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The ability to accurately predict antibody-antigen complex structures from their sequences could greatly advance our understanding of the immune system and would aid in the development of novel antibody therapeutics. There have been considerable recent advancements in predicting protein-protein interactions (PPIs) fueled by progress in machine learning (ML). To understand the current state of the field, we compare six representative methods for predicting antibody-antigen complexes from sequence, including two deep learning approaches trained to predict PPIs in general (AlphaFold-Multimer and RoseTTAFold), two composite methods that initially predict antibody and antigen structures separately and dock them (using antibody-mode ClusPro), local refinement in Rosetta (SnugDock) of globally docked poses from ClusPro, and a pipeline combining homology modeling with rigid-body docking informed by ML-based epitope and paratope prediction (AbAdapt). We find that AlphaFold-Multimer outperformed other methods, although the absolute performance leaves considerable room for improvement. AlphaFold-Multimer models of lower quality display significant structural biases at the level of tertiary motifs (TERMs) toward having fewer structural matches in non-antibody-containing structures from the Protein Data Bank (PDB). Specifically, better models exhibit more common PDB-like TERMs at the antibody-antigen interface than worse ones. Importantly, the clear relationship between performance and the commonness of interfacial TERMs suggests that the scarcity of interfacial geometry data in the structural database may currently limit the application of ML to the prediction of antibody-antigen interactions., (© 2024 The Protein Society.)

Computational analysis of paratope-epitope interactions between antibodies and their corresponding antigens can facilitate our understanding of the molecular mechanism underlying humoral immunity and boost the design of new therapeutics for many diseases. The recent breakthrough in artificial intelligence has made it possible to predict protein-protein interactions and model their structures. Unfortunately, detecting antigen-binding sites associated with a specific antibody is still a challenging problem. To tackle this challenge, we implemented a deep learning model to characterize interaction patterns between antibodies and their corresponding antigens. With high accuracy, our model can distinguish between antibody-antigen complexes and other types of protein-protein complexes. More intriguingly, we can identify antigens from other common protein binding regions with an accuracy of higher than 70% even if we only have the epitope information. This indicates that antigens have distinct features on their surface that antibodies can recognize. Additionally, our model was unable to predict the partnerships between antibodies and their particular antigens. This result suggests that one antigen may be targeted by more than one antibody and that antibodies may bind to previously unidentified proteins. Taken together, our results support the precision of antibody-antigen interactions while also suggesting positive future progress in the prediction of specific pairing., Competing Interests: Declaration of competing interest The authors declare no conflict of interests., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Large language models trained on sequence information alone can learn high-level principles of protein design. However, beyond sequence, the three-dimensional structures of proteins determine their specific function, activity, and evolvability. Here, we show that a general protein language model augmented with protein structure backbone coordinates can guide evolution for diverse proteins without the need to model individual functional tasks. We also demonstrate that ESM-IF1, which was only trained on single-chain structures, can be extended to engineer protein complexes. Using this approach, we screened about 30 variants of two therapeutic clinical antibodies used to treat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We achieved up to 25-fold improvement in neutralization and 37-fold improvement in affinity against antibody-escaped viral variants of concern BQ.1.1 and XBB.1.5, respectively. These findings highlight the advantage of integrating structural information to identify efficient protein evolution trajectories without requiring any task-specific training data.

Single-chain variable fragment (scFv) antibodies hold great potential as diagnostic tools and therapeutic agents, especially for tumor cells. Since these applications require their production with improved properties, the design strategy of scFvs is crucial for their active, soluble, and high yield expression with high affinity towards their antigens. The order of V L and V H domains is one of the important parameters that affect the expression and binding affinity properties of scFvs. In addition, the optimum order of V L and V H domains could change for each scFv. In the present study, we used computer simulation tools to evaluate the effect of variable domain orientation on structure, stability, interacting residues of scFvs, and binding free energies of scFv-antigen complexes. We selected anti-HER2 scFv, which is specific for human epidermal growth receptor 2 (HER2) overexpressed in breast cancer, and anti-IL-1β scFv against IL-1β which is an important inflammatory biomarker, as model scFvs. Molecular dynamics simulations of the scFv-antigen complexes for 100 ns resulted in stability and compactness for both scFv constructs. Interaction and binding free energies calculated by the Molecular Mechanics-Poisson-Boltzmann Surface Area (MM-PBSA) approach suggested that the relative binding energies of anti-HER2 scFv-V L V H and anti-HER2 scFv-V H V L constructs had similar binding affinity towards HER2, while a relatively more negative binding free energy obtained between anti-IL-1β scFv-V H V L and IL-1β pointed to a higher binding affinity. The in silico approach and the results obtained here could be applied as a guide for future experimental interaction studies for highly specific scFvs used as biotechnological tools.Communicated by Ramaswamy H. Sarma.

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

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., (© The Author(s) 2024. Published by Oxford University Press.)

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.

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.

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.

Immobilization technology plays an important role in enhancing enzyme stability and environmental adaptability. Despite its rapid development, this technology still encounters many challenges such as enzyme leakage, difficulties in large-scale implementation, and limited reusability. Drawing inspiration from natural paired molecules, this study aimed to establish a method for immobilized α-glucosidase using artificial antibody-antigen interaction. The proposed method consists of three main parts: synthesis of artificial antibodies, synthesis of artificial antigens, and assembly of the artificial antibody-antigen complex. The critical step in this method involves selecting a pair of structurally similar compounds: catechol as a template for preparing artificial antibodies and protocatechualdehyde for modifying the enzyme to create the artificial antigens. By utilizing the same functional groups in these compounds, specific recognition of the antigen by the artificial antibody can be achieved, thereby immobilizing the enzymes. The results demonstrated that the immobilization amount, specific activity, and enzyme activity of the immobilized α-glucosidase were 25.09 ± 0.10 mg/g, 5.71 ± 0.17 U/mg protein and 143.25 ± 1.71 U/g carrier , respectively. The immobilized α-glucosidase not only exhibited excellent reusability but also demonstrated remarkable performance in catalyzing the hydrolysis of 4-methylumbelliferyl-α-D-glucopyranoside., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Progression of various cancers and autoimmune diseases is associated with changes in systemic or local tissue temperatures, which may impact current therapies. The role of fever and acute inflammation-range temperatures on the stability and activity of antibodies relevant for cancers and autoimmunity is unknown. To produce molecular dynamics (MD) trajectories of immune complexes at relevant temperatures, we used the Research Collaboratory for Structural Bioinformatics (RCSB) database to identify 50 antibody:antigen complexes of interest, in addition to single antibodies and antigens, and deployed Groningen Machine for Chemical Simulations (GROMACS) to prepare and run the structures at different temperatures for 100-500 ns, in single or multiple random seeds. MD trajectories are freely available. Processed data include Protein Data Bank outputs for all files obtained every 50 ns, and free binding energy calculations for some of the immune complexes. Protocols for using the data are also available. Individual datasets contain unique DOIs. We created a web interface, ThermoPCD, as a platform to explore the data. The outputs of ThermoPCD allow the users to relate thermally-dependent changes in epitopes:paratopes interfaces to their free binding energies, or against own experimentally derived binding affinities. ThermoPCD is a free to use database of immune complexes' trajectories at different temperatures that does not require registration and allows for all the data to be available for download. Database URL: https://sites.google.com/view/thermopcd/home., (© The Author(s) 2024. Published by Oxford University Press.)

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., (© The Author(s) 2024. Published by Oxford University Press.)

Antibodies are a class of proteins that recognize and neutralize pathogens by binding to their antigens. They are the most significant category of biopharmaceuticals for both diagnostic and therapeutic applications. Understanding how antibodies interact with their antigens plays a fundamental role in drug and vaccine design and helps to comprise the complex antigen binding mechanisms. Computational methods for predicting interaction sites of antibody-antigen are of great value due to the overall cost of experimental methods. Machine learning methods and deep learning techniques obtained promising results.In this work, we predict antibody interaction interface sites by applying HSS-PPI, a hybrid method defined to predict the interface sites of general proteins. The approach abstracts the proteins in terms of hierarchical representation and uses a graph convolutional network to classify the amino acids between interface and non-interface. Moreover, we also equipped the amino acids with different sets of physicochemical features together with structural ones to describe the residues. Analyzing the results, we observe that the structural features play a fundamental role in the amino acid descriptions. We compare the obtained performances, evaluated using standard metrics, with the ones obtained with SVM with 3D Zernike descriptors, Parapred, Paratome, and Antibody i-Patch., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Antibodies have increasingly been developed as drugs with over 100 now licensed in the US or EU. During development, it is often necessary to increase or reduce the affinity of an antibody and rational attempts to do so rely on having a structure of the antibody-antigen complex often obtained by modeling. The antigen-binding site consists primarily of six loops known as complementarity-determining regions (CDRs), and an open question has been whether these loops change their conformation when they bind to an antigen. Existing surveys of antibody-antigen complex structures have only examined CDR conformational change in case studies or small-scale surveys. With an increasing number of antibodies where both free and complexed structures have been deposited in the Protein Data Bank, a large-scale survey of CDR conformational change during binding is now possible. To this end, we built a dataset, AbAgDb, that currently includes 177 antibodies with high-quality CDRs, each of which has at least one bound and one unbound structure. We analyzed the conformational change of the C α backbone of each CDR upon binding and found that, in most cases, the CDRs (other than CDR-H3) show minimal movement, while 70.6% and 87% of CDR-H3s showed global C α RMSD ≤ 1.0Å and ≤ 2.0Å, respectively. We also compared bound CDR conformations with the conformational space of unbound CDRs and found most of the bound conformations are included in the unbound conformational space. In future, our results will contribute to developing insights into antibodies and new methods for modeling and docking.

The atomic-resolution structural information that X-ray crystallography can provide on the binding interface between a Fab and its cognate antigen is highly valuable for understanding the mechanism of interaction. However, many Fab:antigen complexes are recalcitrant to crystallization, making the endeavor a considerable effort with no guarantee of success. Consequently, there have been significant steps taken to increase the likelihood of Fab:antigen complex crystallization by altering the Fab framework. In this investigation, we applied the surface entropy reduction strategy coupled with phage-display technology to identify a set of surface substitutions that improve the propensity of a human Fab framework to crystallize. In addition, we showed that combining these surface substitutions with previously reported Crystal Kappa and elbow substitutions results in an extraordinary improvement in Fab and Fab:antigen complex crystallizability, revealing a strong synergistic relationship between these sets of substitutions. Through comprehensive Fab and Fab:antigen complex crystallization screenings followed by structure determination and analysis, we defined the roles that each of these substitutions play in facilitating crystallization and how they complement each other in the process., (© 2023 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.)

Antibodies are used for many therapeutic and biotechnological purposes. Because the affinity of an antibody to the antigen is critical for clinical efficacy of pharmaceuticals, many affinity maturation strategies have been developed. Although we previously reported an affinity maturation strategy in which the association rate of the antibody toward its antigen is improved by introducing a cluster of arginine residues into the framework region of the antibody, the detailed molecular mechanism responsible for this improvement has been unknown. In this study, we introduced five arginine residues into an anti-hen egg white lysozyme antibody (HyHEL10) Fab fragment to create the R5-mutant and comprehensively characterized the interaction between antibody and antigen using thermodynamic analysis, X-ray crystallography, and molecular dynamics (MD) simulations. Our results indicate that introduction of charged residues strongly enhanced the association rate, as previously reported, and the antibody-antigen complex structure was almost the same for the R5-mutant and wild-type Fabs. The MD simulations indicate that the mutation increased conformational diversity in complementarity-determining region loops and thereby enhanced the association rate. These observations provide the molecular basis of affinity maturation by R5 mutation., (© 2023 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.)

The conserved coronavirus fusion peptide is a target of broadly neutralizing antibodies.

In the recent years, therapeutic use of antibodies has seen a huge growth, "due to their inherent proprieties and technological advances in the methods used to study and characterize them. Effective design and engineering of antibodies for therapeutic purposes are heavily dependent on knowledge of the structural principles that regulate antibody-antigen interactions. Several experimental techniques such as X-ray crystallography, cryo-electron microscopy, NMR, or mutagenesis analysis can be applied, but these are usually expensive and time-consuming. Therefore computational approaches like molecular docking may offer a valuable alternative for the characterization of antibody-antigen complexes.Here we describe a protocol for the prediction of the 3D structure of antibody-antigen complexes using the integrative modelling platform HADDOCK. The protocol consists of (1) the identification of the antibody residues belonging to the hypervariable loops which are known to be crucial for the binding and can be used to guide the docking and (2) the detailed steps to perform docking with the HADDOCK 2.4 webserver following different strategies depending on the availability of information about epitope residues., (© 2023. Springer Science+Business Media, LLC, part of Springer Nature.)

Specific antibodies can bind to protein antigens with high affinity and specificity, and this property makes them one of the best protein-based therapeutics. Accurate prediction of antibody‒protein antigen binding affinity is crucial for designing effective antibodies. The current predictive methods for protein‒protein binding affinity usually fail to predict the binding affinity of an antibody‒protein antigen complex with a comparable level of accuracy. Here, new models specific for antibody‒antigen binding affinity prediction are developed according to the different types of interface and surface areas present in antibody‒antigen complex. The contacts-based descriptors are also employed to construct or train different models specific for antibody‒protein antigen binding affinity prediction. The results of this study show that (i) the area-based descriptors are slightly better than the contacts-based descriptors in terms of the predictive power; (ii) the new models specific for antibody‒protein antigen binding affinity prediction are superior to the previously-used general models for predicting the protein‒protein binding affinities; (iii) the performances of the best area-based and contacts-based models developed in this work are better than the performances of a recently-developed graph-based model (i.e., CSM-AB) specific for antibody‒protein antigen binding affinity prediction. The new models developed in this work would not only help understand the mechanisms underlying antibody‒protein antigen interactions, but would also be of some applicable utility in the design and virtual screening of antibody-based therapeutics., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

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., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Humans lack the capacity to produce the Galα1-3Galβ1-4GlcNAc (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.

Antibody recognition of antigens is a critical element of adaptive immunity. One key class of antibody-antigen complexes is comprised of antibodies targeting linear epitopes of proteins, which in some cases are conserved elements of viruses and pathogens of relevance for vaccine design and immunotherapy. Here we report a detailed analysis of the structural and interface features of this class of complexes, based on a set of nearly 200 nonredundant high resolution antibody-peptide complex structures that were assembled from the Protein Data Bank. We found that antibody-bound peptides adopt a broad range of conformations, often displaying limited secondary structure, and that the same peptide sequence bound by different antibodies can in many cases exhibit varying conformations. Propensities of contacts with antibody loops and extent of antibody binding conformational changes were found to be broadly similar to those for antibodies in complex with larger protein antigens. However, antibody-peptide interfaces showed lower buried surface areas and fewer hydrogen bonds than antibody-protein antigen complexes, while calculated binding energy per buried interface area was found to be higher on average for antibody-peptide interfaces, likely due in part to a greater proportion of buried hydrophobic residues and higher shape complementarity. This dataset and these observations can be of use for future studies focused on this class of interactions, including predictive computational modeling efforts and the design of antibodies or epitope-based vaccine immunogens., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Lee, Yin, Ofek and Pierce.)

Previous reports present different models for the stabilization of the Fc-FcγRI immune complex. Although accord exists on the importance of L235 in IgG1 and some hydrophobic contacts for complex stabilization, discord exists regarding the existence of stabilizing glycoprotein contacts between glycans of IgG1 and a conserved FG-loop ( 171 MGKHRY 176 ) of FcγRIa. Complexes formed from the FcγRIa receptor and IgG1s containing biantennary glycans with N-acetylglucosamine, galactose, and α2,6-N-acetylneuraminic terminations were measured by hydrogen-deuterium exchange mass spectrometry (HDX-MS), classified for dissimilarity with Welch's ANOVA and Games-Howell post hoc procedures, and modeled with molecular dynamics (MD) simulations. For each glycoform of the IgG1-FcγRIa complex peptic peptides of Fab, Fc and FcγRIa report distinct H/D exchange rates. MD simulations corroborate the differences in the peptide deuterium content through calculation of the percent of time that transient glycan-peptide bonds exist. These results indicate that stability of IgG1-FcγRIa complexes correlate with the presence of intermolecular glycoprotein interactions between the IgG1 glycans and the 173 KHR 175 motif within the FG-loop of FcγRIa. The results also indicate that intramolecular glycan-protein bonds stabilize the Fc region in isolated and complexed IgG1. Moreover, HDX-MS data evince that the Fab domain has glycan-protein binding contacts within the IgG1-FcγRI complex., Competing Interests: Declaration of interests The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Ltd.)

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.

Identifying the epitope of an antibody is a key step in understanding its function and its potential as a therapeutic. Sequence-based clonal clustering can identify antibodies with similar epitope complementarity, however, antibodies from markedly different lineages but with similar structures can engage the same epitope. We describe a novel computational method for epitope profiling based on structural modelling and clustering. Using the method, we demonstrate that sequence dissimilar but functionally similar antibodies can be found across the Coronavirus Antibody Database, with high accuracy (92% of antibodies in multiple-occupancy structural clusters bind to consistent domains). Our approach functionally links antibodies with distinct genetic lineages, species origins, and coronavirus specificities. This indicates greater convergence exists in the immune responses to coronaviruses than is suggested by sequence-based approaches. Our results show that applying structural analytics to large class-specific antibody databases will enable high confidence structure-function relationships to be drawn, yielding new opportunities to identify functional convergence hitherto missed by sequence-only analysis., Competing Interests: The authors have declared that no competing interests exist.

We used human monoclonal antibodies (humAbs) to study the mechanism of neuron intoxication by tetanus neurotoxin and to evaluate these antibodies as a safe preventive and therapeutic substitute for hyperimmune sera to treat tetanus in mice. By screening memory B cells from immune donors, we selected 2 tetanus neurotoxin-specific mAbs with exceptionally high neutralizing activities and extensively characterized them both structurally and functionally. We found that these antibodies interfered with the binding and translocation of the neurotoxin into neurons by interacting with 2 epitopes, whose identification pinpoints crucial events in the cellular pathogenesis of tetanus. Our observations explain the neutralization ability of these antibodies, which we found to be exceptionally potent in preventing experimental tetanus when injected into mice long before the toxin. Moreover, their Fab derivatives neutralized tetanus neurotoxin in post-exposure experiments, suggesting their potential for therapeutic use via intrathecal injection. As such, we believe these humAbs, as well as their Fab derivatives, meet the requirements to be considered for prophylactic and therapeutic use in human tetanus and are ready for clinical trials.

Antibodies to DNA (anti-DNA) are the serological hallmark of systemic lupus erythematosus. Previous studies have indicated that the phosphodiester backbone is the main antigenic target, with electrostatic interactions important for high avidity. To define further these interactions, the effects of ionic strength on anti-DNA binding of SLE plasmas were assessed in association and dissociation assays by ELISA. As these studies demonstrated, increasing ionic strength to a concentration of 1000 mM NaCl reduced antibody binding although the extent of the reduction varied among samples. In dissociation assays, differences among plasmas were also observed. For one of the plasmas, binding to DNA displayed resistance to dissociation by increasing ionic strength even though these concentrations limited binding in association assays. Time course studies showed a gradual change in binding interactions. These studies indicate that anti-DNA binding can involve both electrostatic and non-electrostatic interactions, with binding in some plasmas showing evidence of hysteresis., (Copyright © 2021 Elsevier Inc. All rights reserved.)

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., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

VHH stands for the variable regions of heavy chain only of camelid IgGs. The VHH family forms a set of interesting proteins derived from antibodies that maintain their capacity to recognize the antigen, despite their relatively small molecular weight (in the 12,000 Da range). Continuing our exploration of the possibilities of those molecules, we chose to design alternative molecules with maintained antigen recognition, but enhanced capacity, by fusing four VHH with one Fc, the fragment crystallizable region of antibodies. In doing so, we aimed at having a molecule with superior quantitative antigen recognition (×4) while maintaining its size below the 110 kDa. In the present paper, we described the building of those molecules that we coined VHH 2 -Fc-VHH 2 . The structure of VHH 2 -Fc-VHH 2 in complex with HER2 antigen was determined using electronic microscopy and modeling. The molecule is shown to bind four HER2 proteins at the end of its flexible arms. VHH 2 -Fc-VHH 2 also shows an internalization capacity via HER2 receptor superior to the reference anti-HER2 monoclonal antibody, Herceptin®, and to a simple fusion of two VHH with one Fc (VHH 2 -Fc). This new type of molecules, VHH 2 -Fc-VHH 2 , could be an interesting addition to the therapeutic arsenal with multiple applications, from diagnostic to therapy., (© 2021 The Protein Society.)

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., Competing Interests: Declaration of interests G.R.S. is on the GSK Vaccines Scientific Advisory Board. Oxford University holds intellectual property related to the Oxford-AstraZeneca vaccine. A.J.P. is Chair of UK Department Health and Social Care’s (DHSC) Joint Committee on Vaccination & Immunisation (JCVI) but does not participate in the JCVI COVID19 committee and is a member of the WHO’s SAGE. The views expressed in this article do not necessarily represent the views of DHSC, JCVI, or WHO. The University of Oxford has entered into a partnership with AstraZeneca on coronavirus vaccine development. The University of Oxford has protected intellectual property disclosed in this publication. S.C.G. is co-founder of Vaccitech (collaborators in the early development of this vaccine candidate) and is named as an inventor on a patent covering use of ChAdOx1-vectored vaccines and a patent application covering this SARS-CoV-2 vaccine (PCT/GB2012/000467). T.L. is named as an inventor on a patent application covering this SARS-CoV-2 vaccine and was a consultant to Vaccitech for an unrelated project during the conduct of the study., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Use of nicotine-specific monoclonal antibodies (mAbs) to sequester and reduce nicotine distribution to brain has been proposed as a therapeutic approach to treat nicotine addiction (the basis of tobacco use disorder). A series of monoclonal antibodies with high affinity for nicotine (nic•mAbs) was isolated from B-cells of vaccinated smokers. Genes encoding 32 unique nicotine binding antibodies were cloned, and the mAbs expressed and tested by surface plasmon resonance to determine their affinity for S-(-)-nicotine. The highest affinity nic•mAbs had binding affinity constants (KD) between 5 and 67 nM. The 4 highest affinity nic•mAbs were selected to undergo additional secondary screening for antigen-specificity, protein properties (including aggregation and stability), and functional in vivo studies to evaluate their capacity for reducing nicotine distribution to brain in rats. The 2 most potent nic•mAbs in single-dose nicotine pharmacokinetic experiments were further tested in a dose-response in vivo study. The most potent lead, ATI-1013, was selected as the lead candidate based on the results of these studies. Pretreatment with 40 and 80 mg/kg ATI-1013 reduced brain nicotine levels by 56 and 95%, respectively, in a repeated nicotine dosing experiment simulating very heavy smoking. Nicotine self-administration was also significantly reduced in rats treated with ATI-1013. A pilot rat 30-day repeat-dose toxicology study (4x200mg/kg ATI-1013) in the presence of nicotine indicated no drug-related safety concerns. These data provide evidence that ATI-1013 could be a potential therapy for the treatment of nicotine addiction., Competing Interests: At the time the research was conducted: i) TT, ZB, and MK are/were employees and shareholders of Antidote Therapeutics, Inc. (ATI), ii) S. Fuller was a consultant to ATI and shareholder, iii) NB and S. Fallot were employees and shareholders of Blink Biomedical, SAS, iv) DS was an employee of BioFactura, Inc. and ATI shareholder, v) SH was an employee of Noble Life Sciences, Inc. MK, TT, NB, S. Fallot, ZB, S. Fuller, MLS, and PP are coinventors on the following patent application: “Novel Nicotine-binding Antibodies” US201816639050 20180814. MR, NB, S. Fallot, MLS, AS, PP, S. Fuller, SH, DS, and BZ and their respective institutions received research funding from Antidote for salaries, experiments, and manufacturing as subawardees1,5,6 or subcontractors3,4 to Antidote under R01 DA038877 from the National Institute on Drug Abuse (NIDA) at the National Institutes of Health.

The innate immune system's natural killer (NK) cells exert their cytolytic function against a variety of pathological challenges, including tumors and virally infected cells. Their activation depends on net signaling mediated via inhibitory and activating receptors that interact with specific ligands displayed on the surfaces of target cells. The CD94/NKG2C heterodimer is one of the NK activating receptors and performs its function by interacting with the trimeric ligand comprised of the HLA-E/β2m/nonameric peptide complex. Here, simulations of the all-atom multi-microsecond molecular dynamics in five immune complexes provide atomistic insights into the receptor-ligand molecular recognition, as well as the molecular events that facilitate the NK cell activation. We identify NKG2C, the HLA-E α2 domain, and the nonameric peptide as the key elements involved in the molecular machinery of signal transduction via an intertwined hydrogen bond network. Overall, the study addresses the complex intricacies that are necessary to understand the mechanisms of the innate immune system.

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., (© 2021 by The American Society of Hematology.)

Antibodies are secreted proteins that are crucial to recognition of pathogens by the immune system and are also efficient pharmaceuticals. The affinity and specificity of target recognition can increase remarkably through avidity effects, when the antibody can bind a multivalent antigen through more than one epitope simultaneously. A key goal of antibody engineering is thus to optimize avidity, but little is known about the nanoscale spatial dependence of avidity in antibodies. Here, we develop a set of anti-parallel coiled-coils spanning from 7 to 20 nm and validate their structure using biophysical techniques. We use the coiled-coils to control the spacing between two epitopes, and measure how antigen spacing affects the stability of the bivalent antibody:antigen complex. We find a maximal avidity enhancement at a spacing of 13 nm. In contrast to recent studies, we find the avidity to be relatively insensitive to epitope spacing near the avidity maximum as long as it is within the spatial tolerance of the antibody. We thus only see a ~ twofold variation of avidity in the range from 7 to 20 nm. The coiled-coil systems developed here may prove a useful protein nanocaliper for profiling the spatial tolerance and avidity profile of bispecific antibodies.

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., Competing Interests: Declaration of interests A provisional patent for the novel SARS-CoV-2 mAbs described in this study has been filed. The authors listed on that application are R.J.X., J.F.S., C.O.B., P.J.B., and B.E. The Broad Institute has filed multiple patents in the area of single cell RNA-seq on which A.R. and O.R.-R. are named inventors. Rockefeller University has applied for a patent relating to the replication competent VSV/SARS-CoV-2 chimeric virus on which Y.W., T.H., and P.D.B. are listed as inventors (US patent 63/036,124). R.J.X. is co-founder and equity holder of Jnana Therapeutics. R.J.X. and A.R. are co-founders and equity holders of Celsius Therapeutics. A.R. is an equity holder in Immunitas and was an SAB member of ThermoFisher Scientific, Syros Pharmaceuticals, Neogene Therapeutics, and Asimov. A.R. and O.R.-R. are employees of Genentech (member of the Roche Group) since August and October 2020, respectively. These companies did not provide support for this work., (Copyright © 2021 Elsevier Inc. All rights reserved.)

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., Competing Interests: Declaration of interests B.W., W.-H.L., C.-W.H., Y.H., P.M.L.N., E.Z.X.N., H.C.T., C.Y.L., R.A.M., M.K.S., F.J.T., Y.Y.C.Y., and C.-I.W. are listed as inventors of a filed patent for all 27 monoclonal antibodies mentioned in this manuscript., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Accurate predictive modeling of antibody-antigen complex structures and structure-based antibody design remain major challenges in computational biology, with implications for biotherapeutics, immunity, and vaccines. Through a systematic search for high-resolution structures of antibody-antigen complexes and unbound antibody and antigen structures, in conjunction with identification of experimentally determined binding affinities, we have assembled a non-redundant set of test cases for antibody-antigen docking and affinity prediction. This benchmark more than doubles the number of antibody-antigen complexes and corresponding affinities available in our previous benchmarks, providing an unprecedented view of the determinants of antibody recognition and insights into molecular flexibility. Initial assessments of docking and affinity prediction tools highlight the challenges posed by this diverse set of cases, which includes camelid nanobodies, therapeutic monoclonal antibodies, and broadly neutralizing antibodies targeting viral glycoproteins. This dataset will enable development of advanced predictive modeling and design methods for this therapeutically relevant class of protein-protein interactions., Competing Interests: Declaration of interests I.M. is employed by GlaxoSmithKline plc, which discovers and sells antibody therapies. Z.W. is a cofounder of Rgenta Therapeutics and serves on its scientific advisory board. J.J.G. is an unpaid board member of the Rosetta Commons. Under institutional participation agreements between the University of Washington, acting on behalf of the Rosetta Commons, Johns Hopkins University may be entitled to a portion of revenue received on licensing Rosetta software, including some methods described in this article. As a member of the Scientific Advisory Board, J.J.G. has a financial interest in Cyrus Biotechnology. Cyrus Biotechnology distributes the Rosetta software, which includes methods described in this article. These arrangements have been reviewed and approved by the Johns Hopkins University in accordance with its conflict of interest policies., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

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., (© 2021 by The American Society of Hematology.)

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., (© 2021 by The American Society of Hematology.)

Although human antibodies elicited by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid (N) protein are profoundly boosted upon infection, little is known about the function of N-reactive antibodies. Herein, we isolate and profile a panel of 32 N protein-specific monoclonal antibodies (mAbs) from a quick recovery coronavirus disease-19 (COVID-19) convalescent patient who has dominant antibody responses to the SARS-CoV-2 N protein rather than to the SARS-CoV-2 spike (S) protein. The complex structure of the N protein RNA binding domain with the highest binding affinity mAb (nCoV396) reveals changes in the epitopes and antigen's allosteric regulation. Functionally, a virus-free complement hyperactivation analysis demonstrates that nCoV396 specifically compromises the N protein-induced complement hyperactivation, which is a risk factor for the morbidity and mortality of COVID-19 patients, thus laying the foundation for the identification of functional anti-N protein mAbs.

The lack of progress in finding an efficient vaccine for a human immunodeficiency virus (HIV) is daunting. In fact, this search has spanned nearly four decades without much success. There are several objective reasons for such a failure, which include the highly glycosylated nature of HIV-1, the presence of neotopes, and high mutation rates. This article argues that the presence of highly flexible and intrinsically disordered regions in both human anti-HIV-1 antibodies and the major HIV-1immunogen, its surface glycoprotein gp120, represent one of the major causes for the lack of success in utilization of structure-based reverse vaccinology.

Hapten-specific endogenous antibodies are naturally occurring antibodies present in human blood. Herein, we investigated a new strategy in which small-molecule haptens were utilized as naturally occurring antibody binders for peptide half-life extension. The glucagon-like peptide 1 receptor agonist exendin 4 was site-specifically functionalized with the dinitrophenyl (DNP) hapten at the C-terminus via sortase A-mediated ligation. The resulting Ex4-DNP conjugates retained GLP-1 receptor activation potency in vitro and had a similar in vivo acute glucose-lowering effect comparable to that of native Ex4. Pharmacokinetic studies and hypoglycemic duration tests demonstrated that the Ex4-DNP conjugates displayed significantly elongated half-lives and improved long-acting antidiabetic activity in the presence of endogenous anti-DNP antibodies. In chronic treatment studies, once-daily administration of optimal conjugate 7 demonstrated more beneficial effects without prominent toxicity compared with Ex4. This strategy provides a new approach and represents an alternative to the well-established peptide-Fc fusion strategy to improve the peptide half-life and the therapeutic efficacy.

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., Competing Interests: The authors have declared that no competing interests exist.

Seasonal influenza vaccines confer protection against specific viral strains but have restricted breadth that limits their protective efficacy. The H1 and H3 subtypes of influenza A virus cause most of the seasonal epidemics observed in humans and are the major drivers of influenza A virus-associated mortality. The consequences of pandemic spread of COVID-19 underscore the public health importance of prospective vaccine development. Here, we show that headless hemagglutinin (HA) stabilized-stem immunogens presented on ferritin nanoparticles elicit broadly neutralizing antibody (bnAb) responses to diverse H1 and H3 viruses in nonhuman primates (NHPs) when delivered with a squalene-based oil-in-water emulsion adjuvant, AF03. The neutralization potency and breadth of antibodies isolated from NHPs were comparable to human bnAbs and extended to mismatched heterosubtypic influenza viruses. Although NHPs lack the immunoglobulin germline VH1-69 residues associated with the most prevalent human stem-directed bnAbs, other gene families compensated to generate bnAbs. Isolation and structural analyses of vaccine-induced bnAbs revealed extensive interaction with the fusion peptide on the HA stem, which is essential for viral entry. Antibodies elicited by these headless HA stabilized-stem vaccines neutralized diverse H1 and H3 influenza viruses and shared a mode of recognition analogous to human bnAbs, suggesting that these vaccines have the potential to confer broadly protective immunity against diverse viruses responsible for seasonal and pandemic influenza infections in humans., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

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.

A novel approach to recover and identify immune complexes (ICs) was developed using size exclusion chromatography (SEC) and affinity chromatography on immunoglobulin binding columns (HiTrap Protein G). The purification process was monitored by 1D SDS-PAGE, protein staining, Western blotting and, finally, liquid chromatography tandem mass spectrometry (LC MS/MS) was used to identify the recovered antigens. This approach was applied to serum with artificially created immune complexes (ICs) comprising vaccine antigen (influenza) and antibody, which led to recovery and identification of influenza peptides within the recovered ICs. This approach was compared with the established method for IC detection and recovery, polyethylene glycol (PEG) precipitation, followed by LC MS/MS. Both approaches successfully enabled capture, recovery and characterization of immunoglobulins and influenza antigen(s) in complex with the immunoglobulins. However, PEG precipitation has the advantage of simplicity and is more suited for large scale studies., Competing Interests: The authors have declared that no competing interests exists.

The identification of antigens incorporated into immune complexes (IC-antigens) is important for studying the pathophysiology of immunological diseases. Immune complexome analysis identifies IC-antigens by analyzing ICs collected from biological fluids by IC-capturing beads. In this study, we optimized the method to improve its comprehensiveness while maintaining selectivity for IC-antigens by comparing the number of identified peptides (model IC experiment) or proteins (human pooled serum) eluted from Protein G beads using different pH solutions (pH 2.0 - 11.0).

Nipah Virus (NiV) has been designated as a priority disease with an urgent need for therapeutic development by World Health Organization. The monoclonal antibody m102.4 binds to the immunodominant NiV receptor-binding glycoprotein (GP), and potently neutralizes NiV, indicating its potential as a therapeutic agent. Although the co-crystal structure of m102.3, an m102.4 derivative, in complex with the GP of the related Hendra Virus (HeV) has been solved, the structural interaction between m102.4 and NiV is uncharacterized. Herein, we used structure-guided alanine-scanning mutagenesis to map the functional epitope and paratope residues that govern the antigen-antibody interaction. Our results revealed that the binding of m102.4 is mediated predominantly by two residues in the HCDR3 region, which is unusually small for an antibody-antigen interaction. We performed computational docking to generate a structural model of m102.4-NiV interaction. Our model indicates that m102.4 targets the common hydrophobic central cavity and a hydrophilic rim on the GP, as observed for the m102.3-HeV co-crystal, albeit with Fv orientation differences. In summary, our study provides insight into the m102.4-NiV interaction, demonstrating that structure-guided alanine-scanning and computational modeling can serve as the starting point for additional antibody reengineering (e.g. affinity maturation) to generate potential therapeutic candidates.

Electrospray mass spectrometry is applied to determine apparent binding energies and quasi equilibrium dissociation constants of immune complex dissociation reactions in the gas phase. Myoglobin, a natural protein-ligand complex, has been used to develop the procedure which starts from determining mean charge states and normalized and averaged ion intensities. The apparent dissociation constant KD m0g#= 3.60 × 10 -12 for the gas phase heme dissociation process was calculated from the mass spectrometry data and by subsequent extrapolation to room temperature to mimic collision conditions for neutral and resting myoglobin. Similarly, for RNAse S dissociation at room temperature a KD m0g#= 4.03 × 10 -12 was determined. The protocol was tested with two immune complexes consisting of epitope peptides and monoclonal antibodies. For the epitope peptide dissociation reaction of the FLAG peptide from the antiFLAG antibody complex an apparent gas phase dissociation constant KD m0g#= 4.04 × 10 -12 was calculated. Likewise, an apparent KD m0g#= 4.58 × 10 -12 was calculated for the troponin I epitope peptide-antiTroponin I antibody immune complex dissociation. Electrospray mass spectrometry is a rapid method, which requires small sample amounts for either identification of protein-bound ligands or for determination of the apparent gas phase protein-ligand complex binding strengths.

The inducible co-stimulator (ICOS) is a member of the CD28/B7 superfamily, and delivers a positive co-stimulatory signal to activated T cells upon binding to its ligand (ICOS-L). Dysregulation of this pathway has been implicated in autoimmune diseases and cancer, and is currently under clinical investigation as an immune checkpoint blockade. Here, we describe the molecular interactions of the ICOS/ICOS-L immune complex at 3.3 Å resolution. A central FDPPPF motif and residues within the CC' loop of ICOS are responsible for the specificity of the interaction with ICOS-L, with a distinct receptor binding orientation in comparison to other family members. Furthermore, our structure and binding data reveal that the ICOS N110 N-linked glycan participates in ICOS-L binding. In addition, we report crystal structures of ICOS and ICOS-L in complex with monoclonal antibodies under clinical evaluation in immunotherapy. Strikingly, antibody paratopes closely mimic receptor-ligand binding core interactions, in addition to contacting peripheral residues to confer high binding affinities. Our results uncover key molecular interactions of an immune complex central to human adaptive immunity and have direct implications for the ongoing development of therapeutic interventions targeting immune checkpoint receptors.