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Introduction: VHHs (VH of heavy-chain-only antibodies) represent a unique alternative to Q7 conventional antibodies because of their smaller size, comparable binding affinity and biophysical properties. Method: In this study, we systematically analyzed VHH NGS sequences from 22 Alpacas and structure data from public database. Results: VHHs in Alpaca can be grouped into five main types with multiple distinct sequence and structure features. Based on the existence of hallmark residues in FR2 region, VHHs can be classified into two groups: nonclassical VHHs (without hallmark residues) and classical VHHs (with hallmark residues). Based on VHH hallmark residues at 42 position (IMGT numbering, FR2 region) and number of cysteines, we found that Alpaca classical VHHs can be further separated into three main types: F_C2 VHHs with F (phenylalanine) at position 42 and having 2 cysteines within sequences, Y_C2 VHHs with Y (tyrosine) at position 42 and having 2 cysteines, and F_C4 with F at position 42 and having 4 cysteines. Non-classical VHHs can be further separated into 2 types based on germlines mapped: N_V3 for VHHs mapped to V3 germlines and N_V4 for V4 germlines. Based on whether FR2 residues are involved in binding, two kinds of paratopes can be identified. Different types of VHHs showed distinct associations with these two paratopes and displayed significant differences in paratope size, residue usage and other structure features. Discussion: Such results will have significant implications in VHH discovery, engine e ring, and design for innovative therapeutics. [ABSTRACT FROM AUTHOR]

IntroductionVHHs (VH of heavy-chain-only antibodies) represent a unique alternative to Q7 conventional antibodies because of their smaller size, comparable binding affinity and biophysical properties. MethodIn this study, we systematically analyzed VHH NGS sequences from 22 Alpacas and structure data from public database. ResultsVHHs in Alpaca can be grouped into five main types with multiple distinct sequence and structure features. Based on the existence of hallmark residues in FR2 region, VHHs can be classified into two groups: nonclassical VHHs (without hallmark residues) and classical VHHs (with hallmark residues). Based on VHH hallmark residues at 42 position (IMGT numbering, FR2 region) and number of cysteines, we found that Alpaca classical VHHs can be further separated into three main types: F_C2 VHHs with F (phenylalanine) at position 42 and having 2 cysteines within sequences, Y_C2 VHHs with Y (tyrosine) at position 42 and having 2 cysteines, and F_C4 with F at position 42 and having 4 cysteines. Non-classical VHHs can be further separated into 2 types based on germlines mapped: N_V3 for VHHs mapped to V3 germlines and N_V4 for V4 germlines. Based on whether FR2 residues are involved in binding, two kinds of paratopes can be identified. Different types of VHHs showed distinct associations with these two paratopes and displayed significant differences in paratope size, residue usage and other structure features. DiscussionSuch results will have significant implications in VHH discovery, engine e ring, and design for innovative therapeutics.

We describe an investigation using structural mass spectrometry (MS) of the impact of two antibodies, 15497 and 15498, binding the highly flexible SARS-CoV-2 Nsp1 protein. We determined the epitopes and paratopes involved in the antibody–protein interactions by using hydrogen–deuterium exchange MS (HDX-MS). Notably, the Fab (Fragment antigen binding) for antibody 15498 captured a high energy form of the antigen exhibiting significant conformational changes that added flexibility over most of the Nsp1 protein. The Fab for antibody 15497, however, showed usual antigen binding behavior, revealing local changes presumably including the binding site. These findings illustrate an unusual antibody effect on an antigen and are consistent with the dynamic nature of the Nsp1 protein. Our studies suggest that this interaction capitalizes on the high flexibility of Nsp1 to undergo conformational change and be trapped in a higher energy state by binding with a specific antibody. [ABSTRACT FROM AUTHOR]

Antibodies, through their ability to target virtually any epitope, play a key role in driving the adaptive immune response in jawed vertebrates. The binding domains of standard antibodies are their variable light (VL) and heavy (VH) domains, both of which present analogous complementarity-determining region (CDR) loops. It has long been known that the VH CDRs contribute more heavily to the antigenbinding surface (paratope), with the CDR-H3 loop providing a major modality for the generation of diverse paratopes. Here, we provide evidence for an additional role of the VL domain as a modulator of CDR-H3 structure, using a diverse set of antibody crystal structures and a large set of molecular dynamics simulations. We show that specific attributes of the VL domain such as subtypes, CDR canonical forms and genes can influence the structural diversity of the CDR-H3 loop, and provide a physical model for how this effect occurs through inter-loop contacts and packing of CDRs against each other. Our results indicate that the rigid minor loops fine-tune the structure of CDR-H3, thereby contributing to the generation of surfaces complementary to the vast number of possible epitope topologies, and provide insights into the interdependent nature of CDR conformations, an understanding of which is important for the rational antibody design process. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a +sense single-strand RNA virus. The virus has four major surface proteins: spike (S), envelope (E), membrane (M), and nucleocapsid (N), respectively. The constitutive proteins present a high grade of symmetry. Identifying a binding site is difficult. The virion is approximately 50–200 nm in diameter. Angiotensin-converting enzyme 2 (ACE2) acts as the cell receptor for the virus. SARS-CoV-2 has an increased affinity to human ACE2 compared with the original SAR strain. Topological space, and its symmetry, is a critical component in molecular interactions. By exploring this space, a suitable ligand space can be characterized accordingly. A spike protein (S) computational model in a complex with ACE 2 was generated using silica methods. Topological spaces were probed using high computational throughput screening techniques to identify and characterize the topological space of both SARS and SARS-CoV-2 spike protein and its ligand space. In order to identify the symmetry clusters, computational analysis techniques, together with statistical analysis, were utilized. The computations are based on crystallographic protein data bank PDB-based models of constitutive proteins. Cartesian coordinates of component atoms and some cluster maps were generated and analyzed. Dihedral angles were used in order to compute a topological receptor space. This computational study uses a multimodal representation of spike protein interactions with some fragment proteins. The chemical space of the receptors (a dimensional volume) suggests the relevance of the receptor as a drug target. The spike protein S of SARS and SARS-CoV-2 is analyzed and compared. The results suggest a mirror symmetry of SARS and SARS-CoV-2 spike proteins. The results show thatSARS-CoV-2 space is variable and has a distinct topology. In conclusion, surface proteins grant virion variability and symmetry in interactions with a potential complementary target (protein, antibody, ligand). The mirror symmetry of dihedral angle clusters determines a high specificity of the receptor space. [ABSTRACT FROM AUTHOR]

Antibodies are the largest class of biotherapeutics. However, in recent years, single-domain antibodies have gained traction due to their smaller size and comparable binding affinity. Antibodies (Abs) and single-domain antibodies (sdAbs) differ in the structures of their binding sites: most significantly, singledomain antibodies lack a light chain and so have just three CDR loops. Given this inherent structural difference, it is important to understand whether Abs and sdAbs are distinguishable in how they engage a binding partner and thus, whether they are suited to different types of epitopes. In this study, we use non-redundant sequence and structural datasets to compare the paratopes, epitopes and antigen interactions of Abs and sdAbs. We demonstrate that even though sdAbs have smaller paratopes, they target epitopes of equal size to those targeted by Abs. To achieve this, the paratopes of sdAbs contribute more interactions per residue than the paratopes of Abs. Additionally, we find that conserved framework residues are of increased importance in the paratopes of sdAbs, suggesting that they include non-specific interactions to achieve comparable affinity. Furthermore, the epitopes of sdAbs are only marginally less accessible than those of Abs: we posit that this may be explained by differences in the orientation and compaction of sdAb and Ab CDR-H3 loops. Overall, our results have important implications for the engineering and humanization of sdAbs, as well as the selection of the best modality for targeting a particular epitope. [ABSTRACT FROM AUTHOR]

CD59 is an abundant immuno-regulatory human protein that protects cells from damage by inhibiting the complement system. CD59 inhibits the assembly of the Membrane Attack Complex (MAC), the bactericidal pore-forming toxin of the innate immune system. In addition, several pathogenic viruses, including HIV-1, escape complement-mediated virolysis by incorporating this complement inhibitor in their own viral envelope. This makes human pathogenic viruses, such as HIV-1, not neutralised by the complement in human fluids. CD59 is also overexpressed in several cancer cells to resist the complement attack. Consistent with its importance as a therapeutical target, CD59-targeting antibodies have been proven to be successful in hindering HIV-1 growth and counteracting the effect of complement inhibition by specific cancer cells. In this work, we make use of bioinformatics and computational tools to identify CD59 interactions with blocking antibodies and to describe molecular details of the paratope–epitope interface. Based on this information, we design and produce paratope-mimicking bicyclic peptides able to target CD59. Our results set the basis for the development of antibody-mimicking small molecules targeting CD59 with potential therapeutic interest as complement activators. [ABSTRACT FROM AUTHOR]

Antibodies are the largest class of biotherapeutics. However, in recent years, single-domain antibodies have gained traction due to their smaller size and comparable binding affinity. Antibodies (Abs) and single-domain antibodies (sdAbs) differ in the structures of their binding sites: most significantly, single-domain antibodies lack a light chain and so have just three CDR loops. Given this inherent structural difference, it is important to understand whether Abs and sdAbs are distinguishable in how they engage a binding partner and thus, whether they are suited to different types of epitopes. In this study, we use non-redundant sequence and structural datasets to compare the paratopes, epitopes and antigen interactions of Abs and sdAbs. We demonstrate that even though sdAbs have smaller paratopes, they target epitopes of equal size to those targeted by Abs. To achieve this, the paratopes of sdAbs contribute more interactions per residue than the paratopes of Abs. Additionally, we find that conserved framework residues are of increased importance in the paratopes of sdAbs, suggesting that they include non-specific interactions to achieve comparable affinity. Furthermore, the epitopes of sdAbs are only marginally less accessible than those of Abs: we posit that this may be explained by differences in the orientation and compaction of sdAb and Ab CDR-H3 loops. Overall, our results have important implications for the engineering and humanization of sdAbs, as well as the selection of the best modality for targeting a particular epitope.

Antibodies, through their ability to target virtually any epitope, play a key role in driving the adaptive immune response in jawed vertebrates. The binding domains of standard antibodies are their variable light (VL) and heavy (VH) domains, both of which present analogous complementarity-determining region (CDR) loops. It has long been known that the VH CDRs contribute more heavily to the antigen-binding surface (paratope), with the CDR-H3 loop providing a major modality for the generation of diverse paratopes. Here, we provide evidence for an additional role of the VL domain as a modulator of CDR-H3 structure, using a diverse set of antibody crystal structures and a large set of molecular dynamics simulations. We show that specific attributes of the VL domain such as subtypes, CDR canonical forms and genes can influence the structural diversity of the CDR-H3 loop, and provide a physical model for how this effect occurs through inter-loop contacts and packing of CDRs against each other. Our results indicate that the rigid minor loops fine-tune the structure of CDR-H3, thereby contributing to the generation of surfaces complementary to the vast number of possible epitope topologies, and provide insights into the interdependent nature of CDR conformations, an understanding of which is important for the rational antibody design process.

Monoclonal antibodies are commonly assumed to be monospecific, but anecdotal studies have reported genetic diversity in antibody heavy chain and light chain genes found within individual hybridomas. As the prevalence of such diversity has never been explored, we analyzed 185 random hybridomas, in a large multicenter dataset. The hybridomas analyzed were not biased towards those with cloning difficulties or known to have additional chains. Of the hybridomas we evaluated, 126 (68.1%) contained no additional productive chains, while the remaining 59 (31.9%) contained one or more additional productive heavy or light chains. The expression of additional chains degraded properties of the antibodies, including specificity, binding signal and/or signal-to-noise ratio, as determined by enzyme-linked immunosorbent assay and immunohistochemistry. The most abundant mRNA transcripts found in a hybridoma cell line did not necessarily encode the antibody chains providing the correct specificity. Consequently, when cloning antibody genes, functional validation of all possible VH and VL combinations is required to identify those with the highest affinity and lowest cross-reactivity. These findings, reflecting the current state of hybridomas used in research, reiterate the importance of using sequence-defined recombinant antibodies for research or diagnostic use.

Several human monoclonal Abs for treating Influenza have been evaluated in clinical trials with limited success despite demonstrating superiority in preclinical animal models including mice. To conduct efficacy studies in mice, human monoclonal Abs are genetically engineered to contain mouse heavy chain constant domain to facilitate the engagement of Fc-receptors on mouse immune effector cells. Although studies have consistently reported discrepancies in Ab effectiveness following genetic engineering, the structural and mechanistic basis for these inconsistencies remain uncharacterized. Here, we use homology modeling to predict variable region (VR) analogous monoclonal Abs possessing human IgG1, mouse IgG1, and mouse IgG2a heavy chain constant domains. We then examine predicted 3D structures for variations in the spatial location and orientation of corresponding paratope amino acid residues. By structurally aligning crystal structures of Fabs in complex with hemagglutinin (HA), we show that corresponding paratope amino acid residues for VR-analogous human IgG1, mouse IgG1, and mouse IgG2a monoclonal Abs interact differentially with HA suggesting that their epitopes might not be identical. To demonstrate that variations in the paratope 3D fine architecture have implications for Ab specificity and effectiveness, we genetically engineered VR-analogous human IgG1, human IgG4, mouse IgG1, and mouse IgG2a monoclonal Abs and explored their specificity and effectiveness in protecting MDCK cells from infection by pandemic H1N1 and H3N2 Influenza viruses. We found that VR-analogous monoclonal Abs placed on mouse heavy chain constant domains were more efficacious at protecting MDCK cells from Influenza virus infection relative to those on human heavy chain constant domains. Interestingly, mouse but not human heavy chain constant domains increased target breadth in some monoclonal Abs. These data suggest that heavy chain constant domain sequences play a role in shaping Ab repertoires that go beyond class or sub-class differences in immune effector recruitment. This represents a facet of Ab biology that can potentially be exploited to improve the scope and utilization of current therapeutic or prophylactic candidates for influenza. [ABSTRACT FROM AUTHOR]

Generative machine learning (ML) has been postulated to become a major driver in the computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to confirm this hypothesis have been hindered by the infeasibility of testing arbitrarily large numbers of antibody sequences for their most critical design parameters: paratope, epitope, affinity, and developability. To address this challenge, we leveraged a lattice-based antibody-antigen binding simulation framework, which incorporates a wide range of physiological antibody-binding parameters. The simulation framework enables the computation of synthetic antibody-antigen 3D-structures, and it functions as an oracle for unrestricted prospective evaluation and benchmarking of antibody design parameters of ML-generated antibody sequences. We found that a deep generative model, trained exclusively on antibody sequence (one dimensional: 1D) data can be used to design conformational (three dimensional: 3D) epitope-specific antibodies, matching, or exceeding the training dataset in affinity and developability parameter value variety. Furthermore, we established a lower threshold of sequence diversity necessary for high-accuracy generative antibody ML and demonstrated that this lower threshold also holds on experimental real-world data. Finally, we show that transfer learning enables the generation of high-affinity antibody sequences from low-N training data. Our work establishes a priori feasibility and the theoretical foundation of high-throughput ML-based mAb design.

Single-domain antibodies (sdAbs) showed the incredible advantages of small molecular weight, excellent affinity, specificity, and stability compared with traditional IgG antibodies, so their potential in binding hidden antigen epitopes and hazard detection in food, agricultural and veterinary fields were gradually explored. Moreover, its low immunogenicity, easy-to-carry target drugs, and penetration of the blood-brain barrier have made sdAbs remarkable achievements in medical treatment, toxin neutralization, and medical imaging. With the continuous development and maturity of modern molecular biology, protein analysis software and database with different algorithms, and next-generation sequencing technology, the unique paratope structure and different antigen binding modes of sdAbs compared with traditional IgG antibodies have aroused the broad interests of researchers with the increased related studies. However, the corresponding related summaries are lacking and needed. Different antigens, especially hapten antigens, show distinct binding modes with sdAbs. So, in this paper, the unique paratope structure of sdAbs, different antigen binding cases, and the current maturation strategy of sdAbs were classified and summarized. We hope this review lays a theoretical foundation to elucidate the antigen-binding mechanism of sdAbs and broaden the further application of sdAbs.

Combinatorial biology methods such as phage and yeast display, suitable for the generation and screening of huge numbers of protein fragments and mutated variants, have been useful when dissecting the molecular details of the interactions between antibodies and their target antigens (mainly those of protein nature). The relevance of these studies goes far beyond the mere description of binding interfaces, as the information obtained has implications for the understanding of the chemistry of antibody–antigen binding reactions and the biological effects of antibodies. Further modification of the interactions through combinatorial methods to manipulate the key properties of antibodies (affinity and fine specificity) can result in the emergence of novel research tools and optimized therapeutics. [ABSTRACT FROM AUTHOR]

Abstract Background A conformational epitope (CE) is composed of neighboring amino acid residues located on an antigenic protein surface structure. CEs bind their complementary paratopes in B-cell receptors and/or antibodies. An effective and efficient prediction tool for CE analysis is critical for the development of immunology-related applications, such as vaccine design and disease diagnosis. Results We propose a novel method consisting of two sequential modules: matching and prediction. The matching module includes two main approaches. The first approach is a complete sequence search (CSS) that applies BLAST to align the sequence with all known antigen sequences. Fragments with high epitope sequence identities are identified and the predicted residues are annotated on the query structure. The second approach is a spiral vector search (SVS) that adopts a novel surface spiral feature vector for large-scale surface patch detection when queried against a comprehensive epitope database. The prediction module also contains two proposed subsystems. The first system is based on knowledge-based energy and geometrical neighboring residue contents, and the second system adopts combinatorial features, including amino acid contents and physicochemical characteristics, to formulate corresponding geometric spiral vectors and compare them with all spiral vectors from known CEs. An integrated testing dataset was generated for method evaluation, and our two searching methods effectively identified all epitope regions. The prediction results show that our proposed method outperforms previously published systems in terms of sensitivity, specificity, positive predictive value, and accuracy. Conclusions The proposed method significantly improves the performance of traditional epitope prediction. Matching followed by prediction is an efficient and effective approach compared to predicting directly on specific surfaces containing antigenic characteristics.

The dynamic interplay between virus and host plays out across many interacting surfaces as virus and host evolve continually in response to one another. In particular, epitope-paratope interactions (EPIs) between viral antigen and host antibodies drive much of this evolutionary race. In this review, we describe a series of recent studies examining aspects of epitope complexity that go beyond two interacting protein surfaces as EPIs are typically understood. To structure our discussion, we present a framework for understanding epitope complexity as a spectrum along a series of axes, focusing primarily on 1) epitope biochemical complexity (e.g., epitopes involving N-glycans) and 2) antigen conformational/dynamic complexity (e.g., epitopes with differential properties depending on antigen state or fold-axis). We highlight additional epitope complexity factors including epitope tertiary/quaternary structure, which contribute to epistatic relationships between epitope residues within- or adjacent-to a given epitope, as well as epitope overlap resulting from polyclonal antibody responses, which is relevant when assessing antigenic pressure against a given epitope. Finally, we discuss how these different forms of epitope complexity can limit EPI analyses and therapeutic antibody development, as well as recent efforts to overcome these limitations. [ABSTRACT FROM AUTHOR]

Background: Antibody validation for tissue staining is required for reproducibility; criteria to ensure validity have been published recently. The majority of these recommendations imply the use of routinely processed (formalin-fixed, paraffin-embedded) tissue. Materials & methods: We applied to lightly fixed frozen sections a panel of 126 antibodies validated for formalin-fixed, paraffin-embedded tissue with extended criteria. Results: Less than 30% of the antibodies performed as expected with all fixations. 35% preferred one fixation over another, 13% gave nonspecific staining and 23% did not stain at all. Conclusion: Individual antibody variability of the paratope’s fitness for the fixed antigen may be the cause. Revalidation of established antibody panels is required when they are applied to sections whose fixation and processing are different from the tissue where they were initially validated.

The dynamic interplay between virus and host plays out across many interacting surfaces as virus and host evolve continually in response to one another. In particular, epitope-paratope interactions (EPIs) between viral antigen and host antibodies drive much of this evolutionary race. In this review, we describe a series of recent studies examining aspects of epitope complexity that go beyond two interacting protein surfaces as EPIs are typically understood. To structure our discussion, we present a framework for understanding epitope complexity as a spectrum along a series of axes, focusing primarily on 1) epitope biochemical complexity (e.g., epitopes involving N-glycans) and 2) antigen conformational/dynamic complexity (e.g., epitopes with differential properties depending on antigen state or fold-axis). We highlight additional epitope complexity factors including epitope tertiary/quaternary structure, which contribute to epistatic relationships between epitope residues within- or adjacent-to a given epitope, as well as epitope overlap resulting from polyclonal antibody responses, which is relevant when assessing antigenic pressure against a given epitope. Finally, we discuss how these different forms of epitope complexity can limit EPI analyses and therapeutic antibody development, as well as recent efforts to overcome these limitations.

Antibody‐mediated enzyme formation is a phenomenon first described in 1968 and further studied by molecular Immunologists and Biochemists over the following five decades. The present review is made mainly by analyzing the 27 articles concerned with AMEF that appeared over the course of 47 years, commenting 16 original figures selected to be re‐printed in AMEF's Legacy. We, the reviewers, started by revisiting our own "insider's" experience of discovery, and followed by considering all results, our own and of members of other AMEF Labs. We had planned to conclude the review by correlating the various AMEF mutants to a detailed knowledge of the consensus betaGal structure. However, we became aware of several "robust" papers, published between 1989 and 2014, by authors outside of AMEF Labs. We familiarly called this surge: "The Second Wave" and adorned it with a doodle in Hokusai style. We were thrilled and happy to take them on board and properly examined their data. A team of this second wave had imagined unique uses for AMEF, and new doors to modern biotechnology. Another one had used AMEF as Tool and Marker to attain high levels of crystallography, solving puzzles of conformation, and ultimate structure. Together, they doubled our motivation to review AMEF. Serendipity gives us back the pleasure of finding, a treat at any age. [ABSTRACT FROM AUTHOR]

Several human monoclonal Abs for treating Influenza have been evaluated in clinical trials with limited success despite demonstrating superiority in preclinical animal models including mice. To conduct efficacy studies in mice, human monoclonal Abs are genetically engineered to contain mouse heavy chain constant domain to facilitate the engagement of Fc-receptors on mouse immune effector cells. Although studies have consistently reported discrepancies in Ab effectiveness following genetic engineering, the structural and mechanistic basis for these inconsistencies remain uncharacterized. Here, we use homology modeling to predict variable region (VR) analogous monoclonal Abs possessing human IgG1, mouse IgG1, and mouse IgG2a heavy chain constant domains. We then examine predicted 3D structures for variations in the spatial location and orientation of corresponding paratope amino acid residues. By structurally aligning crystal structures of Fabs in complex with hemagglutinin (HA), we show that corresponding paratope amino acid residues for VR-analogous human IgG1, mouse IgG1, and mouse IgG2a monoclonal Abs interact differentially with HA suggesting that their epitopes might not be identical. To demonstrate that variations in the paratope 3D fine architecture have implications for Ab specificity and effectiveness, we genetically engineered VR-analogous human IgG1, human IgG4, mouse IgG1, and mouse IgG2a monoclonal Abs and explored their specificity and effectiveness in protecting MDCK cells from infection by pandemic H1N1 and H3N2 Influenza viruses. We found that VR-analogous monoclonal Abs placed on mouse heavy chain constant domains were more efficacious at protecting MDCK cells from Influenza virus infection relative to those on human heavy chain constant domains. Interestingly, mouse but not human heavy chain constant domains increased target breadth in some monoclonal Abs. These data suggest that heavy chain constant domain sequences play a role in shaping Ab repertoires that go beyond class or sub-class differences in immune effector recruitment. This represents a facet of Ab biology that can potentially be exploited to improve the scope and utilization of current therapeutic or prophylactic candidates for influenza.

Background: A conformational epitope (CE) is composed of neighboring amino acid residues located on an antigenic protein surface structure. CEs bind their complementary paratopes in B-cell receptors and/or antibodies. An effective and efficient prediction tool for CE analysis is critical for the development of immunology-related applications, such as vaccine design and disease diagnosis. Results: We propose a novel method consisting of two sequential modules: matching and prediction. The matching module includes two main approaches. The first approach is a complete sequence search (CSS) that applies BLAST to align the sequence with all known antigen sequences. Fragments with high epitope sequence identities are identified and the predicted residues are annotated on the query structure. The second approach is a spiral vector search (SVS) that adopts a novel surface spiral feature vector for large-scale surface patch detection when queried against a comprehensive epitope database. The prediction module also contains two proposed subsystems. The first system is based on knowledge-based energy and geometrical neighboring residue contents, and the second system adopts combinatorial features, including amino acid contents and physicochemical characteristics, to formulate corresponding geometric spiral vectors and compare them with all spiral vectors from known CEs. An integrated testing dataset was generated for method evaluation, and our two searching methods effectively identified all epitope regions. The prediction results show that our proposed method outperforms previously published systems in terms of sensitivity, specificity, positive predictive value, and accuracy. Conclusions: The proposed method significantly improves the performance of traditional epitope prediction. Matching followed by prediction is an efficient and effective approach compared to predicting directly on specific surfaces containing antigenic characteristics. [ABSTRACT FROM AUTHOR]

Combinatorial biology methods such as phage and yeast display, suitable for the generation and screening of huge numbers of protein fragments and mutated variants, have been useful when dissecting the molecular details of the interactions between antibodies and their target antigens (mainly those of protein nature). The relevance of these studies goes far beyond the mere description of binding interfaces, as the information obtained has implications for the understanding of the chemistry of antibody–antigen binding reactions and the biological effects of antibodies. Further modification of the interactions through combinatorial methods to manipulate the key properties of antibodies (affinity and fine specificity) can result in the emergence of novel research tools and optimized therapeutics.

Antibody-based drugs, which now represent the dominant biologic therapeutic modality, are used to modulate disparate signaling pathways across diverse disease indications. One fundamental premise that has driven this therapeutic antibody revolution is the belief that each monoclonal antibody exhibits exquisitely specific binding to a single-drug target. Herein, we review emerging evidence in antibody off-target binding and relate current key findings to the risk of failure in therapeutic development. We further summarize the current state of understanding of structural mechanisms underpining the different phenomena that may drive polyreactivity and polyspecificity, and highlight current thinking on how de-risking studies may be best implemented in the screening triage. We conclude with a summary of what we believe to be key observations in the field to date, and a call for the wider antibody research community to work together to build the tools needed to maximize our understanding in this nascent area.

Due to their shared genetic history, antibodies from the same clonotype often bind to the same epitope. This knowledge is used in immune repertoire mining, where known binders are used to search bulk sequencing repertoires to identify new binders. However, current computational methods cannot identify epitope convergence between antibodies from different clonotypes, limiting the sequence diversity of antigen-specific antibodies that can be identified. We describe how the antibody binding site, the paratope, can be used to cluster antibodies with common antigen reactivity from different clonotypes. Our method, paratyping, uses the predicted paratope to identify these novel cross clonotype matches. We experimentally validated our predictions on a pertussis toxoid dataset. Our results show that even the simplest abstraction of the antibody binding site, using only the length of the loops involved and predicted binding residues, is sufficient to group antigen-specific antibodies and provide additional information to conventional clonotype analysis.Abbreviations: BCR: B-cell receptor; CDR: complementarity-determining region; PTx: pertussis toxoid

Solving the structure of an antibody-antigen complex gives atomic level information of the interactions between an antibody and its antigen, but such structures are expensive and hard to obtain. Alternative experimental sources include epitope mapping and binning experiments, which can be used as a surrogate to identify key interacting residues. However, their resolution is usually not sufficient to identify if two antibodies have identical interactions. Computational approaches to this problem have so far been based on the premise that antibodies with similar sequences behave similarly. Such approaches will fail to identify sequence-distant antibodies that target the same epitope. Here, we present Ab-Ligity, a structure-based similarity measure tailored to antibody-antigen interfaces. Using predicted paratopes on model antibody structures, we assessed its ability to identify those antibodies that target highly similar epitopes. Most antibodies adopting similar binding modes can be identified from sequence similarity alone, using methods such as clonotyping. In the challenging subset of antibodies whose sequences differ significantly, Ab-Ligity is still able to predict antibodies that would bind to highly similar epitopes (precision of 0.95 and recall of 0.69). We compared Ab-Ligity’s performance to an existing tool for comparing general protein interfaces, InterComp, and showed improved performance on antibody cases achieved in a substantially reduced time. These results suggest that Ab-Ligity will allow the identification of diverse (sequence-dissimilar) antibodies that bind to the same epitopes from large datasets such as immune repertoires. The tool is available at http://opig.stats.ox.ac.uk/resources.

The evolution of mutations in SARS-CoV-2 at antigenic sites that impact neutralizing antibody responses in humans poses a risk to immunity developed through vaccination and natural infection. The highly successful RNA-based vaccines have enabled rapid vaccine updates that incorporate mutations from current variants of concern (VOCs). It is therefore important to anticipate future antigenic mutations as the virus navigates the heterogeneous global landscape of host immunity. Toward this goal, we survey epitope-paratope interfaces of anti-SARS-CoV-2 antibodies to map an antigenic space that captures the role of each spike protein residue within the polyclonal antibody response directed against the ACE2-receptor binding domain (RBD) or the N-terminal domain (NTD). In particular, the antigenic space map builds on recently published epitope definitions by annotating epitope overlap and orthogonality at the residue level. We employ the antigenic space map as a framework to understand how mutations on nine major variants contribute to each variant’s evasion of neutralizing antibodies. Further, we identify constellations of mutations that span the orthogonal epitope regions of the RBD and NTD on the variants with the greatest antibody escape. Finally, we apply the antigenic space map to predict which regions of antigenic space—should they mutate—may be most likely to complementarily augment antibody evasion for the most evasive and transmissible VOCs.

B-cells can neutralize pathogenic molecules by targeting them with extreme specificity using receptors secreted or expressed on their surface (antibodies). This is achieved via molecular interactions between the paratope (i.e., the antibody residues involved in the binding) and the interacting region (epitope) of its target molecule (antigen). Discerning the rules that define this specificity would have profound implications for our understanding of humoral immunogenicity and its applications. The aim of this work is to produce improved, antibody-specific epitope predictions by exploiting features derived from the antigens and their cognate antibodies structures, and combining them using statistical and machine learning algorithms. We have identified several geometric and physicochemical features that are correlated in interacting paratopes and epitopes, used them to develop a Monte Carlo algorithm to generate putative epitopes-paratope pairs, and train a machine-learning model to score them. We show that, by including the structural and physicochemical properties of the paratope, we improve the prediction of the target of a given B-cell receptor. Moreover, we demonstrate a gain in predictive power both in terms of identifying the cognate antigen target for a given antibody and the antibody target for a given antigen, exceeding the results of other available tools.

Hepatitis B virus (HBV) is a global health burden that causes acute and chronic hepatitis. To develop an HBV-neutralizing antibody that effectively prevents HBV infection, we previously generated a human anti-preS1 monoclonal antibody (1A8) that binds to genotypes A–D and validated its HBV-neutralizing activity in vitro. In the present study, we aimed to determine the fine epitope and paratope of 1A8 to understand the mechanism of HBV neutralization. We performed alanine-scanning mutagenesis on the preS1 (aa 19–34, genotype C) and the heavy (HCDR) and light (LCDR) chain complementarity-determining regions. The 1A8 recognized the three residues (Leu22, Gly23, and Phe25) within the highly conserved receptor-binding motif (NPLGFFP) of the preS1, while four CDR residues of 1A8 were critical in antigen binding. Structural analysis of the epitope–paratope interaction by molecular modeling revealed that Leu100 in the HCDR3, Ala50 in the HCDR2, and Tyr96 in the LCDR3 closely interacted with Leu22, Gly23, and Phe25 of the preS1. Additionally, we found that 1A8 also binds to the receptor-binding motif (NPLGFLP) of infrequently occurring HBV. The results suggest that 1A8 may broadly and effectively block HBV entry and thus have potential as a promising candidate for the prevention and treatment of HBV infection.

Antibodies are a critical part of the immune system, having the function of recognizing and mediating the neutralization of undesirable molecules (antigens) for future destruction. Being able to predict which amino acids belong to theparatope, the region on the antibody that binds to the antigen, can facilitate antibody engineering and predictions of antibody-antigen structures. The suitability of deep neural networks has recently been confirmed for this task, with Parapred outperforming all prior models. In this work, we first significantly outperform the computational efficiency of Parapred by leveraging à trous convolutions and self-attention. Second, we implementcross-modal attentionby allowing the antibody residues to attend over antigen residues. This leads to new state-of-the-art results in paratope prediction, along with novel opportunities to interpret the outcome of the prediction. [ABSTRACT FROM AUTHOR]

B-cells can neutralize pathogenic molecules by targeting them with extreme specificity using receptors secreted or expressed on their surface (antibodies). This is achieved via molecular interactions between the paratope (i.e., the antibody residues involved in the binding) and the interacting region (epitope) of its target molecule (antigen). Discerning the rules that define this specificity would have profound implications for our understanding of humoral immunogenicity and its applications. The aim of this work is to produce improved, antibody-specific epitope predictions by exploiting features derived from the antigens and their cognate antibodies structures, and combining them using statistical and machine learning algorithms. We have identified several geometric and physicochemical features that are correlated in interacting paratopes and epitopes, used them to develop a Monte Carlo algorithm to generate putative epitopes-paratope pairs, and train a machine-learning model to score them. We show that, by including the structural and physicochemical properties of the paratope, we improve the prediction of the target of a given B-cell receptor. Moreover, we demonstrate a gain in predictive power both in terms of identifying the cognate antigen target for a given antibody and the antibody target for a given antigen, exceeding the results of other available tools. [ABSTRACT FROM AUTHOR]

Microcontact printing (µCP) is a practical and versatile approach to create nanostructured patterns of biomolecular probes, but it involves conformational changes on the patterned bioreceptors that often lead to a loss on the biological activity of the resulting structures. Herein we introduce indirect µCP to create functional patterns of bioreceptors on solid substrates. This is a simple strategy that relies on physisorbing biomolecular probes of interest in the nanostructured gaps that result after patterning backfilling agents by standard µCP. This study presents the approach, assesses bovine serum albumin as backfilling agent for indirect µCP on different materials, reports the limitations of standard µCP on the functionality of patterned antibodies, and demonstrates the capabilities of indirect µCP to solve this issue. Bioreceptors were herein structured as diffractive gratings and used to measure biorecognition events in label-free conditions. Besides, as a preliminary approach towards sensing biomarkers, this work also reports the implementation of indirect µCP in an immunoassay to detect human immunoglobulin E. [ABSTRACT FROM AUTHOR]

Nanobodies (Nbs) are a class of antigen-binding protein derived from camelid immune systems, which achieve equivalent binding affinities and specificities to classical antibodies (Abs) despite being comprised of only a single variable domain. Here, we use a data set of 156 unique Nb:antigen complex structures to characterize Nb–antigen binding and draw comparison to a set of 156 unique Ab:antigen structures. We analyse residue composition and interactions at the antigen interface, together with structural features of the paratopes of both data sets. Our analysis finds that the set of Nb structures displays much greater paratope diversity, in terms of the structural segments involved in the paratope, the residues used at these positions to contact the antigen and furthermore the type of contacts made with the antigen. Our findings suggest a different relationship between contact propensity and sequence variability from that observed for Ab VH domains. The distinction between sequence positions that control interaction specificity and those that form the domain scaffold is much less clear-cut for Nbs, and furthermore H3 loop positions play a much more dominant role in determining interaction specificity. [ABSTRACT FROM AUTHOR]

The ability of the human immune system to generate antibodies to any given antigen can be strongly influenced by immunoglobulin V-gene allelic polymorphisms. However, previous studies have provided only limited examples. Therefore, the prevalence of this phenomenon has been unclear. By analyzing >1,000 publicly available antibody-antigen structures, we show that many V-gene allelic polymorphisms in antibody paratopes are determinants for antibody binding activity. Biolayer interferometry experiments further demonstrate that paratope allelic polymorphisms on both heavy and light chains often abolish antibody binding. We also illustrate the importance of minor V-gene allelic polymorphisms with low frequency in several broadly neutralizing antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza virus. Overall, this study not only highlights the pervasive impact of V-gene allelic polymorphisms on antibody binding but also provides mechanistic insights into the variability of antibody repertoires across individuals, which in turn have important implications for vaccine development and antibody discovery. [Display omitted] • Analysis of V-gene allelic polymorphisms in 1,048 antibody-antigen complex structures • Many antibodies contain allelic polymorphisms predicted to disrupt binding • These include antibodies with different V genes and to different pathogens • Some broadly neutralizing antibodies depend on minor V-gene allelic polymorphisms By analyzing >1,000 publicly available antibody-antigen complex structures, Yuan et al. demonstrate that antibody binding activity is often influenced by V-gene allelic polymorphisms. This result provides mechanistic insights into the variability of antibody repertoires across individuals, which in turn have important implications for vaccine development and antibody discovery. [ABSTRACT FROM AUTHOR]

Since the death of Jean Genet, his name and oeuvre have been the subject of heated debate. Influential critics have argued that Genet was an aristocratic anti-Semite, rather than a revolutionary poet who took sides with the outcasts. In this article, I analyze the positions, patterns, and strategies of this multifaceted debate, suggesting that provocation and marginalization constitute an integral part of Genet’s aesthetics. In the act of judging Genet from historical, political, and ethical perspectives, the critics operate as executors of his literary project, confirming the paratopic position the writer presumably desired.

At the 10th Human Genome Mapping (HGM10) Workshop, in New Haven, for the first time, immunoglobulin (IG) or antibody and T cell receptor (TR) variable (V), diversity (D), joining (J), and constant (C) genes were officially recognized as ‘genes’, as were the conventional genes. Under these HGM auspices, IMGT®, the international ImMunoGeneTics information system®, was created in June 1989 at Montpellier (University of Montpellier and CNRS). The creation of IMGT® marked the birth of immunoinformatics, a new science, at the interface between immunogenetics and bioinformatics. The accuracy and the consistency between genes and alleles, sequences, and three-dimensional (3D) structures are based on the IMGT Scientific chart rules generated from the IMGT-ONTOLOGY axioms and concepts: IMGT standardized keywords (IDENTIFICATION), IMGT gene and allele nomenclature (CLASSIFICATION), IMGT standardized labels (DESCRIPTION), IMGT unique numbering and IMGT Collier de Perles (NUMEROTATION). These concepts provide IMGT® immunoinformatics insights for antibody V and C domain structure and function, used for the standardized description in IMGT® web resources, databases and tools, immune repertoires analysis, single cell and/or high-throughput sequencing (HTS, NGS), antibody humanization, and antibody engineering in relation with effector properties.