Biochemical patterns of antibody polyreactivity revealed through a bioinformatics-based analysis of CDR loops

Binding Promiscuity of Therapeutic Factor VIII

The binding promiscuity of proteins defines their ability to indiscriminately bind multiple unrelated molecules. Binding promiscuity is implicated, at least in part, in the off-target reactivity, nonspecific biodistribution, immunogenicity, and/or short half-life of potentially efficacious protein drugs, thus affecting their clinical use. In this review, we discuss the current evidence for the binding promiscuity of factor VIII (FVIII), a protein used for the treatment of hemophilia A, which displays poor pharmacokinetics, and elevated immunogenicity. We summarize the different canonical and noncanonical interactions that FVIII may establish in the circulation and that could be responsible for its therapeutic liabilities. We also provide information suggesting that the FVIII light chain, and especially its C1 and C2 domains, could play an important role in the binding promiscuity. We believe that the knowledge accumulated over years of FVIII usage could be exploited for the development of strategies to predict protein binding promiscuity and therefore anticipate drug efficacy and toxicity. This would open a mutational space to reduce the binding promiscuity of emerging protein drugs while conserving their therapeutic potency.

A Refined Human Linear B Cell Epitope Map of Outer Surface Protein C (OspC) From the Lyme Disease Spirochete, Borrelia Burgdorferi

Background

A detailed understanding of the human antibody response to outer surface protein C (OspC) of Borrelia burgdorferi has important implications for Lyme disease diagnostics and vaccines.

Methods

In this report, 13 peptides encompassing 8 reported OspC linear B-cell epitopes from OspC types A, B, and K, including the largely conserved C-terminus (residues 193-210), were evaluated by multiplex immunoassay (MIA) for IgG reactivity with ~700 human serum samples confirmed positive in a 2-tiered Lyme disease diagnostic assay (Bb+) and ~160 post-treatment Lyme disease (PTLD) serum samples. The vmp-like sequence E (VlsE) C6-17 peptide was included as a positive control.

Results

Serum IgG from Bb+ samples were reactive with 10 of the 13 OspC-derived peptides tested, with the C-terminal peptide (residues 193-210) being the most reactive. Spearman's rank correlation matrices and hierarchical clustering revealed a strong correlation between 193-210 and VlsE C6-17 peptide reactivity but little demonstrable association between 193-210 and the other OspC peptides or recombinant OspC. OspC peptide reactivities (excluding 193-210) were strongly correlated with each other and were disproportionately influenced by a subset of pan-reactive samples. In the PTLD sample set, none of the OspC-derived peptides were significantly reactive over baseline, even though VlsE C6-17 peptide reactivity remained.

Conclusions

The asynchronous and potentially short-lived serologic response to OspC-derived peptides reveals the complexity of B-cell responses to B. burgdorferi lipoproteins and confounds interpretation of antibody profiles for Lyme disease diagnostics.

Profiling the broad antibody diversity of lymphatic filariasis immune antibody repertoire by deep sequencing

Lymphatic filariasis is caused by infections of thread-like filarial worms, namely Wuchereria bancrofti, Brugia Malayi and Brugia timori. However, in-depth analysis of the antibody repertoire against Lymphatic filariasis is lacking. Using high-throughput sequencing of antibody repertoires, immunome analysis of IgG (LG) and IgM (LM) repertoires were studied. Despite significant differences between LG and LM in V(D)J gene usage, IGHV4-34, IGHV6-1, IGHD3-10 and IGHJ4 were preferred in both repertoires. The CDR3 in the LG repertoire showed a longer length than LM. Higher SHM level were observed in LG sequences and presence of oligoclonal sequences indicates the extent of clonal expansion. The prevalence of rare clonotypes in LM repertoire depicts the high clonal diversity when compared to LG repertoire. Monoclonal antibodies against closely related parasitic infections were present within the LG repertoire suggesting that immune repertoires may not be as exclusive and biased against the target infection as initially thought. The characterization of the immune repertoire can provide critical insight into the antibody response patterns in disease state, antibody generation process during infections and future antibody designs.

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.

Systematic evaluation of intratumoral and peripheral BCR repertoires in three cancers

The current understanding of humoral immune response in cancer patients suggests that tumors may be infiltrated with diffuse B cells of extra-tumoral origin or may develop organized lymphoid structures, where somatic hypermutation and antigen-driven selection occur locally. These processes are believed to be significantly influenced by the tumor microenvironment through secretory factors and biased cell-cell interactions. To explore the manifestation of this influence, we used deep unbiased immunoglobulin profiling and systematically characterized the relationships between B cells in circulation, draining lymph nodes (draining LNs), and tumors in 14 patients with three human cancers. We demonstrated that draining LNs are differentially involved in the interaction with the tumor site, and that significant heterogeneity exists even between different parts of a single lymph node (LN). Next, we confirmed and elaborated upon previous observations regarding intratumoral immunoglobulin heterogeneity. We identified B cell receptor (BCR) clonotypes that were expanded in tumors relative to draining LNs and blood and observed that these tumor-expanded clonotypes were less hypermutated than non-expanded (ubiquitous) clonotypes. Furthermore, we observed a shift in the properties of complementarity-determining region 3 of the BCR heavy chain (CDR-H3) towards less mature and less specific BCR repertoire in tumor-infiltrating B-cells compared to circulating B-cells, which may indicate less stringent control for antibody-producing B cell development in tumor microenvironment (TME). In addition, we found repertoire-level evidence that B-cells may be selected according to their CDR-H3 physicochemical properties before they activate somatic hypermutation (SHM). Altogether, our work outlines a broad picture of the differences in the tumor BCR repertoire relative to non-tumor tissues and points to the unexpected features of the SHM process.

Learning antibody sequence constraints from allelic inclusion

Antibodies and B-cell receptors (BCRs) are produced by B cells, and are built of a heavy chain and a light chain. Although each B cell could express two different heavy chains and four different light chains, usually only a unique pair of heavy chain and light chain is expressed-a phenomenon known as allelic exclusion. However, a small fraction of naive-B cells violate allelic exclusion by expressing two productive light chains, one of which has impaired function; this has been called allelic inclusion. We demonstrate that these B cells can be used to learn constraints on antibody sequence. Using large-scale single-cell sequencing data from humans, we find examples of light chain allelic inclusion in thousands of naive-B cells, which is an order of magnitude larger than existing datasets. We train machine learning models to identify the abnormal sequences in these cells. The resulting models correlate with antibody properties that they were not trained on, including polyreactivity, surface expression, and mutation usage in affinity maturation. These correlations are larger than what is achieved by existing antibody modeling approaches, indicating that allelic inclusion data contains useful new information. We also investigate the impact of similar selection forces on the heavy chain in mouse, and observe that pairing with the surrogate light chain significantly restricts heavy chain diversity.

Human antibody polyreactivity is governed primarily by the heavy-chain complementarity-determining regions

Although antibody variable regions mediate antigen-specific binding, they can also mediate non-specific interactions with non-cognate antigens, impacting diverse immunological processes and the efficacy, safety, and half-life of antibody therapeutics. To understand the molecular basis of antibody non-specificity, we sorted two dissimilar human naïve antibody libraries against multiple reagents to enrich for variants with different levels of polyreactivity. Sequence analysis of >300,000 paired antibody variable regions revealed that the heavy chain primarily mediates human antibody polyreactivity, and this is due to the high positive charge, high hydrophobicity, and combinations thereof in the corresponding complementarity-determining regions, which can be predicted using a machine learning model developed in this work. Notably, a subset of the most important features governing antibody non-specific interactions, namely those that contain tyrosine, also govern specific antigen recognition. Our findings are broadly relevant for understanding fundamental aspects of antibody molecular recognition and the applied aspects of antibody-drug design.

Protein language models enable prediction of polyreactivity of monospecific, bispecific, and heavy-chain-only antibodies

Background

Early assessment of antibody off-target binding is essential for mitigating developability risks such as fast clearance, reduced efficacy, toxicity, and immunogenicity. The baculovirus particle (BVP) binding assay has been widely utilized to evaluate polyreactivity of antibodies. As a complementary approach, computational prediction of polyreactivity is desirable for counter-screening antibodies from in silico discovery campaigns. However, there is a lack of such models.

Methods

Herein, we present the development of an ensemble of three deep learning models based on two pan-protein foundational protein language models (ESM2 and ProtT5) and an antibody-specific protein language model (PLM) (Antiberty). These models were trained in a transfer learning network to predict the outcomes in the BVP assay and the bovine serum albumin binding assay, which was developed as a complement to the BVP assay. The training was conducted on a large dataset of antibody sequences augmented with experimental conditions, which were collected through a highly efficient application system.

Results

The resulting models demonstrated robust performance on canonical mAbs (monospecific with heavy and light chain), bispecific Abs, and single-domain Fc (VHH-Fc). PLMs outperformed a model built using molecular descriptors calculated from AlphaFold 2 predicted structures. Embeddings from the antibody-specific and foundational PLMs resulted in similar performance.

Conclusion

To our knowledge, this represents the first application of PLMs to predict assay data on bispecifics and VHH-Fcs.

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.

Single-domain antibodies against SARS-CoV-2 RBD from a two-stage phage screening of universal and focused synthetic libraries

BACKGROUND

Coronavirus disease 2019 (COVID-19) is an evolving global pandemic, and nanobodies, as well as other single-domain antibodies (sdAbs), have been recognized as a potential diagnostic and therapeutic tool for infectious diseases. High-throughput screening techniques such as phage display have been developed as an alternative to in vivo immunization for the discovery of antibody-like target-specific binders.

METHODS

We designed and constructed a highly diverse synthetic phage library sdAb-U (single-domain Antibody - Universal library ) based on a human framework. The SARS-CoV-2 receptor-binding domain (RBD) was expressed and purified. The universal library sdAb-U was panned against the RBD protein target for two rounds, followed by monoclonal phage ELISA (enzyme-linked immunosorbent assay) to identify RBD-specific binders (the first stage). High-affinity binders were sequenced and the obtained CDR1 and CDR2 sequences were combined with fully randomized CDR3 to construct a targeted (focused) phage library sdAb-RBD, for subsequent second-stage phage panning (also two rounds) and screening. Then, sequences with high single-to-background ratios in phage ELISA were selected for expression. The binding affinities of sdAbs to RBD were measured by an ELISA-based method. In addition, we conducted competition ELISA (using ACE2 ectodomain S19-D615) and SARS-CoV-2 pseudovirus neutralization assays for the high-affinity RBD-binding sdAb39.

RESULTS

Significant enrichments were observed in both the first-stage (universal library) and the second-stage (focused library) phage panning. Five RBD-specific binders were identified in the first stage with high ELISA signal-to-background ratios. In the second stage, we observed a much higher possibility of finding RBD-specific clones in phage ELISA. Among 45 selected RBD-positive sequences, we found eight sdAbs can be well expressed, and five of them show high-affinity to RBD (EC50 < 100nM). We finally found that sdAb39 (EC50 ~ 4nM) can compete with ACE2 for binding to RBD.

CONCLUSION

Overall, this two-stage strategy of synthetic phage display libraries enables rapid selection of SARS-CoV-2 RBD sdAb with potential therapeutic activity, and this two-stage strategy can potentially be used for rapid discovery of sdAbs against other targets.

Developability considerations for bispecific and multispecific antibodies

Bispecific antibodies (bsAb) and multispecific antibodies (msAb) encompass a diverse variety of formats that can concurrently bind multiple epitopes, unlocking mechanisms to address previously difficult-to-treat or incurable diseases. Early assessment of candidate developability enables demotion of antibodies with low potential and promotion of the most promising candidates for further development. Protein-based therapies have a stringent set of developability requirements in order to be competitive (e.g. high-concentration formulation, and long half-life) and their assessment requires a robust toolkit of methods, few of which are validated for interrogating bsAbs/msAbs. Important considerations when assessing the developability of bsAbs/msAbs include their molecular format, likelihood for immunogenicity, specificity, stability, and potential for high-volume production. Here, we summarize the critical aspects of developability assessment, and provide guidance on how to develop a comprehensive plan tailored to a given bsAb/msAb.

Optimization of therapeutic antibodies for reduced self-association and non-specific binding via interpretable machine learning

Antibody development, delivery, and efficacy are influenced by antibody-antigen affinity interactions, off-target interactions that reduce antibody bioavailability and pharmacokinetics, and repulsive self-interactions that increase the stability of concentrated antibody formulations and reduce their corresponding viscosity. Yet identifying antibody variants with optimal combinations of these three types of interactions is challenging. Here we show that interpretable machine-learning classifiers, leveraging antibody structural features descriptive of their variable regions and trained on experimental data for a panel of 80 clinical-stage monoclonal antibodies, can identify antibodies with optimal combinations of low off-target binding in a common physiological-solution condition and low self-association in a common antibody-formulation condition. For three clinical-stage antibodies with suboptimal combinations of off-target binding and self-association, the classifiers predicted variable-region mutations that optimized non-affinity interactions while maintaining high-affinity antibody-antigen interactions. Interpretable machine-learning models may facilitate the optimization of antibody candidates for therapeutic applications.

Polyreactivity of antibodies from different B-cell subpopulations is determined by distinct sequence patterns of variable region

An antibody molecule that can bind to multiple distinct antigens is defined as polyreactive. In the present study, we performed statistical analyses to assess sequence correlates of polyreactivity of >600 antibodies cloned from different B-cell types of healthy humans. The data revealed several sequence patterns of variable regions of heavy and light immunoglobulin chains that determine polyreactivity. The most prominent identified patterns were increased number of basic amino acid residues, reduced frequency of acidic residues, increased number of aromatic and hydrophobic residues, and longer length of CDR L1. Importantly, our study revealed that antibodies isolated from different B-cell populations used distinct sequence patterns (or combinations of them) for polyreactive antigen binding. Furthermore, we combined the data from sequence analyses with molecular modeling of selected polyreactive antibodies and demonstrated that human antibodies can use multiple pathways for achieving antigen-binding promiscuity. These data reconcile some contradictions in the literature regarding the determinants of antibody polyreactivity. Moreover, our study demonstrates that the mechanism of polyreactivity of antibodies evolves during immune response and might be tailored to specific functional properties of different B-cell compartments. Finally, these data can be of use for efforts in the development and engineering of therapeutic antibodies.

Biochemical and biophysical characterization of natural polyreactivity in antibodies

To become specialized binders, antibodies undergo a process called affinity maturation to maximize their binding affinity. Despite this process, some antibodies retain low-affinity binding to diverse epitopes in a phenomenon called polyreactivity. Here we seek to understand the molecular basis of this polyreactivity in antibodies. Our results highlight that polyreactive antigen-binding fragments (Fabs) bind their targets with low affinities, comparable to T cell receptor recognition of autologous classical major histocompatibility complex. Extensive mutagenic studies find no singular amino acid residue or biochemical property responsible for polyreactive interaction, suggesting that polyreactive antibodies use multiple strategies for engagement. Finally, our crystal structures and all-atom molecular dynamics simulations of polyreactive Fabs show increased rigidity compared to their monoreactive relatives, forming a neutral and accessible platform for diverse antigens to bind. Together, these data support a cooperative strategy of rigid neutrality in establishing the polyreactive status of an antibody molecule.

Conserved biophysical compatibility among the highly variable germline-encoded regions shapes TCR-MHC interactions

T cells are critically important components of the adaptive immune system primarily responsible for identifying and responding to pathogenic challenges. This recognition of pathogens is driven by the interaction between membrane-bound T cell receptors (TCRs) and antigenic peptides presented on major histocompatibility complex (MHC) molecules. The formation of the TCR-peptide-MHC complex (TCR-pMHC) involves interactions among germline-encoded and hypervariable amino acids. Germline-encoded and hypervariable regions can form contacts critical for complex formation, but only interactions between germline-encoded contacts are likely to be shared across many of all the possible productive TCR-pMHC complexes. Despite this, experimental investigation of these interactions have focused on only a small fraction of the possible interaction space. To address this, we analyzed every possible germline-encoded TCR-MHC contact in humans, thereby generating the first comprehensive characterization of these largely antigen-independent interactions. Our computational analysis suggests that germline-encoded TCR-MHC interactions that are conserved at the sequence level are rare due to the high amino acid diversity of the TCR CDR1 and CDR2 loops, and that such conservation is unlikely to dominate the dynamic protein-protein binding interface. Instead, we propose that binding properties such as the docking orientation are defined by regions of biophysical compatibility between these loops and the MHC surface.

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.

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.

Broad responses to chemical adducts shape the natural antibody repertoire in early infancy

Natural antibodies are an integral part of innate humoral immunity yet their development and polyreactive nature are still enigmatic. Here, we show that characteristic monoclonal natural antibodies recognize common chemical moieties or adducts, supporting the view that polyreactive antibodies may often correspond to anti-adduct antibodies. We next examined the development of immunoglobulin M (IgM) and IgG to 81 ubiquitous adducts from birth to old age. Newborn IgM only reacted to a limited number of consensus determinants. This highly restricted neonatal repertoire abruptly diversified around 6 months of age through the development of antibodies to environmental antigens and age-driven epigenetic modifications. In contrast, the IgG repertoire was diverse across the entire life span. Our studies reveal an unrecognized component of humoral immunity directed to common adducts. These findings set the ground for further investigations into the role of anti-adduct B cell responses in homeostatic functions and pathological conditions.

Accelerating therapeutic protein design with computational approaches toward the clinical stage

Therapeutic protein, represented by antibodies, is of increasing interest in human medicine. However, clinical translation of therapeutic protein is still largely hindered by different aspects of developability, including affinity and selectivity, stability and aggregation prevention, solubility and viscosity reduction, and deimmunization. Conventional optimization of the developability with widely used methods, like display technologies and library screening approaches, is a time and cost-intensive endeavor, and the efficiency in finding suitable solutions is still not enough to meet clinical needs. In recent years, the accelerated advancement of computational methodologies has ushered in a transformative era in the field of therapeutic protein design. Owing to their remarkable capabilities in feature extraction and modeling, the integration of cutting-edge computational strategies with conventional techniques presents a promising avenue to accelerate the progression of therapeutic protein design and optimization toward clinical implementation. Here, we compared the differences between therapeutic protein and small molecules in developability and provided an overview of the computational approaches applicable to the design or optimization of therapeutic protein in several developability issues.

The evolutionary and functional significance of germline immunoglobulin gene variation

The recombination between immunoglobulin (IG) gene segments determines an individual's naïve antibody repertoire and, consequently, (auto)antigen recognition. Emerging evidence suggests that mammalian IG germline variation impacts humoral immune responses associated with vaccination, infection, and autoimmunity - from the molecular level of epitope specificity, up to profound changes in the architecture of antibody repertoires. These links between IG germline variants and immunophenotype raise the question on the evolutionary causes and consequences of diversity within IG loci. We discuss why the extreme diversity in IG loci remains a mystery, why resolving this is important for the design of more effective vaccines and therapeutics, and how recent evidence from multiple lines of inquiry may help us do so.

IgD shapes the pre-immune naïve B cell compartment in humans

B cell maturation and immunoglobulin (Ig) repertoire selection are governed by expression of a functional B cell receptor (BCR). Naïve B cells co-express their BCR as IgM and IgD isotype. However, the role of the additionally expressed IgD on naïve B cells is not known. Here we assessed the impact of IgD on naïve B cell maturation and Ig repertoire selection in 8 individuals from 3 different families with heterozygous loss-of-function or loss-of expression mutations in IGHD. Although naïve B cells from these individuals expressed IgM on their surface, the IGHD variant in heterozygous state entailed a chimeric situation by allelic exclusion with almost half of the naïve B cell population lacking surface IgD expression. Flow cytometric analyses revealed a distinct phenotype of IgD-negative naïve B cells with decreased expression of CD19, CD20 and CD21 as well as lower BAFF-R and integrin-β7 expression. IgD-negative B cells were less responsive in vitro after engaging the IgM-BCR, TLR7/9 or CD40 pathway. Additionally, a selective disadvantage of IgD-negative B cells within the T2 transitional and mature naïve B cell compartment as well as reduced frequencies of IgM^lo/- B cells within the mature naïve B cell compartment lacking IgD were evident. RNA-Ig-seq of bulk sorted B cell populations showed an altered selection of distinct V_H segments in the IgD-negative mature naïve B cell population. We conclude that IgD expression on human naïve B cells is redundant for generation of naïve B cells in general, but further shapes the naive B cell compartment starting from T2 transitional B cells. Our observations suggest an unexpected role of IgD expression to be critical for selection of distinct Ig V_H segments into the pre-immune Ig repertoire and for the survival of IgM^lo/- naïve B cells known to be enriched in poly-/autoreactive B cell clones.

Chickens, more than humans, focus the diversity of their immunoglobulin genes on the complementarity-determining region but utilise amino acids, indicative of a more cross-reactive antibody repertoire

The mechanisms of B-cell diversification differ greatly between aves and mammals, but both produce B cells and antibodies capable of supporting an effective immune response. To see how differences in the generation of diversity might affect overall repertoire diversity, we have compared the diversity characteristics of immunoglobulin genes from domestic chickens to those from humans. Both use V(D)J gene rearrangement and somatic hypermutation, but only chickens use somatic gene conversion. A range of diversity analysis tools were used to investigate multiple aspects of amino acid diversity at both the germline and repertoire levels. The effect of differing amino acid usages on antibody characteristics was assessed. At both the germline and repertoire levels, chickens exhibited lower amino acid diversity in comparison to the human immunoglobulin genes, especially outside of the complementarity-determining region (CDR). Chickens were also found to possess much larger and more hydrophilic CDR3s with a higher predicted protein binding potential, suggesting that the antigen-binding site in chicken antibodies is more flexible and more polyreactive than that seen in human antibodies.

An in silico method to assess antibody fragment polyreactivity

Antibodies are essential biological research tools and important therapeutic agents, but some exhibit non-specific binding to off-target proteins and other biomolecules. Such polyreactive antibodies compromise screening pipelines, lead to incorrect and irreproducible experimental results, and are generally intractable for clinical development. Here, we design a set of experiments using a diverse naïve synthetic camelid antibody fragment (nanobody) library to enable machine learning models to accurately assess polyreactivity from protein sequence (AUC > 0.8). Moreover, our models provide quantitative scoring metrics that predict the effect of amino acid substitutions on polyreactivity. We experimentally test our models' performance on three independent nanobody scaffolds, where over 90% of predicted substitutions successfully reduced polyreactivity. Importantly, the models allow us to diminish the polyreactivity of an angiotensin II type I receptor antagonist nanobody, without compromising its functional properties. We provide a companion web-server that offers a straightforward means of predicting polyreactivity and polyreactivity-reducing mutations for any given nanobody sequence.

Prediction of polyreactive and nonspecific single-chain fragment variables through structural biochemical features and protein language-based descriptors

BACKGROUND

Monoclonal antibodies (mAbs) have been used as therapeutic agents, which must overcome many developability issues after the discovery from in vitro display libraries. Especially, polyreactive mAbs can strongly bind to a specific target and weakly bind to off-target proteins, which leads to poor antibody pharmacokinetics in clinical development. Although early assessment of polyreactive mAbs is important in the early discovery stage, experimental assessments are usually time-consuming and expensive. Therefore, computational approaches for predicting the polyreactivity of single-chain fragment variables (scFvs) in the early discovery stage would be promising for reducing experimental efforts.

RESULTS

Here, we made prediction models for the polyreactivity of scFvs with the known polyreactive antibody features and natural language model descriptors. We predicted 19,426 protein structures of scFvs with trRosetta to calculate the polyreactive antibody features and investigated the classifying performance of each factor for polyreactivity. In the known polyreactive features, the net charge of the CDR2 loop, the tryptophan and glycine residues in CDR-H3, and the lengths of the CDR1 and CDR2 loops, importantly contributed to the performance of the models. Additionally, the hydrodynamic features, such as partial specific volume, gyration radius, and isoelectric points of CDR loops and scFvs, were newly added to improve model performance. Finally, we made the prediction model with a robust performance ([Formula: see text]) with an ensemble learning of the top 3 best models.

CONCLUSION

The prediction models for polyreactivity would help assess polyreactive scFvs in the early discovery stage and our approaches would be promising to develop machine learning models with quantitative data from high throughput assays for antibody screening.

Computational Assessment of Protein-Protein Binding Specificity within a Family of Synaptic Surface Receptors

Atomic-level information is essential to explain the formation of specific protein complexes in terms of structure and dynamics. The set of Dpr and DIP proteins, which play a key role in the neuromorphogenesis in the nervous system of Drosophila melanogaster, offer a rich paradigm to learn about protein-protein recognition. Many members of the DIP subfamily cross-react with several members of the Dpr family and vice versa. While there exists a total of 231 possible Dpr-DIP heterodimer complexes from the 21 Dpr and 11 DIP proteins, only 57 "cognate" pairs have been detected by surface plasmon resonance (SPR) experiments, suggesting that the remaining 174 pairs have low or unreliable binding affinity. Our goal is to assess the performance of computational approaches to characterize the global set of interactions between Dpr and DIP proteins and identify the specificity of binding between each DIP with their corresponding Dpr binding partners. In addition, we aim to characterize how mutations influence the specificity of the binding interaction. In this work, a wide range of knowledge-based and physics-based approaches are utilized, including mutual information, linear discriminant analysis, homology modeling, molecular dynamics simulations, Poisson-Boltzmann continuum electrostatics calculations, and alchemical free energy perturbation to decipher the origin of binding specificity of the Dpr-DIP complexes examined. Ultimately, the results show that those two broad strategies are complementary, with different strengths and limitations. Biological inter-relations are more clearly revealed through knowledge-based approaches combining evolutionary and structural features, the molecular determinants controlling binding specificity can be predicted accurately with physics-based approaches based on atomic models.

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies

Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.

B-cell receptor physical properties affect relative IgG1 and IgE responses in mouse egg allergy

Mutated and unmutated IgE and IgG play different and partly opposing roles in allergy development, but the mechanisms controlling their relative production are incompletely understood. Here, we analyzed the IgE-response in murine food allergy. Deep sequencing of the complementary-determining region (CDR) repertoires indicated that an ongoing unmutated extrafollicular IgE response coexists with a germinal center response, even after long-lasting allergen challenges. Despite overall IgG1-dominance, a significant proportion of clonotypes contained several-fold more IgE than IgG1. Clonotypes with differential bias to either IgE or IgG1 showed distinct hypermutation and clonal expansion. Hypermutation rates were associated with different physiochemical binding properties of individual B-cell receptors (BCR). Increasing BCR signaling strength inhibited class switching from IgG1 to IgE in vitro, preferentially constraining IgE formation. These data indicate that antigen-binding properties of individual BCRs determine differential IgE hypermutation and IgE versus IgG1 production on the level of single B-cell clones.

How can polyreactive antibodies conquer rapidly evolving viruses?

Broadly neutralizing antibodies against rapidly evolving viruses (e.g., HIV-1 and influenza virus), often manifest antigen-binding promiscuity. Based on a recent study, we hypothesize on the significance of antibody polyreactivity in neutralization of rapidly evolving viruses. We propose that polyreactivity contributes to toleration of viral variants and shortens the time for generating neutralizing antibodies.

The importance of polyreactive antibodies in protection against pneumococcal infection

Antibodies are a key element of the immune response. They can bind their molecular targets with exquisite sensitivity and specificity, providing protection against a multitude of pathogens. They have long been understood to be markers of a successful response to vaccination, and are now widely manufactured as highly specific and robust immunotherapeutic agents. Less well understood are the polyreactive antibodies, found in serum, which are able to bind more than one target molecule. Here, we highlight new research into these naturally occurring polyreactive antibodies, which demonstrates their importance for protection against Streptococcus pneumoniae, a common cause of airway infection.