingly divergent over time, a pattern we then observe in mice experimentally infected with influenza virus. We investigated whether affinity maturation might nonetheless strongly select for particular amino acid motifs across diverse genetic backgrounds, but we found no evidence of convergence to similar CDR3 sequences or amino acid substitutions. These results suggest that although germline-encoded specificities can lead to similar immune responses between individuals, diverse evolutionary routes to high affinity limit the genetic predictability of responses to infection and vaccination.

Efficient encoding of large antigenic spaces by epitope prioritization with Dolphyn

We investigated a relatively underexplored component of the gut-immune axis by profiling the antibody response to gut phages using Phage Immunoprecipitation Sequencing (PhIP-Seq). To enhance this approach, we developed Dolphyn, a novel method that uses machine learning to select peptides from protein sets and compresses the proteome through epitope-stitching. Dolphyn improves the fraction of gut phage library peptides bound by antibodies from 10% to 31% in healthy individuals, while also reducing the number of synthesized peptides by 78%. In our study on gut phages, we discovered that the immune system develops antibodies to bacteria-infecting viruses in the human gut, particularly E.coli-infecting Myoviridae. Cost-effective PhIP-Seq libraries designed with Dolphyn enable the assessment of a wider range of proteins in a single experiment, thus facilitating the study of the gut-immune axis.

SARS-CoV-2 antibodies recognize 23 distinct epitopic sites on the receptor binding domain

The COVID-19 pandemic and SARS-CoV-2 variants have dramatically illustrated the need for a better understanding of antigen (epitope)-antibody (paratope) interactions. To gain insight into the immunogenic characteristics of epitopic sites (ES), we systematically investigated the structures of 340 Abs and 83 nanobodies (Nbs) complexed with the Receptor Binding Domain (RBD) of the SARS-CoV-2 spike protein. We identified 23 distinct ES on the RBD surface and determined the frequencies of amino acid usage in the corresponding CDR paratopes. We describe a clustering method for analysis of ES similarities that reveals binding motifs of the paratopes and that provides insights for vaccine design and therapies for SARS-CoV-2, as well as a broader understanding of the structural basis of Ab-protein antigen (Ag) interactions.

Untangling immunoglobulin genotype-function associations

Immunoglobulin (IG) genes, encoding B cell receptors (BCRs), are fundamental components of the mammalian immune system, which evolved to recognize the diverse antigenic universe present in nature. To handle these myriad inputs, BCRs are generated through combinatorial recombination of a set of highly polymorphic germline genes, resulting in a vast repertoire of antigen receptors that initiate responses to pathogens and regulate commensals. Following antigen recognition and B cell activation, memory B cells and plasma cells form, allowing for the development of anamnestic antibody (Ab) responses. How inherited variation in IG genes impacts host traits, disease susceptibility, and Ab recall responses is a topic of great interest. Here, we consider approaches to translate emerging knowledge about IG genetic diversity and expressed repertoires to inform our understanding of Ab function in health and disease etiology. As our understanding of IG genetics grows, so will our need for tools to decipher preferences for IG gene or allele usage in different contexts, to better understand antibody responses at the population level.

Unearthing the Rosetta Stone of public antibody responses

Comprehensive profiling of humoral responses to viruses reveals that germline-encoded V gene motifs govern the emergence of recurrent antibody epitopes across individuals.