High-throughput epitope binning of therapeutic monoclonal antibodies: why you need to bin the fridge

Epitope binning for multiple antibodies simultaneously using mammalian cell display and DNA sequencing

Epitope binning, an approach for grouping antibodies based on epitope similarities, is a critical step in antibody drug discovery. However, conventional methods are complex, involving individual antibody production. Here, we established Epitope Binning-seq, an epitope binning platform for simultaneously analyzing multiple antibodies. In this system, epitope similarity between the query antibodies (qAbs) displayed on antigen-expressing cells and a fluorescently labeled reference antibody (rAb) targeting a desired epitope is analyzed by flow cytometry. The qAbs with epitope similar to the rAb can be identified by next-generation sequencing analysis of fluorescence-negative cells. Sensitivity and reliability of this system are confirmed using rAbs, pertuzumab and trastuzumab, which target human epidermal growth factor receptor 2. Epitope Binning-seq enables simultaneous epitope evaluation of 14 qAbs at various abundances in libraries, grouping them into respective epitope bins. This versatile platform is applicable to diverse antibodies and antigens, potentially expediting the identification of clinically useful antibodies.

Mapping antibody footprints using binding profiles

The increasing use of monoclonal antibodies (mAbs) in biology and medicine necessitates efficient methods for characterizing their binding epitopes. Here, we developed a high-throughput antibody footprinting method based on binding profiles. We used an antigen microarray to profile 23 human anti-influenza hemagglutinin (HA) mAbs using HA proteins of 43 human influenza strains isolated between 1918 and 2018. We showed that the mAb's binding profile can be used to characterize its influenza subtype specificity, binding region, and binding site. We present mAb-Patch-an epitope prediction method that is based on a mAb's binding profile and the 3D structure of its antigen. mAb-Patch was evaluated using four mAbs with known solved mAb-HA structures. mAb-Patch identifies over 67% of the true epitope when considering only 50-60 positions along the antigen. Our work provides proof of concept for utilizing antibody binding profiles to screen large panels of mAbs and to down-select antibodies for further functional studies.

Hydrogen Deuterium Exchange and other Mass Spectrometry-based Approaches for Epitope Mapping

Antigen-antibody interactions are a fundamental subset of protein-protein interactions responsible for the "survival of the fittest". Determining the interacting interface of the antigen, called an epitope, and that on the antibody, called a paratope, is crucial to antibody development. Because each antigen presents multiple epitopes (unique footprints), sophisticated approaches are required to determine the target region for a given antibody. Although X-ray crystallography, Cryo-EM, and nuclear magnetic resonance can provide atomic details of an epitope, they are often laborious, poor in throughput, and insensitive. Mass spectrometry-based approaches offer rapid turnaround, intermediate structural resolution, and virtually no size limit for the antigen, making them a vital approach for epitope mapping. In this review, we describe in detail the principles of hydrogen deuterium exchange mass spectrometry in application to epitope mapping. We also show that a combination of MS-based approaches can assist or complement epitope mapping and push the limit of structural resolution to the residue level. We describe in detail the MS methods used in epitope mapping, provide our perspective about the approaches, and focus on elucidating the role that HDX-MS is playing now and in the future by organizing a discussion centered around several improvements in prototype instrument/applications used for epitope mapping. At the end, we provide a tabular summary of the current literature on HDX-MS-based epitope mapping.

Computational epitope binning reveals functional equivalence of sequence-divergent paratopes

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Epitope binning groups target-specific protein binders recognizing the same binding region.

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The “Epibin” method utilizes docking models to computationally predict competition and identify bins.

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Epibin recapitulated binding competition of repebody variants as determined by immunoassays.

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In addition, Epibin enabled identification of ‘paratope-equivalent’ residues in sequence-dissimilar variants.

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Computational epitope binning can scale to allow characterization of entire antigen-specific antibody repertoires.

The therapeutic efficacy of a protein binder largely depends on two factors: its binding site and its binding affinity. Advances in in vitro library display screening and next-generation sequencing have enabled accelerated development of strong binders, yet identifying their binding sites still remains a major challenge. The differentiation, or “binning”, of binders into different groups that recognize distinct binding sites on their target is a promising approach that facilitates high-throughput screening of binders that may show different biological activity. Here we study the extent to which the information contained in the amino acid sequences comprising a set of target-specific binders can be leveraged to bin them, inferring functional equivalence of their binding regions, or paratopes, based directly on comparison of the sequences, their modeled structures, or their modeled interactions. Using a leucine-rich repeat binding scaffold known as a “repebody” as the source of diversity in recognition against interleukin-6 (IL-6), we show that the “Epibin” approach introduced here effectively utilized structural modelling and docking to extract specificity information encoded in the repebody amino acid sequences and thereby successfully recapitulate IL-6 binding competition observed in immunoassays. Furthermore, our computational binning provided a basis for designing in vitro mutagenesis experiments to pinpoint specificity-determining residues. Finally, we demonstrate that the Epibin approach can extend to antibodies, retrospectively comparing its predictions to results from antigen-specific antibody competition studies. The study thus demonstrates the utility of modeling structure and binding from the amino acid sequences of different binders against the same target, and paves the way for larger-scale binning and analysis of entire repertoires.

High-Throughput Surface Plasmon Resonance Biosensors for Identifying Diverse Therapeutic Monoclonal Antibodies

Identification of antibodies targeting diverse functional epitopes on an antigen is highly crucial for discovering effective therapeutic candidates. Employing a traditional stepwise antibody "screening funnel" as well as prioritizing affinity-based selections over epitope-based selections, result in lead antibody panels lacking epitope diversity. In the present study, we employed an array-based surface plasmon resonance (SPR) platform to perform high-throughput epitope binning analysis on a large number of monoclonal antibodies (mAbs) generated in the early drug discovery process. The mAb panel contained clones from different antibody generation techniques and diverse transgenic mouse strains. The epitope binning results were analyzed in unique ways using various visualizations in the form of dendrograms and network plots, which assisted in determining diversity and redundancy in the mAb sample set. The binning data were further integrated with affinity information to evaluate the performance of seven different transgenic mouse strains. The combination of epitope binning results with binding kinetics and sequence analysis provided an effective and efficient way of selecting high affinity antibodies representing a diverse set of sequence families and epitopes.

Targeting a noncanonical, hairpin-containing G-quadruplex structure from the MYCN gene

The MYCN gene encodes the transcription factor N-Myc, a driver of neuroblastoma (NB). Targeting G-quadruplexes (G4s) with small molecules is attractive strategy to control the expression of undruggable proteins such as N-Myc. However, selective binders to G4s are challenging to identify due to the structural similarity of many G4s. Here, we report the discovery of a small molecule ligand (4) that targets the noncanonical, hairpin containing G4 structure found in the MYCN gene using small molecule microarrays (SMMs). Unlike many G4 binders, the compound was found to bind to a pocket at the base of the hairpin region of the MYCN G4. This compound stabilizes the G4 and has affinity of 3.5 ± 1.6 μM. Moreover, an improved analog, MY-8, suppressed levels of both MYCN and MYCNOS (a lncRNA embedded within the MYCN gene) in NBEB neuroblastoma cells. This work indicates that the approach of targeting complex, hybrid G4 structures that exist throughout the human genome may be an applicable strategy to achieve selectivity for targeting disease-relevant genes including protein coding (MYCN) as well as non-coding (MYCNOS) gene products.

Learning context-aware structural representations to predict antigen and antibody binding interfaces

MOTIVATION

Understanding how antibodies specifically interact with their antigens can enable better drug and vaccine design, as well as provide insights into natural immunity. Experimental structural characterization can detail the 'ground truth' of antibody-antigen interactions, but computational methods are required to efficiently scale to large-scale studies. To increase prediction accuracy as well as to provide a means to gain new biological insights into these interactions, we have developed a unified deep learning-based framework to predict binding interfaces on both antibodies and antigens.

RESULTS

Our framework leverages three key aspects of antibody-antigen interactions to learn predictive structural representations: (i) since interfaces are formed from multiple residues in spatial proximity, we employ graph convolutions to aggregate properties across local regions in a protein; (ii) since interactions are specific between antibody-antigen pairs, we employ an attention layer to explicitly encode the context of the partner; (iii) since more data are available for general protein-protein interactions, we employ transfer learning to leverage this data as a prior for the specific case of antibody-antigen interactions. We show that this single framework achieves state-of-the-art performance at predicting binding interfaces on both antibodies and antigens, and that each of its three aspects drives additional improvement in the performance. We further show that the attention layer not only improves performance, but also provides a biologically interpretable perspective into the mode of interaction.

AVAILABILITY AND IMPLEMENTATION

The source code is freely available on github at https://github.com/vamships/PECAN.git.

Localization of the Interaction Site of Herpes Simplex Virus Glycoprotein D (gD) on the Membrane Fusion Regulator, gH/gL

A cascade of protein-protein interactions between four herpes simplex virus (HSV) glycoproteins (gD, gH/gL, and gB) drive fusion between the HSV envelope and host membrane, thereby allowing for virus entry and infection. Specifically, binding of gD to one of its receptors induces a conformational change that allows gD to bind to the regulatory complex gH/gL, which then activates the fusogen gB, resulting in membrane fusion. Using surface plasmon resonance and a panel of anti-gD monoclonal antibodies (MAbs) that sterically blocked the interaction, we previously showed that gH/gL binds directly to gD at sites distinct from the gD receptor binding site. Here, using an analogous strategy, we first evaluated the ability of a panel of uncharacterized anti-gH/gL MAbs to block binding to gD and/or inhibit fusion. We found that the epitopes of four gD-gH/gL-blocking MAbs were located within flexible regions of the gH N terminus and the gL C terminus, while the fifth was placed around gL residue 77. Taken together, our data localized the gD binding region on gH/gL to a group of gH and gL residues at the membrane distal region of the heterodimer. Surprisingly, a second set of MAbs did not block gD-gH/gL binding but instead stabilized the complex by altering the kinetic binding. However, despite this prolonged gD-gH/gL interaction, "stabilizing" MAbs also inhibited cell-cell fusion, suggesting a unique mechanism by which the fusion process is halted. Our findings support targeting the gD-gH/gL interaction to prevent fusion in both therapeutic and vaccine strategies against HSV.IMPORTANCE Key to developing a human HSV vaccine is an understanding of the virion glycoproteins involved in entry. HSV employs multiple glycoproteins for attachment, receptor interaction, and membrane fusion. Determining how these proteins function was resolved, in part, by structural biology coupled with immunological and biologic evidence. After binding, virion gD interacts with a receptor to activate the regulator gH/gL complex, triggering gB to drive fusion. Multiple questions remain, one being the physical location of each glycoprotein interaction site. Using protective antibodies with known epitopes, we documented the long-sought interaction between gD and gH/gL, detailing the region on gD important to create the gD-gH/gL triplex. Now, we have identified the corresponding gD contact sites on gH/gL. Concurrently we discovered a novel mechanism whereby gH/gL antibodies stabilize the complex and inhibit fusion progression. Our model for the gD-gH/gL triplex provides a new framework for studying fusion, which identifies targets for vaccine development.

Characterizing Epitope Binding Regions of Entire Antibody Panels by Combining Experimental and Computational Analysis of Antibody: Antigen Binding Competition

Vaccines and immunotherapies depend on the ability of antibodies to sensitively and specifically recognize particular antigens and specific epitopes on those antigens. As such, detailed characterization of antibody-antigen binding provides important information to guide development. Due to the time and expense required, high-resolution structural characterization techniques are typically used sparingly and late in a development process. Here, we show that antibody-antigen binding can be characterized early in a process for whole panels of antibodies by combining experimental and computational analyses of competition between monoclonal antibodies for binding to an antigen. Experimental "epitope binning" of monoclonal antibodies uses high-throughput surface plasmon resonance to reveal which antibodies compete, while a new complementary computational analysis that we call "dock binning" evaluates antibody-antigen docking models to identify why and where they might compete, in terms of possible binding sites on the antigen. Experimental and computational characterization of the identified antigenic hotspots then enables the refinement of the competitors and their associated epitope binding regions on the antigen. While not performed at atomic resolution, this approach allows for the group-level identification of functionally related monoclonal antibodies (i.e., communities) and identification of their general binding regions on the antigen. By leveraging extensive epitope characterization data that can be readily generated both experimentally and computationally, researchers can gain broad insights into the basis for antibody-antigen recognition in wide-ranging vaccine and immunotherapy discovery and development programs.

Rapid exploration of the epitope coverage produced by an Ebola survivor to guide the discovery of therapeutic antibody cocktails

Background

Development of successful neutralizing antibodies is dependent upon broad epitope coverage to increase the likelihood of achieving therapeutic function. Recent advances in synthetic biology have allowed us to conduct an epitope binning study on a large panel of antibodies identified to bind to Ebola virus glycoprotein with only published sequences.

Methods and Results

A rapid, first-pass epitope binning experiment revealed seven distinct epitope families that overlapped with known structural epitopes from the literature. A focused set of antibodies was selected from representative clones per bin to guide a second-pass binning that revealed previously unassigned epitopes, confirmed epitopes known to be associated with neutralizing antibodies, and demonstrated asymmetric blocking of EBOV GP from allosteric effectors reported from literature.

Conclusions

Critically, this workflow allows us to probe the epitope landscape of EBOV GP without any prior structural knowledge of the antigen or structural benchmark clones. Incorporating epitope binning on hundreds of antibodies during early stage antibody characterization ensures access to a library’s full epitope coverage, aids in the identification of high quality reagents within the library that recapitulate this diversity for use in other studies, and ultimately enables the rational development of therapeutic cocktails that take advantage of multiple mechanisms of action such as cooperative synergistic effects to enhance neutralization function and minimize the risk of mutagenic escape. The use of high-throughput epitope binning during new outbreaks such as the current COVID-19 pandemic is particularly useful in accelerating timelines due to the large amount of information gained in a single experiment.

Flow Cytometry-Based Epitope Binning Using Competitive Binding Profiles for the Characterization of Monoclonal Antibodies against Cellular and Soluble Protein Targets

A key step in the therapeutic antibody drug discovery process is early identification of diverse candidate molecules. Information comparing antibody binding epitopes can be used to classify antibodies within a large panel, guiding rational lead molecule selection. We describe a novel epitope binning method utilizing high-throughput flow cytometry (HTFC) that leverages cellular barcoding or spectrally distinct beads to multiplex samples to characterize antibodies raised against cell membrane receptor or soluble protein targets. With no requirement for sample purification or direct labeling, the method is suited for early characterization of antibody candidates. This method generates competitive binding profiles of each antibody against a defined set of known or unknown reference antibodies for binding to epitopes of an antigen. Antibodies with closely related competitive binding profiles indicate similar epitopes and are classified in the same bin. These large, high-throughput, multiplexed experiments can yield epitope bins or clusters for the entire antibody panel, from which a conceptual map of the epitope space for each antibody can be created. Combining this valuable epitope information with other data, such as functional activity, sequence, and selectivity of binding to orthologs and paralogs, enables us to advance the best epitope-diverse candidates for further development.

An Introduction to Epitope Mapping

Antibodies are protein molecules used routinely for therapeutic, diagnostic, and research purposes due to their exquisite ability to selectively recognize and bind a given antigen. The particular area of the antigen recognized by the antibody is called the epitope, and for proteinaceous antigens the epitope can be of complex nature. Information about the binding epitope of an antibody can provide important mechanistic insights and indicate for what applications an antibody might be useful. Therefore, a variety of epitope mapping techniques have been developed to localize such regions. Although the real picture is even more complex, epitopes in protein antigens are broadly grouped into linear or discontinuous epitopes depending on the positioning of the epitope residues in the antigen sequence and the requirement of structure. Specialized methods for mapping of the two different classes of epitopes, using high-throughput or high-resolution methods, have been developed. While different in their detail, all of the experimental methods rely on assessing the binding of the antibody to the antigen or a set of antigen mimics. Early approaches utilizing sets of truncated proteins, small numbers of synthesized peptides, and structural analyses of antibody-antigen complexes have been significantly refined. Current state-of-the-art methods involve combinations of mutational scanning, protein display, and high-throughput screening in conjunction with bioinformatic analyses of large datasets.

Kinetic Analysis and Epitope Binning Using Surface Plasmon Resonance

The ability to quantify binding affinity of molecular interactions is an essential component of drug development and life science research. This chapter outlines the practical use of surface plasmon resonance spectroscopy to monitor protein-protein interactions with an emphasis on basic experimental design. A short summary of epitope binning assays is also included.

Computationally-driven identification of antibody epitopes

Understanding where antibodies recognize antigens can help define mechanisms of action and provide insights into progression of immune responses. We investigate the extent to which information about binding specificity implicitly encoded in amino acid sequence can be leveraged to identify antibody epitopes. In computationally-driven epitope localization, possible antibody–antigen binding modes are modeled, and targeted panels of antigen variants are designed to experimentally test these hypotheses. Prospective application of this approach to two antibodies enabled epitope localization using five or fewer variants per antibody, or alternatively, a six-variant panel for both simultaneously. Retrospective analysis of a variety of antibodies and antigens demonstrated an almost 90% success rate with an average of three antigen variants, further supporting the observation that the combination of computational modeling and protein design can reveal key determinants of antibody–antigen binding and enable efficient studies of collections of antibodies identified from polyclonal samples or engineered libraries.

Rare, high-affinity mouse anti-PD-1 antibodies that function in checkpoint blockade, discovered using microfluidics and molecular genomics

Conventionally, mouse hybridomas or well-plate screening are used to identify therapeutic monoclonal antibody candidates. In this study, we present an alternative to hybridoma-based discovery that combines microfluidics, yeast single-chain variable fragment (scFv) display, and deep sequencing to rapidly interrogate and screen mouse antibody repertoires. We used our approach on six wild-type mice to identify 269 molecules that bind to programmed cell death protein 1 (PD-1), which were present at an average of 1 in 2,000 in the pre-sort scFv libraries. Two rounds of fluorescence-activated cell sorting (FACS) produced populations of PD-1-binding scFv with a mean enrichment of 800-fold, whereas most scFv present in the pre-sort mouse repertoires were de-enriched. Therefore, our work suggests that most of the antibodies present in the repertoires of immunized mice are not strong binders to PD-1. We observed clusters of related antibody sequences in each mouse following FACS, suggesting evolution of clonal lineages. In the pre-sort repertoires, these putative clonal lineages varied in both the complementary-determining region (CDR)3K and CDR3H, while the FACS-selected PD-1-binding subsets varied primarily in the CDR3H. PD-1 binders were generally not highly diverged from germline, showing 98% identity on average with germline V-genes. Some CDR3 sequences were discovered in more than one animal, even across different mouse strains, suggesting convergent evolution. We synthesized 17 of the anti-PD-1 binders as full-length monoclonal antibodies. All 17 full-length antibodies bound recombinant PD-1 with K_D < 500 nM (average = 62 nM). Fifteen of the 17 full-length antibodies specifically bound surface-expressed PD-1 in a FACS assay, and nine of the antibodies functioned as checkpoint inhibitors in a cellular assay. We conclude that our method is a viable alternative to hybridomas, with key advantages in comprehensiveness and turnaround time.

Global sensing of the antigenic structure of herpes simplex virus gD using high-throughput array-based SPR imaging

While HSV-2 typically causes genital lesions, HSV-1 is increasingly the cause of genital herpes. In addition, neonatal HSV infections are associated with a high rate of mortality and HSV-2 may increase the risk for HIV or Zika infections, reinforcing the need to develop an effective vaccine. In the GSK Herpevac trial, doubly sero-negative women were vaccinated with a truncated form of gD2 [gD2(284t)], then examined for anti-gD serum titers and clinical manifestations of disease. Surprisingly, few vaccinees were protected against genital HSV-2 but 86% were protected from genital HSV-1. These observations suggest that subtle differences in gD structure might influence a protective response. To better understand the antigenic structure of gD and how it impacts a protective response, we previously utilized several key anti-gD monoclonal antibodies (mAbs) to dissect epitopes in vaccinee sera. Several correlations were observed but the methodology limited the number of sera and mAbs that could be tested. Here, we used array-based surface plasmon imaging (SPRi) to simultaneously measure a larger number of protein-protein interactions. We carried out cross-competition or "epitope binning" studies with 39 anti-gD mAbs and four soluble forms of gD, including a form [gD2(285t)] that resembles the Herpevac antigen. The results from these experiments allowed us to organize the mAbs into four epitope communities. Notably, relationships within and between communities differed depending on the form of gD, and off-rate analysis suggested differences in mAb-gD avidity depending on the gD serotype and length. Together, these results show that gD1 and gD2 differ in their structural topography. Consistent with the Herpevac results, several mAbs that bind both gD1 and gD2 neutralize only HSV-1. Thus, this technology provides new insights into the antigenic structure of gD and provides a rationale as to how vaccination with a gD2 subunit may lead to protection from HSV-1 infection.

Comparison of surface plasmon resonance binding curves for characterization of protein interactions and analysis of screening data

Label-free technologies, such as surface plasmon resonance, are typically used for characterization of protein interactions and in screening for selection of antibodies or small molecules with preferred binding properties. In characterization, complete binding curves are normally fitted to defined interaction models to provide affinity and rate constants, whereas report points indicative of binding and stability of binding are often used for analysis of screening data. As an alternative to these procedures, here we describe how the analysis, in certain cases, can be simplified by comparison with upper and lower limit binding curves that represent expected or wanted binding profiles. The use of such profiles is applied to the analysis of kinetically complex IgG-Fc receptor interactions and for selection of antibody candidates. The comparison procedure described may be particularly useful in batch-to-batch comparisons and in comparability and biosimilar studies of biotherapeutic medicines. In screening, more informed selections may become possible as entire binding profiles and not a few report points are used in the analysis and as each new sample is directly compared with a predefined outcome.