CD19-CD28: an affinity-optimized CD28 agonist for combination with glofitamab (CD20-TCB) as off-the-shelf immunotherapy.

ABSTRACT: Effective T-cell responses not only require the engagement of T-cell receptors (TCRs; "signal 1"), but also the availability of costimulatory signals ("signal 2"). T-cell bispecific antibodies (TCBs) deliver a robust signal 1 by engaging the TCR signaling component CD3ε, while simultaneously binding to tumor antigens. The CD20-TCB glofitamab redirects T cells to CD20-expressing malignant B cells. Although glofitamab exhibits strong single-agent efficacy, adding costimulatory signaling may enhance the depth and durability of T-cell-mediated tumor cell killing. We developed a bispecific CD19-targeted CD28 agonist (CD19-CD28), RG6333, to enhance the efficacy of glofitamab and similar TCBs by delivering signal 2 to tumor-infiltrating T cells. CD19-CD28 distinguishes itself from the superagonistic antibody TGN1412, because its activity requires the simultaneous presence of a TCR signal and CD19 target binding. This is achieved through its engineered format incorporating a mutated Fc region with abolished FcγR and C1q binding, CD28 monovalency, and a moderate CD28 binding affinity. In combination with glofitamab, CD19-CD28 strongly increased T-cell effector functions in ex vivo assays using peripheral blood mononuclear cells and spleen samples derived from patients with lymphoma and enhanced glofitamab-mediated regression of aggressive lymphomas in humanized mice. Notably, the triple combination of glofitamab with CD19-CD28 with the costimulatory 4-1BB agonist, CD19-4-1BBL, offered substantially improved long-term tumor control over glofitamab monotherapy and respective duplet combinations. Our findings highlight CD19-CD28 as a safe and highly efficacious off-the-shelf combination partner for glofitamab, similar TCBs, and other costimulatory agonists. CD19-CD28 is currently in a phase 1 clinical trial in combination with glofitamab. This trial was registered at www.clinicaltrials.gov as #NCT05219513.

The Patent and Literature Antibody Database (PLAbDab): an evolving reference set of functionally diverse, literature-annotated antibody sequences and structures.

Antibodies are key proteins of the adaptive immune system, and there exists a large body of academic literature and patents dedicated to their study and concomitant conversion into therapeutics, diagnostics, or reagents. These documents often contain extensive functional characterisations of the sets of antibodies they describe. However, leveraging these heterogeneous reports, for example to offer insights into the properties of query antibodies of interest, is currently challenging as there is no central repository through which this wide corpus can be mined by sequence or structure. Here, we present PLAbDab (the Patent and Literature Antibody Database), a self-updating repository containing over 150,000 paired antibody sequences and 3D structural models, of which over 65 000 are unique. We describe the methods used to extract, filter, pair, and model the antibodies in PLAbDab, and showcase how PLAbDab can be searched by sequence, structure, or keyword. PLAbDab uses include annotating query antibodies with potential antigen information from similar entries, analysing structural models of existing antibodies to identify modifications that could improve their properties, and facilitating the compilation of bespoke datasets of antibody sequences/structures that bind to a specific antigen. PLAbDab is freely available via Github (https://github.com/oxpig/PLAbDab) and as a searchable webserver (https://opig.stats.ox.ac.uk/webapps/plabdab/).

ABlooper: fast accurate antibody CDR loop structure prediction with accuracy estimation.

MOTIVATION: Antibodies are a key component of the immune system and have been extensively used as biotherapeutics. Accurate knowledge of their structure is central to understanding their antigen-binding function. The key area for antigen binding and the main area of structural variation in antibodies are concentrated in the six complementarity determining regions (CDRs), with the most important for binding and most variable being the CDR-H3 loop. The sequence and structural variability of CDR-H3 make it particularly challenging to model. Recently deep learning methods have offered a step change in our ability to predict protein structures. RESULTS: In this work, we present ABlooper, an end-to-end equivariant deep learning-based CDR loop structure prediction tool. ABlooper rapidly predicts the structure of CDR loops with high accuracy and provides a confidence estimate for each of its predictions. On the models of the Rosetta Antibody Benchmark, ABlooper makes predictions with an average CDR-H3 RMSD of 2.49 Å, which drops to 2.05 Å when considering only its 75% most confident predictions. AVAILABILITY AND IMPLEMENTATION: https://github.com/oxpig/ABlooper. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PEP-Patch: Electrostatics in Protein-Protein Recognition, Specificity, and Antibody Developability.

The electrostatic properties of proteins arise from the number and distribution of polar and charged residues. Electrostatic interactions in proteins play a critical role in numerous processes such as molecular recognition, protein solubility, viscosity, and antibody developability. Thus, characterizing and quantifying electrostatic properties of a protein are prerequisites for understanding these processes. Here, we present PEP-Patch, a tool to visualize and quantify the electrostatic potential on the protein surface in terms of surface patches, denoting separated areas of the surface with a common physical property. We highlight its applicability to elucidate protease substrate specificity and antibody-antigen recognition and predict heparin column retention times of antibodies as an indicator of pharmacokinetics.

Structural mechanism of Fab domain dissociation as a measure of interface stability.

Therapeutic antibodies should not only recognize antigens specifically, but also need to be free from developability issues, such as poor stability. Thus, the mechanistic understanding and characterization of stability are critical determinants for rational antibody design. In this study, we use molecular dynamics simulations to investigate the melting process of 16 antigen binding fragments (Fabs). We describe the Fab dissociation mechanisms, showing a separation in the VH-VL and in the CH1-CL domains. We found that the depths of the minima in the free energy curve, corresponding to the bound states, correlate with the experimentally determined melting temperatures. Additionally, we provide a detailed structural description of the dissociation mechanism and identify key interactions in the CDR loops and in the CH1-CL interface that contribute to stabilization. The dissociation of the VH-VL or CH1-CL domains can be represented by conformational changes in the bend angles between the domains. Our findings elucidate the melting process of antigen binding fragments and highlight critical residues in both the variable and constant domains, which are also strongly germline dependent. Thus, our proposed mechanisms have broad implications in the development and design of new and more stable antigen binding fragments.

Prodrug-Activating Chain Exchange (PACE) converts targeted prodrug derivatives to functional bi- or multispecific antibodies.

Driven by the potential to broaden the target space of conventional monospecific antibodies, the field of multi-specific antibody derivatives is growing rapidly. The production and screening of these artificial proteins entails a high combinatorial complexity. Antibody-domain exchange was previously shown to be a versatile strategy to produce bispecific antibodies in a robust and efficient manner. Here, we show that the domain exchange reaction to generate hybrid antibodies also functions under physiological conditions. Accordingly, we modified the exchange partners for use in therapeutic applications, in which two inactive prodrugs convert into a product with additional functionalities. We exemplarily show the feasibility for generating active T cell bispecific antibodies from two inactive prodrugs, which per se do not activate T cells alone. The two complementary prodrugs harbor antigen-targeting Fabs and non-functional anti-CD3 Fvs fused to IgG-CH3 domains engineered to drive chain-exchange reactions between them. Importantly, Prodrug-Activating Chain Exchange (PACE) could be an attractive option to conditionally activate therapeutics at the target site. Several examples are provided that demonstrate the efficacy of PACE as a new principle of cancer immunotherapy in vitro and in a human xenograft model.

Co-evolutionary distance predictions contain flexibility information.

MOTIVATION: Co-evolution analysis can be used to accurately predict residue-residue contacts from multiple sequence alignments. The introduction of machine-learning techniques has enabled substantial improvements in precision and a shift from predicting binary contacts to predict distances between pairs of residues. These developments have significantly improved the accuracy of de novo prediction of static protein structures. With AlphaFold2 lifting the accuracy of some predicted protein models close to experimental levels, structure prediction research will move on to other challenges. One of those areas is the prediction of more than one conformation of a protein. Here, we examine the potential of residue-residue distance predictions to be informative of protein flexibility rather than simply static structure. RESULTS: We used DMPfold to predict distance distributions for every residue pair in a set of proteins that showed both rigid and flexible behaviour. Residue pairs that were in contact in at least one reference structure were classified as rigid, flexible or neither. The predicted distance distribution of each residue pair was analysed for local maxima of probability indicating the most likely distance or distances between a pair of residues. We found that rigid residue pairs tended to have only a single local maximum in their predicted distance distributions while flexible residue pairs more often had multiple local maxima. These results suggest that the shape of predicted distance distributions contains information on the rigidity or flexibility of a protein and its constituent residues. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Conformational Ensembles of Antibodies Determine Their Hydrophobicity.

A major challenge in the development of antibody biotherapeutics is their tendency to aggregate. One root cause for aggregation is exposure of hydrophobic surface regions to the solvent. Many current techniques predict the relative aggregation propensity of antibodies via precalculated scales for the hydrophobicity or aggregation propensity of single amino acids. However, those scales cannot describe the nonadditive effects of a residue's surrounding on its hydrophobicity. Therefore, they are inherently limited in their ability to describe the impact of subtle differences in molecular structure on the overall hydrophobicity. Here, we introduce a physics-based approach to describe hydrophobicity in terms of the hydration free energy using grid inhomogeneous solvation theory (GIST). We apply this method to assess the effects of starting structures, conformational sampling, and protonation states on the hydrophobicity of antibodies. Our results reveal that high-quality starting structures, i.e., crystal structures, are crucial for the prediction of hydrophobicity and that conformational sampling can compensate errors introduced by the starting structure. On the other hand, sampling of protonation states only leads to good results when combined with high-quality structures, whereas it can even be detrimental otherwise. We conclude by pointing out that a single static homology model may not be adequate for predicting hydrophobicity.

VH -VL interdomain dynamics observed by computer simulations and NMR.

The relative orientation of the two variable domains, VH and VL , influences the shape of the antigen binding site, that is, the paratope, and is essential to understand antigen specificity. ABangle characterizes the VH -VL orientation by using five angles and a distance and compares it to other known structures. Molecular dynamics simulations of antibody variable domains (Fvs) reveal fluctuations in the relative domain orientations. The observed dynamics between these domains are confirmed by NMR experiments on a single-chain variable fragment antibody (scFv) in complex with IL-1ß and an antigen-binding fragment (Fab). The variability of these relative domain orientations can be interpreted as a structural feature of antibodies, which increases the antibody repertoire significantly and can enlarge the number of possible binding partners substantially. The movements of the VH and VL domains are well sampled with molecular dynamics simulations and are in agreement with the NMR ensemble. Fast Fourier transformation of the ABangle metrics allows to assign timescales of 0.1-10 GHz to the fastest collective interdomain movements. The results clearly show the necessity of dynamics to understand and characterize the favorable orientations of the VH and VL domains implying a considerable binding interface flexibility and reveal in all antibody fragments (Fab, scFv, and Fv) very similar VH -VL interdomain variations comparable to the distributions observed for known X-ray structures of antibodies. SIGNIFICANCE STATEMENT: Antibodies have become key players as therapeutic agents. The binding ability of antibodies is determined by the antigen-binding fragment (Fab), in particular the variable fragment region (Fv). Antigen-binding is mediated by the complementarity-determining regions consisting of six loops, each three of the heavy and light chain variable domain VH and VL . The relative orientation of the VH and VL domains influences the shape of the antigen-binding site and is a major objective in antibody design. In agreement with NMR experiments and molecular dynamics simulations, we show a considerable binding site flexibility in the low nanosecond timescale. Thus we suggest that this flexibility and its implications for binding and specificity should be considered when designing and optimizing therapeutic antibodies.

SCALOP: sequence-based antibody canonical loop structure annotation.

MOTIVATION: Canonical forms of the antibody complementarity-determining regions (CDRs) were first described in 1987 and have been redefined on multiple occasions since. The canonical forms are often used to approximate the antibody binding site shape as they can be predicted from sequence. A rapid predictor would facilitate the annotation of CDR structures in the large amounts of repertoire data now becoming available from next generation sequencing experiments. RESULTS: SCALOP annotates CDR canonical forms for antibody sequences, supported by an auto-updating database to capture the latest cluster information. Its accuracy is comparable to that of a standard structural predictor but it is 800 times faster. The auto-updating nature of SCALOP ensures that it always attains the best possible coverage. AVAILABILITY AND IMPLEMENTATION: SCALOP is available as a web application and for download under a GPLv3 license at opig.stats.ox.ac.uk/webapps/scalop. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Antibody side chain conformations are position-dependent.

Side chain prediction is an integral component of computational antibody design and structure prediction. Current antibody modelling tools use backbone-dependent rotamer libraries with conformations taken from general proteins. Here we present our antibody-specific rotamer library, where rotamers are binned according to their immunogenetics (IMGT) position, rather than their local backbone geometry. We find that for some amino acid types at certain positions, only a restricted number of side chain conformations are ever observed. Using this information, we are able to reduce the breadth of the rotamer sampling space. Based on our rotamer library, we built a side chain predictor, position-dependent antibody rotamer swapper (PEARS). On a blind test set of 95 antibody model structures, PEARS had the highest average χ1 and χ1+2 accuracy (78.7% and 64.8%) compared to three leading backbone-dependent side chain predictors. Our use of IMGT position, rather than backbone ϕ/ψ, meant that PEARS was more robust to errors in the backbone of the model structure. PEARS also achieved the lowest number of side chain-side chain clashes. PEARS is freely available as a web application at http://opig.stats.ox.ac.uk/webapps/pears.

Sphinx: merging knowledge-based and ab initio approaches to improve protein loop prediction.

Motivation: Loops are often vital for protein function, however, their irregular structures make them difficult to model accurately. Current loop modelling algorithms can mostly be divided into two categories: knowledge-based, where databases of fragments are searched to find suitable conformations and ab initio, where conformations are generated computationally. Existing knowledge-based methods only use fragments that are the same length as the target, even though loops of slightly different lengths may adopt similar conformations. Here, we present a novel method, Sphinx, which combines ab initio techniques with the potential extra structural information contained within loops of a different length to improve structure prediction. Results: We show that Sphinx is able to generate high-accuracy predictions and decoy sets enriched with near-native loop conformations, performing better than the ab initio algorithm on which it is based. In addition, it is able to provide predictions for every target, unlike some knowledge-based methods. Sphinx can be used successfully for the difficult problem of antibody H3 prediction, outperforming RosettaAntibody, one of the leading H3-specific ab initio methods, both in accuracy and speed. Availability and Implementation: Sphinx is available at http://opig.stats.ox.ac.uk/webapps/sphinx. Contact: deane@stats.ox.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.

Selective pharmacological inhibition of DDR1 prevents experimentally-induced glomerulonephritis in prevention and therapeutic regime.

BACKGROUND: Discoidin domain receptor 1 (DDR1) is a collagen-activated receptor tyrosine kinase extensively implicated in diseases such as cancer, atherosclerosis and fibrosis. Multiple preclinical studies, performed using either a gene deletion or a gene silencing approaches, have shown this receptor being a major driver target of fibrosis and glomerulosclerosis. METHODS: The present study investigated the role and relevance of DDR1 in human crescentic glomerulonephritis (GN). Detailed DDR1 expression was first characterized in detail in human GN biopsies using a novel selective anti-DDR1 antibody using immunohistochemistry. Subsequently the protective role of DDR1 was investigated using a highly selective, novel, small molecule inhibitor in a nephrotoxic serum (NTS) GN model in a prophylactic regime and in the NEP25 GN mouse model using a therapeutic intervention regime. RESULTS: DDR1 expression was shown to be mainly limited to renal epithelium. In humans, DDR1 is highly induced in injured podocytes, in bridging cells expressing both parietal epithelial cell (PEC) and podocyte markers and in a subset of PECs forming the cellular crescents in human GN. Pharmacological inhibition of DDR1 in NTS improved both renal function and histological parameters. These results, obtained using a prophylactic regime, were confirmed in the NEP25 GN mouse model using a therapeutic intervention regime. Gene expression analysis of NTS showed that pharmacological blockade of DDR1 specifically reverted fibrotic and inflammatory gene networks and modulated expression of the glomerular cell gene signature, further validating DDR1 as a major mediator of cell fate in podocytes and PECs. CONCLUSIONS: Together, these results suggest that DDR1 inhibition might be an attractive and promising pharmacological intervention for the treatment of GN, predominantly by targeting the renal epithelium.

SAbPred: a structure-based antibody prediction server.

SAbPred is a server that makes predictions of the properties of antibodies focusing on their structures. Antibody informatics tools can help improve our understanding of immune responses to disease and aid in the design and engineering of therapeutic molecules. SAbPred is a single platform containing multiple applications which can: number and align sequences; automatically generate antibody variable fragment homology models; annotate such models with estimated accuracy alongside sequence and structural properties including potential developability issues; predict paratope residues; and predict epitope patches on protein antigens. The server is available at http://opig.stats.ox.ac.uk/webapps/sabpred.

The H3 loop of antibodies shows unique structural characteristics.

The H3 loop in the Complementarity Determining Region of antibodies plays a key role in their ability to bind the diverse space of potential antigens. It is also exceptionally difficult to model computationally causing a significant hurdle for in silico development of antibody biotherapeutics. In this article, we show that most H3s have unique structural characteristics which may explain why they are so challenging to model. We found that over 75% of H3 loops do not have a sub-Angstrom structural neighbor in the non-antibody world. Also, in a comparison with a nonredundant set of all protein fragments over 30% of H3 loops have a unique structure, with the average for all of other loops being less than 3%. We further observed that this structural difference can be seen at the level of four residue fragments where H3 loops present numerous novel conformations, and also at the level of individual residues with Tyrosine and Glycine often found in energetically unfavorable conformations. Proteins 2017; 85:1311-1318. © 2017 Wiley Periodicals, Inc.

Hapten-directed spontaneous disulfide shuffling: a universal technology for site-directed covalent coupling of payloads to antibodies.

Humanized hapten-binding IgGs were designed with an accessible cysteine close to their binding pockets, for specific covalent payload attachment. Individual analyses of known structures of digoxigenin (Dig)- and fluorescein (Fluo) binding antibodies and a new structure of a biotin (Biot)-binder, revealed a "universal" coupling position (52(+2)) in proximity to binding pockets but without contributing to hapten interactions. Payloads that carry a free thiol are positioned on the antibody and covalently linked to it via disulfides. Covalent coupling is achieved and driven toward complete (95-100%) payload occupancy by spontaneous redox shuffling between antibody and payload. Attachment at the universal position works with different haptens, antibodies, and payloads. Examples are the haptens Fluo, Dig, and Biot combined with various fluorescent or peptidic payloads. Disulfide-bonded covalent antibody-payload complexes do not dissociate in vitro and in vivo. Coupling requires the designed cysteine and matching payload thiol because payload or antibody without the Cys/thiol are not linked (<5% nonspecific coupling). Hapten-mediated positioning is necessary as hapten-thiol-payload is only coupled to antibodies that bind matching haptens. Covalent complexes are more stable in vivo than noncovalent counterparts because digoxigeninylated or biotinylated fluorescent payloads without disulfide-linkage are cleared more rapidly in mice (approximately 50% reduced 48 hour serum levels) compared with their covalently linked counterparts. The coupling technology is applicable to many haptens and hapten binding antibodies (confirmed by automated analyses of the structures of 140 additional hapten binding antibodies) and can be applied to modulate the pharmacokinetics of small compounds or peptides. It is also suitable to link payloads in a reduction-releasable manner to tumor- or tissue-targeting delivery vehicles.

SAbDab: the structural antibody database.

Structural antibody database (SAbDab; http://opig.stats.ox.ac.uk/webapps/sabdab) is an online resource containing all the publicly available antibody structures annotated and presented in a consistent fashion. The data are annotated with several properties including experimental information, gene details, correct heavy and light chain pairings, antigen details and, where available, antibody-antigen binding affinity. The user can select structures, according to these attributes as well as structural properties such as complementarity determining region loop conformation and variable domain orientation. Individual structures, datasets and the complete database can be downloaded.

Prediction of VH-VL domain orientation for antibody variable domain modeling.

The antigen-binding site of antibodies forms at the interface of their two variable domains, VH and VL, making VH-VL domain orientation a factor that codetermines antibody specificity and affinity. Preserving VH-VL domain orientation in the process of antibody engineering is important in order to retain the original antibody properties, and predicting the correct VH-VL orientation has also been recognized as an important factor in antibody homology modeling. In this article, we present a fast sequence-based predictor that predicts VH-VL domain orientation with Q(2) values ranging from 0.54 to 0.73 on the evaluation set. We describe VH-VL orientation in terms of the six absolute ABangle parameters that have recently been proposed as a means to separate the different degrees of freedom of VH-VL domain orientation. In order to assess the impact of adjusting VH-VL orientation according to our predictions, we use the set of antibody structures of the recently published Antibody Modeling Assessment (AMA) II study. In comparison to the original AMAII homology models, we find an improvement in the accuracy of VH-VL orientation modeling, which also translates into an improvement in the average root-mean-square deviation with regard to the crystal structures.

Immunoglobulin domain crossover as a generic approach for the production of bispecific IgG antibodies.

We describe a generic approach to assemble correctly two heavy and two light chains, derived from two existing antibodies, to form human bivalent bispecific IgG antibodies without use of artificial linkers. Based on the knobs-into-holes technology that enables heterodimerization of the heavy chains, correct association of the light chains and their cognate heavy chains is achieved by exchange of heavy-chain and light-chain domains within the antigen binding fragment (Fab) of one half of the bispecific antibody. This "crossover" retains the antigen-binding affinity but makes the two arms so different that light-chain mispairing can no longer occur. Applying the three possible "CrossMab" formats, we generated bispecific antibodies against angiopoietin-2 (Ang-2) and vascular endothelial growth factor A (VEGF-A) and show that they can be produced by standard techniques, exhibit stabilities comparable to natural antibodies, and bind both targets simultaneously with unaltered affinity. Because of its superior side-product profile, the CrossMab(CH1-CL) was selected for in vivo profiling and showed potent antiangiogenic and antitumoral activity.

Bispecific digoxigenin-binding antibodies for targeted payload delivery.

Bispecific antibodies that bind cell-surface targets as well as digoxigenin (Dig) were generated for targeted payload delivery. Targeting moieties are IgGs that bind the tumor antigens Her2, IGF1R, CD22, or LeY. A Dig-binding single-chain Fv was attached in disulfide-stabilized form to C termini of CH3 domains of targeting antibodies. Bispecific molecules were expressed in mammalian cells and purified in the same manner as unmodified IgGs. They are stable without aggregation propensity and retain binding specificity/affinity to cell-surface antigens and Dig. Digoxigeninylated payloads were generated that retain full functionality and can be complexed to bispecific antibodies in a defined 21 ratio. Payloads include small compounds (Dig-Cy5, Dig-Doxorubicin) and proteins (Dig-GFP). Complexed payloads are targeted by the bispecifics to cancer cells and because these complexes are stable in serum, they can be applied for targeted delivery. Because Dig bispecifics also effectively capture digoxigeninylated compounds under physiological conditions, separate administration of uncharged Dig bispecifics followed by application of Dig payload is sufficient to achieve antibody-mediated targeting in vitro and in vivo.