Structural insight into SARS-CoV-2 neutralizing antibodies and modulation of syncytia.

Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is initiated by binding of the viral Spike protein to host receptor angiotensin-converting enzyme 2 (ACE2), followed by fusion of viral and host membranes. Although antibodies that block this interaction are in emergency use as early coronavirus disease 2019 (COVID-19) therapies, the precise determinants of neutralization potency remain unknown. We discovered a series of antibodies that potently block ACE2 binding but exhibit divergent neutralization efficacy against the live virus. Strikingly, these neutralizing antibodies can inhibit or enhance Spike-mediated membrane fusion and formation of syncytia, which are associated with chronic tissue damage in individuals with COVID-19. As revealed by cryoelectron microscopy, multiple structures of Spike-antibody complexes have distinct binding modes that not only block ACE2 binding but also alter the Spike protein conformational cycle triggered by ACE2 binding. We show that stabilization of different Spike conformations leads to modulation of Spike-mediated membrane fusion with profound implications for COVID-19 pathology and immunity.

Reduced neutralization of SARS-CoV-2 B.1.617 by vaccine and convalescent serum.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has undergone progressive change, with variants conferring advantage rapidly becoming dominant lineages, e.g., B.1.617. With apparent increased transmissibility, variant B.1.617.2 has contributed to the current wave of infection ravaging the Indian subcontinent and has been designated a variant of concern in the United Kingdom. Here we study the ability of monoclonal antibodies and convalescent and vaccine sera to neutralize B.1.617.1 and B.1.617.2, complement this with structural analyses of Fab/receptor binding domain (RBD) complexes, and map the antigenic space of current variants. Neutralization of both viruses is reduced compared with ancestral Wuhan-related strains, but there is no evidence of widespread antibody escape as seen with B.1.351. However, B.1.351 and P.1 sera showed markedly more reduction in neutralization of B.1.617.2, suggesting that individuals infected previously by these variants may be more susceptible to reinfection by B.1.617.2. This observation provides important new insights for immunization policy with future variant vaccines in non-immune populations.

B cell genomics behind cross-neutralization of SARS-CoV-2 variants and SARS-CoV.

Monoclonal antibodies (mAbs) are a focus in vaccine and therapeutic design to counteract severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants. Here, we combined B cell sorting with single-cell VDJ and RNA sequencing (RNA-seq) and mAb structures to characterize B cell responses against SARS-CoV-2. We show that the SARS-CoV-2-specific B cell repertoire consists of transcriptionally distinct B cell populations with cells producing potently neutralizing antibodies (nAbs) localized in two clusters that resemble memory and activated B cells. Cryo-electron microscopy structures of selected nAbs from these two clusters complexed with SARS-CoV-2 spike trimers show recognition of various receptor-binding domain (RBD) epitopes. One of these mAbs, BG10-19, locks the spike trimer in a closed conformation to potently neutralize SARS-CoV-2, the recently arising mutants B.1.1.7 and B.1.351, and SARS-CoV and cross-reacts with heterologous RBDs. Together, our results characterize transcriptional differences among SARS-CoV-2-specific B cells and uncover cross-neutralizing Ab targets that will inform immunogen and therapeutic design against coronaviruses.

Anti-HA Glycoforms Drive B Cell Affinity Selection and Determine Influenza Vaccine Efficacy.

Protective vaccines elicit high-affinity, neutralizing antibodies by selection of somatically hypermutated B cell antigen receptors (BCR) on immune complexes (ICs). This implicates Fc-Fc receptor (FcR) interactions in affinity maturation, which, in turn, are determined by IgG subclass and Fc glycan composition within ICs. Trivalent influenza virus vaccination elicited regulation of anti-hemagglutinin (HA) IgG subclass and Fc glycans, with abundance of sialylated Fc glycans (sFc) predicting quality of vaccine response. We show that sFcs drive BCR affinity selection by binding the Type-II FcR CD23, thus upregulating the inhibitory FcγRIIB on activated B cells. This elevates the threshold requirement for BCR signaling, resulting in B cell selection for higher affinity BCR. Immunization with sFc HA ICs elicited protective, high-affinity IgGs against the conserved stalk of the HA. These results reveal a novel, endogenous pathway for affinity maturation that can be exploited for eliciting high-affinity, broadly neutralizing antibodies through immunization with sialylated immune complexes.

Structural basis for Marburg virus neutralization by a cross-reactive human antibody.

The filoviruses, including Marburg and Ebola, express a single glycoprotein on their surface, termed GP, which is responsible for attachment and entry of target cells. Filovirus GPs differ by up to 70% in protein sequence, and no antibodies are yet described that cross-react among them. Here, we present the 3.6 Å crystal structure of Marburg virus GP in complex with a cross-reactive antibody from a human survivor, and a lower resolution structure of the antibody bound to Ebola virus GP. The antibody, MR78, recognizes a GP1 epitope conserved across the filovirus family, which likely represents the binding site of their NPC1 receptor. Indeed, MR78 blocks binding of the essential NPC1 domain C. These structures and additional small-angle X-ray scattering of mucin-containing MARV and EBOV GPs suggest why such antibodies were not previously elicited in studies of Ebola virus, and provide critical templates for development of immunotherapeutics and inhibitors of entry.

Improved deep learning prediction of antigen-antibody interactions.

Identifying antibodies that neutralize specific antigens is crucial for developing effective immunotherapies, but this task remains challenging for many target antigens. The rise of deep learning-based computational approaches presents a promising avenue to address this challenge. Here, we assess the performance of a deep learning approach through two benchmark tests aimed at predicting antibodies for the receptor-binding domain of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. Three different strategies for constructing input sequence alignments are employed for predicting structural models of antigen-antibody complexes. In our initial testing set, which comprises known experimental structures, these strategies collectively yield a significant top-ranked prediction for 61% of cases and a success rate of 47%. Notably, one strategy that utilizes the sequences of known antigen binders outperforms the other two, achieving a precision of 90% in a subsequent test set of ~1,000 antibodies, balanced between true and control antibodies for the antigen, albeit with a lower recall of 25%. Our results underscore the potential of integrating deep learning methods with single B cell sequencing techniques to enhance the prediction accuracy of antigen-antibody interactions.

AttABseq: an attention-based deep learning prediction method for antigen-antibody binding affinity changes based on protein sequences.

The optimization of therapeutic antibodies through traditional techniques, such as candidate screening via hybridoma or phage display, is resource-intensive and time-consuming. In recent years, computational and artificial intelligence-based methods have been actively developed to accelerate and improve the development of therapeutic antibodies. In this study, we developed an end-to-end sequence-based deep learning model, termed AttABseq, for the predictions of the antigen-antibody binding affinity changes connected with antibody mutations. AttABseq is a highly efficient and generic attention-based model by utilizing diverse antigen-antibody complex sequences as the input to predict the binding affinity changes of residue mutations. The assessment on the three benchmark datasets illustrates that AttABseq is 120% more accurate than other sequence-based models in terms of the Pearson correlation coefficient between the predicted and experimental binding affinity changes. Moreover, AttABseq also either outperforms or competes favorably with the structure-based approaches. Furthermore, AttABseq consistently demonstrates robust predictive capabilities across a diverse array of conditions, underscoring its remarkable capacity for generalization across a wide spectrum of antigen-antibody complexes. It imposes no constraints on the quantity of altered residues, rendering it particularly applicable in scenarios where crystallographic structures remain unavailable. The attention-based interpretability analysis indicates that the causal effects of point mutations on antibody-antigen binding affinity changes can be visualized at the residue level, which might assist automated antibody sequence optimization. We believe that AttABseq provides a fiercely competitive answer to therapeutic antibody optimization.

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

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

Genetic and structural basis of the human anti-α-galactosyl antibody response.

Humans lack the capacity to produce the Galα1-3Galß1-4GlcNAc (α-gal) glycan, and produce anti-α-gal antibodies upon exposure to the carbohydrate on a diverse set of immunogens, including commensal gut bacteria, malaria parasites, cetuximab, and tick proteins. Here we use X-ray crystallographic analysis of antibodies from α-gal knockout mice and humans in complex with the glycan to reveal a common binding motif, centered on a germline-encoded tryptophan residue at Kabat position 33 (W33) of the complementarity-determining region of the variable heavy chain (CDRH1). Immunoglobulin sequencing of anti-α-gal B cells in healthy humans and tick-induced mammalian meat anaphylaxis patients revealed preferential use of heavy chain germline IGHV3-7, encoding W33, among an otherwise highly polyclonal antibody response. Antigen binding was critically dependent on the presence of the germline-encoded W33 residue for all of the analyzed antibodies; moreover, introduction of the W33 motif into naive IGHV3-23 antibody phage libraries enabled the rapid selection of α-gal binders. Our results outline structural and genetic factors that shape the human anti-α-galactosyl antibody response, and provide a framework for future therapeutics development.

Structure and conformational variability of the HER2-trastuzumab-pertuzumab complex.

Single particle analysis from cryogenic transmission electron microscopy (cryo-EM) is particularly attractive for complexes for which structure prediction remains intractable, such as antibody-antigen complexes. Here we obtain the detailed structure of a particularly difficult complex between human epidermal growth factor receptor 2 (HER2) and the antigen-binding fragments from two distinct therapeutic antibodies binding to distant parts of the flexible HER2, pertuzumab and trastuzumab (HTP). We highlight the strengths and limitations of current data processing software in dealing with various kinds of heterogeneities, particularly continuous conformational heterogeneity, and in describing the motions that can be extracted from our dataset. Our HTP structure provides a more detailed view than the one previously available for this ternary complex. This allowed us to pinpoint a previously overlooked loop in domain IV that may be involved both in binding of trastuzumab and in HER2 dimerization. This finding may contribute to explain the synergistic anticancer effect of the two antibodies. We further propose that the flexibility of the HTP complex, beyond the difficulties it causes for cryo-EM analysis, actually reflects regulation of HER2 signaling and its inhibition by therapeutic antibodies. Notably we obtain our best data with ultra-thin continuous carbon grids, showing that with current cameras their use to alleviate particle misdistribution is compatible with a protein complex of only 162 kDa. Perhaps most importantly, we provide here a dataset for such a smallish protein complex for further development of software accounting for continuous conformational heterogeneity in cryo-EM images.

DNA Nanostructure-Templated Antibody Complexes Provide Insights into the Geometric Requirements of Human Complement Cascade Activation.

The classical complement pathway is activated by antigen-bound IgG antibodies. Monomeric IgG must oligomerize to activate complement via the hexameric C1q complex, and hexamerizing mutants of IgG appear as promising therapeutic candidates. However, structural data have shown that it is not necessary to bind all six C1q arms to initiate complement, revealing a symmetry mismatch between C1 and the hexameric IgG complex that has not been adequately explained. Here, we use DNA nanotechnology to produce specific nanostructures to template antigens and thereby spatially control IgG valency. These DNA-nanotemplated IgG complexes can activate complement on cell-mimetic lipid membranes, which enabled us to determine the effect of IgG valency on complement activation without the requirement to mutate antibodies. We investigated this using biophysical assays together with 3D cryo-electron tomography. Our data revealed the importance of interantigen distance on antibody-mediated complement activation, and that the cleavage of complement component C4 by the C1 complex is proportional to the number of ideally spaced antigens. Increased IgG valency also translated to better terminal pathway activation and membrane attack complex formation. Together, these data provide insights into how nanopatterning antigen-antibody complexes influence the activation of the C1 complex and suggest routes to modulate complement activation by antibody engineering. Furthermore, to our knowledge, this is the first time DNA nanotechnology has been used to study the activation of the complement system.

Structural Characterization of Monoclonal Antibodies and Epitope Mapping by FFAP Footprinting.

Covalent labeling in combination with mass spectrometry is a powerful approach used in structural biology to study protein structures, interactions, and dynamics. Recently, the toolbox of covalent labeling techniques has been expanded with fast fluoroalkylation of proteins (FFAP). FFAP is a novel radical labeling method that utilizes fluoroalkyl radicals generated from hypervalent Togni reagents for targeting aromatic residues. This report further demonstrates the benefits of FFAP as a new method for structural characterization of therapeutic antibodies and interaction interfaces of antigen-antibody complexes. The results obtained from human trastuzumab and its complex with human epidermal growth factor receptor 2 (HER2) correlate well with previously published structural data and demonstrate the potential of FFAP in structural biology.

Prediction of Paratope-Epitope Pairs Using Convolutional Neural Networks.

Antibodies play a central role in the adaptive immune response of vertebrates through the specific recognition of exogenous or endogenous antigens. The rational design of antibodies has a wide range of biotechnological and medical applications, such as in disease diagnosis and treatment. However, there are currently no reliable methods for predicting the antibodies that recognize a specific antigen region (or epitope) and, conversely, epitopes that recognize the binding region of a given antibody (or paratope). To fill this gap, we developed ImaPEp, a machine learning-based tool for predicting the binding probability of paratope-epitope pairs, where the epitope and paratope patches were simplified into interacting two-dimensional patches, which were colored according to the values of selected features, and pixelated. The specific recognition of an epitope image by a paratope image was achieved by using a convolutional neural network-based model, which was trained on a set of two-dimensional paratope-epitope images derived from experimental structures of antibody-antigen complexes. Our method achieves good performances in terms of cross-validation with a balanced accuracy of 0.8. Finally, we showcase examples of application of ImaPep, including extensive screening of large libraries to identify paratope candidates that bind to a selected epitope, and rescoring and refining antibody-antigen docking poses.

Internalization of HIV-1 by Phagocytes Is Increased When Virions Are Opsonized with Multimeric Antibody in the Presence of Complement.

The low abundance of envelope spikes and the inability of IgG to aggregate virions render HIV-1 an inadequate target for antibody-mediated clearance by phagocytes. In an attempt to improve the ability of antibody to mediate the internalization of HIV-1 virions, we generated multimers of the broadly neutralizing HIV-1-specific monoclonal antibody (MAb) VRC01 using site-directed mutagenesis of the Fc segment. We then measured virion internalization using primary human monocytes and neutrophils. We found that, in the absence of complement, immune complexes consisting of HIV-1 virions and VRC01 multimers were slightly more efficiently internalized than were complexes formed with monomeric VRC01. The presence of complement, however, greatly augmented internalization of immune complexes formed with the multimeric MAb but had little impact on monomeric MAb-mediated internalization. Multimerization and the presence of complement overcome the limited ability of monomeric antibody to mediate internalization of HIV-1 virions and may thus provide a therapeutic approach to clearing virus. IMPORTANCE Antibody-mediated internalization of HIV-1 by phagocytes, a potential mechanism for clearing virus, is very inefficient. In an effort to improve viral clearance, we produced a multimeric form of the broadly neutralizing monoclonal antibody VRC01. We found that VRC01 antibody multimers (primarily hexamers) were only slightly more efficient in mediating HIV-1 internalization than was monomeric VRC01. However, the addition of complement resulted in substantially greater internalization of multimer-opsonized virus. In contrast, complement had little if any impact on internalization of monomer-opsonized virus. Therefore, antibody multimerization in combination with complement may overcome the limited ability of monomeric antibody to mediate internalization of HIV-1 virions. Our findings may provide a therapeutic approach to clearing virus.

IsAb: a computational protocol for antibody design.

The design of therapeutic antibodies has attracted a large amount of attention over the years. Antibodies are widely used to treat many diseases due to their high efficiency and low risk of adverse events. However, the experimental methods of antibody design are time-consuming and expensive. Although computational antibody design techniques have had significant advances in the past years, there are still some challenges that need to be solved, such as the flexibility of antigen structure, the lack of antibody structural data and the absence of standard antibody design protocol. In the present work, we elaborated on an in silico antibody design protocol for users to easily perform computer-aided antibody design. First, the Rosetta web server will be applied to generate the 3D structure of query antibodies if there is no structural information available. Then, two-step docking will be used to identify the binding pose of an antibody-antigen complex when the binding information is unknown. ClusPro is the first method to be used to conduct the global docking, and SnugDock is applied for the local docking. Sequentially, based on the predicted binding poses, in silico alanine scanning will be used to predict the potential hotspots (or key residues). Finally, computational affinity maturation protocol will be used to modify the structure of antibodies to theoretically increase their affinity and stability, which will be further validated by the bioassays in the future. As a proof of concept, we redesigned antibody D44.1 and compared it with previously reported data in order to validate IsAb protocol. To further illustrate our proposed protocol, we used cemiplimab antibody, a PD-1 checkpoint inhibitor, as an example to showcase a step-by-step tutorial.

N-glycan-mediated shielding of ADAMTS13 prevents binding of pathogenic autoantibodies in immune-mediated TTP.

Immune-mediated thrombotic thrombocytopenic purpura (iTTP) is an autoimmune disorder caused by the development of autoantibodies targeting different domains of ADAMTS13. Profiling studies have shown that residues R568, F592, R660, Y661, and Y665 within exosite-3 of the spacer domain provide an immunodominant region of ADAMTS13 for pathogenic autoantibodies that develop in patients with iTTP. Modification of these 5 core residues with the goal of reducing autoantibody binding revealed a significant tradeoff between autoantibody resistance and proteolytic activity. Here, we employed structural bioinformatics to identify a larger epitope landscape on the ADAMTS13 spacer domain. Models of spacer-antibody complexes predicted that residues R568, L591, F592, K608, M609, R636, L637, R639, R660, Y661, Y665, and L668 contribute to an expanded epitope within the spacer domain. Based on bioinformatics-guided predictions, we designed a panel of N-glycan insertions in this expanded epitope to reduce the binding of spacer domain autoantibodies. One N-glycan variant (NGLY3-ADAMTS13, containing a K608N substitution) showed strongly reduced reactivity with TTP patient sera (28%) as compared with WT-ADAMTS13 (100%). Insertion of an N-glycan at amino acid position 608 did not interfere with processing of von Willebrand factor, positioning the resulting NGLY3-ADAMTS13 variant as a potential novel therapeutic option for treatment of iTTP.

Structural characterization of a novel GPVI-nanobody complex reveals a biologically active domain-swapped GPVI dimer.

Glycoprotein VI (GPVI) is the major signaling receptor for collagen on platelets. We have raised 54 nanobodies (Nb), grouped into 33 structural classes based on their complementary determining region 3 loops, against recombinant GPVI-Fc (dimeric GPVI) and have characterized their ability to bind recombinant GPVI, resting and activated platelets, and to inhibit platelet activation by collagen. Nbs from 6 different binding classes showed the strongest binding to recombinant GPVI-Fc, suggesting that there was not a single dominant class. The most potent 3, Nb2, 21, and 35, inhibited collagen-induced platelet aggregation with nanomolar half maximal inhibitory concentration (IC50) values and inhibited platelet aggregation under flow. The binding KD of the most potent Nb, Nb2, against recombinant monomeric and dimeric GPVI was 0.6 and 0.7 nM, respectively. The crystal structure of monomeric GPVI in complex with Nb2 revealed a binding epitope adjacent to the collagen-related peptide (CRP) binding groove within the D1 domain. In addition, a novel conformation of GPVI involving a domain swap between the D2 domains was observed. The domain swap is facilitated by the outward extension of the C-C' loop, which forms the domain swap hinge. The functional significance of this conformation was tested by truncating the hinge region so that the domain swap cannot occur. Nb2 was still able to displace collagen and CRP binding to the mutant, but signaling was abolished in a cell-based NFAT reporter assay. This demonstrates that the C-C' loop region is important for GPVI signaling but not ligand binding and suggests the domain-swapped structure may represent an active GPVI conformation.

Structure of blood coagulation factor VIII in complex with an anti-C1 domain pathogenic antibody inhibitor.

Antibody inhibitor development in hemophilia A represents the most significant complication resulting from factor VIII (fVIII) replacement therapy. Recent studies have demonstrated that epitopes present in the C1 domain contribute to a pathogenic inhibitor response. In this study, we report the structure of a group A anti-C1 domain inhibitor, termed 2A9, in complex with a B domain-deleted, bioengineered fVIII construct (ET3i). The 2A9 epitope forms direct contacts to the C1 domain at 3 different surface loops consisting of Lys2065-Trp2070, Arg2150-Tyr2156, and Lys2110-Trp2112. Additional contacts are observed between 2A9 and the A3 domain, including the Phe1743-Tyr1748 loop and the N-linked glycosylation at Asn1810. Most of the C1 domain loops in the 2A9 epitope also represent a putative interface between fVIII and von Willebrand factor. Lastly, the C2 domain in the ET3i:2A9 complex adopts a large, novel conformational change, translocating outward from the structure of fVIII by 20 Å. This study reports the first structure of an anti-C1 domain antibody inhibitor and the first fVIII:inhibitor complex with a therapeutically active fVIII construct. Further structural understanding of fVIII immunogenicity may result in the development of more effective and safe fVIII replacement therapies.

Fever as an evolutionary agent to select immune complexes interfaces.

We herein analyzed all available protein-protein interfaces of the immune complexes from the Protein Data Bank whose antigens belong to pathogens or cancers that are modulated by fever in mammalian hosts. We also included, for comparison, protein interfaces from immune complexes that are not significantly modulated by the fever response. We highlight the distribution of amino acids at these viral, bacterial, protozoan and cancer epitopes, and at their corresponding paratopes that belong strictly to monoclonal antibodies. We identify the "hotspots", i.e. residues that are highly connected at such interfaces, and assess the structural, kinetic and thermodynamic parameters responsible for complex formation. We argue for an evolutionary pressure for the types of residues at these protein interfaces that may explain the role of fever as a selective force for optimizing antibody binding to antigens.

A computational study of co-inhibitory immune complex assembly at the interface between T cells and antigen presenting cells.

The activation and differentiation of T-cells are mainly directly by their co-regulatory receptors. T lymphocyte-associated protein-4 (CTLA-4) and programed cell death-1 (PD-1) are two of the most important co-regulatory receptors. Binding of PD-1 and CTLA-4 with their corresponding ligands programed cell death-ligand 1 (PD-L1) and B7 on the antigen presenting cells (APC) activates two central co-inhibitory signaling pathways to suppress T cell functions. Interestingly, recent experiments have identified a new cis-interaction between PD-L1 and B7, suggesting that a crosstalk exists between two co-inhibitory receptors and the two pairs of ligand-receptor complexes can undergo dynamic oligomerization. Inspired by these experimental evidences, we developed a coarse-grained model to characterize the assembling of an immune complex consisting of CLTA-4, B7, PD-L1 and PD-1. These four proteins and their interactions form a small network motif. The temporal dynamics and spatial pattern formation of this network was simulated by a diffusion-reaction algorithm. Our simulation method incorporates the membrane confinement of cell surface proteins and geometric arrangement of different binding interfaces between these proteins. A wide range of binding constants was tested for the interactions involved in the network. Interestingly, we show that the CTLA-4/B7 ligand-receptor complexes can first form linear oligomers, while these oligomers further align together into two-dimensional clusters. Similar phenomenon has also been observed in other systems of cell surface proteins. Our test results further indicate that both co-inhibitory signaling pathways activated by B7 and PD-L1 can be down-regulated by the new cis-interaction between these two ligands, consistent with previous experimental evidences. Finally, the simulations also suggest that the dynamic and the spatial properties of the immune complex assembly are highly determined by the energetics of molecular interactions in the network. Our study, therefore, brings new insights to the co-regulatory mechanisms of T cell activation.