The human infectious disease COVID-19 caused by the SARS-CoV-2 virus has become a major threat to global public health. Developing a vaccine is the preferred prophylactic response to epidemics and pandemics. However, for individuals who have contracted the disease, the rapid design of antibodies that can target the SARS-CoV-2 virus fulfils a critical need. Further, discovering antibodies that bind multiple variants of SARS-CoV-2 can aid in the development of rapid antigen tests (RATs) which are critical for the identification and isolation of individuals currently carrying COVID-19. Here we provide a proof-of-concept study for the computational design of high-affinity antibodies that bind to multiple variants of the SARS-CoV-2 spike protein using RosettaAntibodyDesign (RAbD). Well characterized antibodies that bind with high affinity to the SARS-CoV-1 (but not SARS-CoV-2) spike protein were used as templates and re-designed to bind the SARS-CoV-2 spike protein with high affinity, resulting in a specificity switch. A panel of designed antibodies were experimentally validated. One design bound to a broad range of vari-ants of concern including the Omicron, Delta, Wuhan, and South African spike protein variants.

[This corrects the article DOI: 10.1016/j.heliyon.2023.e15032.].

TCR-like antibodies represent a unique type of engineered antibodies with specificity toward pHLA, a ligand normally restricted to the sensitive recognition by T cells. Here, we report a phage display-based sequential development path of such antibodies. The strategy goes from initial lead identification through in silico informed CDR engineering in combination with framework engineering for affinity and thermostability optimization, respectively. The strategy allowed the identification of HLA-DQ2.5 gluten peptide-specific TCR-like antibodies with low picomolar affinity. Our method outlines an efficient and general method for development of this promising class of antibodies, which should facilitate their utility including translation to human therapy.

Antibodies are widely developed and used as therapeutics to treat cancer, infectious disease, and inflammation. During development, initial leads routinely undergo additional engineering to increase their target affinity. Experimental methods for affinity maturation are expensive, laborious, and time-consuming and rarely allow the efficient exploration of the relevant design space. Deep learning (DL) models are transforming the field of protein engineering and design. While several DL-based protein design methods have shown promise, the antibody design problem is distinct, and specialized models for antibody design are desirable. Inspired by hallucination frameworks that leverage accurate structure prediction DL models, we propose the FvHallucinator for designing antibody sequences, especially the CDR loops, conditioned on an antibody structure. Such a strategy generates targeted CDR libraries that retain the conformation of the binder and thereby the mode of binding to the epitope on the antigen. On a benchmark set of 60 antibodies, FvHallucinator generates sequences resembling natural CDRs and recapitulates perplexity of canonical CDR clusters. Furthermore, the FvHallucinator designs amino acid substitutions at the VH-VL interface that are enriched in human antibody repertoires and therapeutic antibodies. We propose a pipeline that screens FvHallucinator designs to obtain a library enriched in binders for an antigen of interest. We apply this pipeline to the CDR H3 of the Trastuzumab-HER2 complex to generate in silico designs predicted to improve upon the binding affinity and interfacial properties of the original antibody. Thus, the FvHallucinator pipeline enables generation of inexpensive, diverse, and targeted antibody libraries enriched in binders for antibody affinity maturation.

Monoclonal IgG antibodies are the fastest growing class of biologics, but large differences exist in their plasma half-life in humans. Thus, to design IgG antibodies with favorable pharmacokinetics, it is crucial to identify the determinants of such differences. Here, we demonstrate that the variable region sequences of IgG antibodies greatly affect cellular uptake and subsequent recycling and rescue from intracellular degradation by endothelial cells. When the variable sequences are masked by the cognate antigen, it influences both their transport behavior and binding to the neonatal Fc receptor (FcRn), a key regulator of IgG plasma half-life. Furthermore, we show how charge patch differences in the variable domains modulate both binding and transport properties and that a short plasma half-life, due to unfavorable charge patches, may partly be overcome by Fc-engineering for improved FcRn binding.

The continued emergence of new SARS-CoV-2 variants has accentuated the growing need for fast and reliable methods for the design of potentially neutralizing antibodies (Abs) to counter immune evasion by the virus. Here, we report on the de novo computational design of high-affinity Ab variable regions (Fv) through the recombination of VDJ genes targeting the most solvent-exposed hACE2-binding residues of the SARS-CoV-2 spike receptor binding domain (RBD) protein using the software tool OptMAVEn-2.0. Subsequently, we carried out computational affinity maturation of the designed variable regions through amino acid substitutions for improved binding with the target epitope. Immunogenicity of designs was restricted by preferring designs that match sequences from a 9-mer library of "human Abs" based on a human string content score. We generated 106 different antibody designs and reported in detail on the top five that trade-off the greatest computational binding affinity for the RBD with human string content scores. We further describe computational evaluation of the top five designs produced by OptMAVEn-2.0 using a Rosetta-based approach. We used Rosetta SnugDock for local docking of the designs to evaluate their potential to bind the spike RBD and performed "forward folding" with DeepAb to assess their potential to fold into the designed structures. Ultimately, our results identified one designed Ab variable region, P1.D1, as a particularly promising candidate for experimental testing. This effort puts forth a computational workflow for the de novo design and evaluation of Abs that can quickly be adapted to target spike epitopes of emerging SARS-CoV-2 variants or other antigenic targets.

Over the last few years, deep learning (DL) models have made a significant impact on the field of protein structure prediction.1 Recently DL models such as AlphaFold22 have achieved state-of-the-art performance on blind structure prediction of difficult protein targets. Additionally, several DL models have also been proposed for variant stability prediction (ESM-v13) and design of de novo proteins (trDesign4). Protein design is the inverse problem of protein structure prediction, i.e., the prediction of sequence given structure. Antibody design against an antigen of interest is a particularly challenging problem since it involves the design of the highly variable CDR loop regions to bind an antigen with reasonable affinity and specificity. In this work, we have extended and assessed some of the latest DL frameworks proposed for protein design to design antibodies against antigens of interest. Specifically, we have developed the hallucination framework with DeepAb5 to design antibody CDR regions conditioned on structure. Our DL framework allows the generation of antibody CDR sequence libraries conditioned on structure. Additionally, we have compiled a benchmark datasets of Deep Mutational Scanning experiments for single-site mutation analysis and deep sequencing datasets for combinatorial design to validate antibodies designed with this framework. Our framework can identify stability and affinity enhancing designs.Furthermore, we have applied this framework to design CDR loops against target antigens and compared our designs with experimental deep sequencing data. We will present some of the early successes and important outstanding challenges in the use of current DL frameworks for antibody design. 1. Gao, W., Mahajan, S. P., Sulam, J. & Gray, J. J. Patterns 1, 100142 (2020). 2. Jumper, J. et al. Nature (2021). 3. Meier, J. et al. (2021).4. Norn, C. et al. doi:10.1101/2020.07.23.218917.5. Ruffolo, J. A., Sulam, J. & Gray, J. J. (2021).

Antibody-based therapeutics (ABTs) are used to treat a range of diseases. Most ABTs are either full-length IgG1 antibodies or fusions between for instance antigen (Ag)-binding receptor domains and the IgG1 Fc fragment. Interestingly, their plasma half-life varies considerably, which may relate to how they engage the neonatal Fc receptor (FcRn). As such, there is a need for an in-depth understanding of how different features of ABTs affect FcRn-binding and transport behavior. Here, we report on how FcRn-engagement of the IgG1 Fc fragment compare to clinically relevant IgGs and receptor domain Fc fusions, binding to VEGF or TNF-α. The results reveal FcRn-dependent intracellular accumulation of the Fc, which is in line with shorter plasma half-life than that of full-length IgG1 in human FcRn-expressing mice. Receptor domain fusion to the Fc increases its half-life, but not to the extent of IgG1. This is mirrored by a reduced cellular recycling capacity of the Fc-fusions. In addition, binding of cognate Ag to ABTs show that complexes of similar size undergo cellular transport at different rates, which could be explained by the biophysical properties of each ABT. Thus, the study provides knowledge that should guide tailoring of ABTs regarding optimal cellular sorting and plasma half-life.

In recent years, the observed antibody sequence space has grown exponentially due to advances in high-throughput sequencing of immune receptors. The rise in sequences has not been mirrored by a rise in structures, as experimental structure determination techniques have remained low-throughput. Computational modeling, however, has the potential to close the sequence–structure gap. To achieve this goal, computational methods must be robust, fast, easy to use, and accurate. Here we report on the latest advances made in RosettaAntibody and Rosetta SnugDock—methods for antibody structure prediction and antibody–antigen docking. We simplified the user interface, expanded and automated the template database, generalized the kinematics of antibody–antigen docking (which enabled modeling of single-domain antibodies) and incorporated new loop modeling techniques. To evaluate the effects of our updates on modeling accuracy, we developed rigorous tests under a new scientific benchmarking framework within Rosetta. Benchmarking revealed that more structurally similar templates could be identified in the updated database and that SnugDock broadened its applicability without losing accuracy. However, there are further advances to be made, including increasing the accuracy and speed of CDR-H3 loop modeling, before computational approaches can accurately model any antibody.

Antibodies specific for peptides bound to human leukocyte antigen (HLA) molecules are valuable tools for studies of antigen presentation and may have therapeutic potential. Here, we generated human T cell receptor (TCR)-like antibodies toward the immunodominant signature gluten epitope DQ2.5-glia-α2 in celiac disease (CeD). Phage display selection combined with secondary targeted engineering was used to obtain highly specific antibodies with picomolar affinity. The crystal structure of a Fab fragment of the lead antibody 3.C11 in complex with HLA-DQ2.5:DQ2.5-glia-α2 revealed a binding geometry and interaction mode highly similar to prototypic TCRs specific for the same complex. Assessment of CeD biopsy material confirmed disease specificity and reinforced the notion that abundant plasma cells present antigen in the inflamed CeD gut. Furthermore, 3.C11 specifically inhibited activation and proliferation of gluten-specific CD4+ T cells in vitro and in HLA-DQ2.5 humanized mice, suggesting a potential for targeted intervention without compromising systemic immunity.

The semi-public T-cell response towards the gluten epitope DQ2.5-glia-alpha 2 uses a prototypic TCR encoded by the germline segments TRAV26-1 and TRBV7-2. Through mutagenesis experiments, we show that a TRAV26-1encoded recognition motif contacts the MHC beta-chain and the TCR CDR3 beta loop underpinning this conserved T-cell response restricted to the prototypic TCRs.

Antibodies specific for antigenic peptides bound to major histocompatibility complex (MHC) molecules are valuable tools for studies of antigen presentation. Such T-cell receptor (TCR)-like antibodies may also have therapeutic potential in human disease due to their ability to target disease-associated antigens with high specificity. We previously generated celiac disease (CeD) relevant TCR-like antibodies that recognize the prevalent gluten epitope DQ2.5-glia-α1a in complex with HLA-DQ2.5. Here, we report on second-generation high-affinity antibodies towards this epitope as well as a panel of novel TCR-like antibodies to another immunodominant gliadin epitope, DQ2.5-glia-α2. The strategy for affinity engineering was based on Rosetta modeling combined with pIX phage display and is applicable to similar protein engineering efforts. We isolated picomolar affinity binders and validated them in Fab and IgG format. Flow cytometry experiments with CeD biopsy material confirm the unique disease specificity of these TCR-like antibodies and reinforce the notion that B cells and plasma cells have a dominant role in gluten antigen presentation in the inflamed CeD gut. Further, the lead candidate 3.C11 potently inhibited CD4+ T-cell activation and proliferation in vitro in an HLA and epitope specific manner, pointing to a potential for targeted disease interception without compromising systemic immunity. Significance Statement Consumption of gluten-containing food drives celiac disease in genetically predisposed individuals. The underlying disease mechanism is not fully understood, but it is strictly dependent on activation of pathogenic T cells. We have engineered high-affinity human antibodies recognizing the T-cell target HLA-DQ2.5 in complex with gluten epitopes and studied cell-specific antigen presentation in patients, which shows that plasma cells and not dendritic cells dominate the inflamed tissue. The only available treatment is lifelong adherence to a gluten-free diet, which is difficult and not effective in all cases. We show that at least one of our antibodies can specifically inhibit activation of pathogenic T-cells in vitro and therefore shows promise for therapy.

BACKGROUND & AIMS:Development of celiac disease is believed to involve the transglutaminase-dependent response of CD4+ T cells toward deamidated gluten peptides in the intestinal mucosa of individuals with specific HLA-DQ haplotypes. We investigated the antigen presentation process during this mucosal immune response.METHODS:We generated monoclonal antibodies (mAbs) specific for the peptide-MHC (pMHC) complex of HLA-DQ2.5 and the immunodominant gluten epitope DQ2.5-glia-α1a using phage display. We used these mAbs to assess gluten peptide presentation and phenotypes of presenting cells by flow cytometry and enzyme-linked immune absorbent spot (ELISPOT) in freshly prepared single-cell suspensions from intestinal biopsies from 40 patients with celiac disease (35 untreated and 5 on a gluten-free diet) as well as 18 subjects with confirmed noninflamed gut mucosa (controls, 12 presumed healthy, 5 undergoing pancreatoduodenectomy, and 1 with potential celiac disease).RESULTS:Using the mAbs, we detected MHC complexes on cells from intestinal biopsies from patients with celiac disease who consume gluten, but not from patients on gluten-free diets. We found B cells and plasma cells to be the most abundant cells that present DQ2.5-glia-α1a in the inflamed mucosa. We identified a subset of plasma cells that expresses B-cell receptors (BCR) specific for gluten peptides or the autoantigen transglutaminase 2 (TG2). Expression of MHC class II (MHCII) was not restricted to these specific plasma cells in patients with celiac disease but was observed in an average 30% of gut plasma cells from patients and controls.CONCLUSIONS:A population of plasma cells from intestinal biopsies of patients with celiac disease express MHCII; this is the most abundant cell type presenting the immunodominant gluten peptide DQ2.5-glia-α1a in the tissues from these patients. These results indicate that plasma cells in the gut can function as antigen-presenting cells and might promote and maintain intestinal inflammation in patients with celiac disease or other inflammatory disorders.

Monoclonal antibodies (mAbs) are valuable as research reagents, in diagnosis and in therapy. Their high specificity, the ease in production, favorable biophysical properties and the opportunity to engineer different properties make mAbs a versatile class of biologics. mAbs targeting peptide–major histocompatibility molecule (pMHC) complexes are often referred to as “TCR-like” mAbs, as pMHC complexes are generally recognized by T-cell receptors (TCRs). Presentation of self- and non-self-derived peptide fragments on MHC molecules and subsequent activation of T cells dictate immune responses in health and disease. This includes responses to infectious agents or cancer but also aberrant responses against harmless self-peptides in autoimmune diseases. The ability of TCR-like mAbs to target specific peptides presented on MHC allows for their use to study peptide presentation or for diagnosis and therapy. This extends the scope of conventional mAbs, which are generally limited to cell-surface or soluble antigens. Herein, we review the strategies used to generate TCR-like mAbs and provide a structural comparison with the analogous TCR in pMHC binding. We further discuss their applications as research tools and therapeutic reagents in preclinical models as well as challenges and limitations associated with their use.

The semi-public T-cell response towards the gluten epitope DQ2.5-glia-α2 uses a prototypic TCR encoded by the germline segments TRAV26-1 and TRBV7-2. Through mutagenesis experiments, we show that a TRAV26-1encoded recognition motif contacts the MHC β-chain and the TCR CDR3β loop underpinning this conserved T-cell response restricted to the prototypic TCRs.

Selection of biased T cell receptor (TCR) repertoires across individuals is seen in both infectious diseases and autoimmunity, but the underlying molecular basis leading to these shared repertoires remains unclear. Celiac disease (CD) occurs primarily in HLA-DQ2.5+ individuals and is characterized by a CD4+ T cell response against gluten epitopes dominated by DQ2.5-glia-α1a and DQ2.5-glia-α2. The DQ2.5-glia-α2 response recruits a highly biased TCR repertoire composed of TRAV26-1 paired with TRBV7-2 harboring a semipublic CDR3β loop. We aimed to unravel the molecular basis for this signature. By variable gene segment exchange, directed mutagenesis, and cellular T cell activation studies, we found that TRBV7-3 can substitute for TRBV7-2, as both can contain the canonical CDR3β loop. Furthermore, we identified a pivotal germline-encoded MHC recognition motif centered on framework residue Y40 in TRAV26-1 engaging both DQB1*02 and the canonical CDR3β. This allowed prediction of expanded DQ2.5-glia-α2-reactive TCR repertoires, which were confirmed by single-cell sorting and TCR sequencing from CD patient samples. Our data refine our understanding of how HLA-dependent biased TCR repertoires are selected in the periphery due to germline-encoded residues.