Structural basis for antibody recognition of the proximal MUC16 ectodomain.

BACKGROUND: Mucin 16 (MUC16) overexpression is linked with cancer progression, metastasis, and therapy resistance in high grade serous ovarian cancer and other malignancies. The cleavage of MUC16 forms independent bimodular fragments, the shed tandem repeat sequence which circulates as a protein bearing the ovarian cancer biomarker (CA125) and a proximal membrane-bound component which is critical in MUC16 oncogenic behavior. A humanized, high affinity antibody targeting the proximal ectodomain represents a potential therapeutic agent against MUC16 with lower antigenic potential and restricted human tissue expression. RESULTS: Here, we demonstrate the potential therapeutic versatility of the humanized antibody as a monoclonal antibody, antibody drug conjugate, and chimeric antigen receptor. We report the crystal structures of 4H11-scFv, derived from an antibody specifically targeting the MUC16 C-terminal region, alone and in complex with a 26-amino acid MUC16 segment resolved at 2.36 Å and 2.47 Å resolution, respectively. The scFv forms a robust interaction with an epitope consisting of two consecutive ß-turns and a ß-hairpin stabilized by 2 hydrogen bonds. The VH-VL interface within the 4H11-scFv is stabilized through an intricate network of 11 hydrogen bonds and a cation-π interaction. CONCLUSIONS: Together, our studies offer insight into antibody-MUC16 ectodomain interaction and advance our ability to design agents with potentially improved therapeutic properties over anti-CA125 moiety antibodies.

Parameter estimation and identifiability analysis for a bivalent analyte model of monoclonal antibody-antigen binding.

Surface plasmon resonance (SPR) is an extensively used technique to characterize antigen-antibody interactions. Affinity measurements by SPR typically involve testing the binding of antigen in solution to monoclonal antibodies (mAbs) immobilized on a chip and fitting the kinetics data using 1:1 Langmuir binding model to derive rate constants. However, when it is necessary to immobilize antigens instead of the mAbs, a bivalent analyte (1:2) binding model is required for kinetics analysis. This model is lacking in data analysis packages associated with high throughput SPR instruments and the packages containing this model do not explore multiple local minima and parameter identifiability issues that are common in non-linear optimization. Therefore, we developed a method to use a system of ordinary differential equations for analyzing 1:2 binding kinetics data. Salient features of this method include a grid search on parameter initialization and a profile likelihood approach to determine parameter identifiability. Using this method we found a non-identifiable parameter in data set collected under the standard experimental design. A simulation-guided improved experimental design led to reliable estimation of all rate constants. The method and approach developed here for analyzing 1:2 binding kinetics data will be valuable for expeditious therapeutic antibody discovery research.

Microwave resonator-based sensor system for specific antibody detection.

An antibody-detecting sensor is described that is based on a microwave electrodynamic resonator. A polystyrene film with immobilized bacteria deposited on a lithium niobate plate was placed at one end of the resonator and was used as the sensing element. The second end was electrically shorted. The frequency and depth of the reflection coefficient S11 for three resonances in the range 6.5-8.5 GHz were used as an analytical signal to examine antibody interactions with bacteria and determine the time required for cell immobilization. The sensor distinguished between situations in which bacteria interacted with specific antibodies and those in which no such interaction occurred (control). Although the cell-antibody interaction changed the frequency and depth of the second and third resonance peaks, the parameters of the first resonance peak did not change. The interaction of cells with nonspecific antibodies did not change the parameters of any of the peaks. These results are promising for use in the design of methods to detect specific antibodies, which can supplement the existing methods of antibody analysis.

Attogram-level light-induced antigen-antibody binding confined in microflow.

The analysis of trace amounts of proteins based on immunoassays and other methods is essential for the early diagnosis of various diseases such as cancer, dementia, and microbial infections. Here, we propose a light-induced acceleration of antigen-antibody reaction of attogram-level proteins at the solid-liquid interface by tuning the laser irradiation area comparable to the microscale confinement geometry for enhancing the collisional probability of target molecules and probe particles with optical force and fluidic pressure. This principle was applied to achieve a 102-fold higher sensitivity and ultrafast specific detection in comparison with conventional protein detection methods (a few hours) by omitting any pretreatment procedures; 47-750 ag of target proteins were detected in 300 nL of sample after 3 minutes of laser irradiation. Our findings can promote the development of proteomics and innovative platforms for high-throughput bio-analyses under the control of a variety of biochemical reactions.

Dual Gold-Nanoslit Electrodes for Ultrasensitive Detection of Antigen-Antibody Reactions in Electrochemical Surface Plasmon Resonance.

We present the use of surface charges in dual gold-nanoslit electrodes to improve the surface plasmon resonance (SPR) detection limit by several orders of magnitude. The SPR is directly generated by gold-nanoslit arrays in the two electrodes. The SPR shifts for both nanoslit arrays are measured simultaneously with a simple hyperspectral setup. When biomolecules are captured by specific antibodies on the dual electrodes, the surface charge is changed during the electrochemical process due to the increase in surface impedance. The push-pull-type electrodes generate opposite surface charges. Using the differences in both spectral shifts, the change in surface charge is detected sensitively. We demonstrate that using a [Fe(CN)6]3-/4- redox process after antigen-antibody interactions, the dual nanoslit electrodes show an enhancement of the detection limit from 1 µg/mL to 10 pg/mL.

Quantifying Antibody Binding Kinetics on Fixed Cells and Tissues via Fluorescence Lifetime Imaging.

We present a method for monitoring spatially localized antigen-antibody binding events on physiologically relevant substrates (cell and tissue sections) using fluorescence lifetime imaging. Specifically, we use the difference between the fluorescence decay times of fluorescently tagged antibodies in free solution and in the bound state to track the bound fraction over time and hence deduce the binding kinetics. We make use of a microfluidic probe format to minimize the mass transport effects and localize the analysis to specific regions of interest on the biological substrates. This enables measurement of binding constants (kon) on surface-bound antigens and on cell blocks using model biomarkers. Finally, we directly measure p53 kinetics with differential biomarker expression in ovarian cancer tissue sections, observing that the degree of expression corresponds to the changes in kon, with values of 3.27-3.50 × 103 M-1 s-1 for high biomarker expression and 2.27-2.79 × 103 M-1 s-1 for low biomarker expression.

In silico proof of principle of machine learning-based antibody design at unconstrained scale.

Generative machine learning (ML) has been postulated to become a major driver in the computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to confirm this hypothesis have been hindered by the infeasibility of testing arbitrarily large numbers of antibody sequences for their most critical design parameters: paratope, epitope, affinity, and developability. To address this challenge, we leveraged a lattice-based antibody-antigen binding simulation framework, which incorporates a wide range of physiological antibody-binding parameters. The simulation framework enables the computation of synthetic antibody-antigen 3D-structures, and it functions as an oracle for unrestricted prospective evaluation and benchmarking of antibody design parameters of ML-generated antibody sequences. We found that a deep generative model, trained exclusively on antibody sequence (one dimensional: 1D) data can be used to design conformational (three dimensional: 3D) epitope-specific antibodies, matching, or exceeding the training dataset in affinity and developability parameter value variety. Furthermore, we established a lower threshold of sequence diversity necessary for high-accuracy generative antibody ML and demonstrated that this lower threshold also holds on experimental real-world data. Finally, we show that transfer learning enables the generation of high-affinity antibody sequences from low-N training data. Our work establishes a priori feasibility and the theoretical foundation of high-throughput ML-based mAb design.

[The Basic Research of Antigen-antibody Reaction in Lung Transplantation].

Lung transplantation has become popular in Japan, showing better survival rate than other countries. However, the results are still not satisfactory compared with other solid organ transplantation. One of the reasons for this might be that knowledge on donor-specific antibodies or antibody-related rejection, which has been attracting attention these days, is less than that of kidney or liver transplantation. Our laboratory has continued basic research in this field using rodent lung transplantation model. We have previously shown that type V collagen is associated in chronic rejection as an autoimmune, and that oral administration of type V collagen induces tolerance. The murine chronic rejection model of the minor antigen mismatch was developed, and involvement of the humoral immunity and role of the complement activation were shown. We are now studying the effects of immune checkpoint molecules, which play a central role in the field of cancer therapy, on rejection after lung transplantation. We are also working to verify the effects of anti-complement drugs and molecular targeted drugs in the future treatment on rejection.

First-in-class trispecific VHH-Fc based antibody with potent prophylactic and therapeutic efficacy against SARS-CoV-2 and variants.

SARS-CoV-2 and its variants have persisted in this ongoing COVID-19 pandemic. While the vaccines have greatly reduced the COVID-19 cases, hospitalizations, and death, about half of the world remain unvaccinated due to various reasons. Furthermore, the duration of the immunity gained from COVID-19 vaccination is still unclear. Therefore, there is a need for innovative prophylactic and treatment measures. In response to this need, we previously reported on the successful computer-aided development of potent VHH-based multispecific antibodies that were characterized in vitro. Here, we evaluated in vivo efficacy and safety of the lead trispecific VHH-Fc, ABS-VIR-001. Importantly, our data showed that ABS-VIR-001 treatment prevented SARS-CoV-2 infection and death when provided as an intranasal prophylaxis in a humanized ACE-2 mouse model. In addition, ABS-VIR-001 post-exposure treatment was shown to greatly reduce viral loads by as much as 50-fold. A detailed panel of metabolic and cellular parameters demonstrated that ABS-VIR-001 treatment was overall comparable to the PBS treatment, indicating a favorable safety profile. Notably, our inhibition studies show that ABS-VIR-001 continued to demonstrate unwavering efficacy against SARS-CoV-2 mutants, associated with key variants including Delta and Omicron, owing to its multiple epitope design. Lastly, we rigorously tested and confirmed the excellent thermostability of ABS-VIR-001 when heated to 45 °C for up to 4 weeks. Taken together, our study suggests that ABS-VIR-001 is an efficacious and durable prophylaxis and post-exposure treatment for COVID-19 with promising safety and manufacturability features for global distribution.

Optothermophoretic flipping method for biomolecule interaction enhancement.

The widely used surface-based biomolecule sensing scheme has greatly facilitated the investigation of protein-protein interactions in lab-on-a-chip microfluidic systems. However, in most biosensing schemes, the interactions are driven in a passive way: The overall sensing time and sensitivity are totally dependent on the Brownian diffusion process, which has greatly hindered their efficiency, especially at low concentration levels or single-molecule analysis. To break this limitation, we developed an all-optical active method termed optothermophoretic flipping (OTF). It is the first temporal modulated method that biomolecules were enriched and pushed to their counterparts for effective contact via a flipped thermophoresis. As a proof-of-concept experiment, we tested its performance via antibody-antigen binding on a surface plasmon resonance imaging (SPRi) platform. Compared with the interaction solely based on Brownian diffusion, we achieved a 23.6-fold sensitivity increment in biomolecule interactions sensing. This method has opened new opportunities for various biosensing platforms that require high-sensitivity in colloidal sciences and biochemical studies.

Escherichia coli recombinant expression of SARS-CoV-2 protein fragments.

We have developed a method for the inexpensive, high-level expression of antigenic protein fragments of SARS-CoV-2 proteins in Escherichia coli. Our approach uses the thermophilic family 9 carbohydrate-binding module (CBM9) as an N-terminal carrier protein and affinity tag. The CBM9 module was joined to SARS-CoV-2 protein fragments via a flexible proline-threonine linker, which proved to be resistant to E. coli proteases. Two CBM9-spike protein fragment fusion proteins and one CBM9-nucleocapsid fragment fusion protein largely resisted protease degradation, while most of the CBM9 fusion proteins were degraded at some site in the SARS-CoV-2 protein fragment. All of the fusion proteins were highly expressed in E. coli and the CBM9-ID-H1 fusion protein was shown to yield 122 mg/L of purified product. Three purified CBM9-SARS-CoV-2 fusion proteins were tested and found to bind antibodies directed to the appropriate SARS-CoV-2 antigenic regions. The largest intact CBM9 fusion protein, CBM9-ID-H1, incorporates spike protein amino acids 540-588, which is a conserved region overlapping and C-terminal to the receptor binding domain that is widely recognized by human convalescent sera and contains a putative protective epitope.

Analysis of the Neutralizing Activity of Antibodies Targeting Open or Closed SARS-CoV-2 Spike Protein Conformations.

SARS-CoV-2 infection elicits a polyclonal neutralizing antibody (nAb) response that primarily targets the spike protein, but it is still unclear which nAbs are immunodominant and what distinguishes them from subdominant nAbs. This information would however be crucial to predict the evolutionary trajectory of the virus and design future vaccines. To shed light on this issue, we gathered 83 structures of nAbs in complex with spike protein domains. We analyzed in silico the ability of these nAbs to bind the full spike protein trimer in open and closed conformations, and predicted the change in binding affinity of the most frequently observed spike protein variants in the circulating strains. This led us to define four nAb classes with distinct variant escape fractions. By comparing these fractions with those measured from plasma of infected patients, we showed that the class of nAbs that most contributes to the immune response is able to bind the spike protein in its closed conformation. Although this class of nAbs only partially inhibits the spike protein binding to the host's angiotensin converting enzyme 2 (ACE2), it has been suggested to lock the closed pre-fusion spike protein conformation and therefore prevent its transition to an open state. Furthermore, comparison of our predictions with mRNA-1273 vaccinated patient plasma measurements suggests that spike proteins contained in vaccines elicit a different nAb class than the one elicited by natural SARS-CoV-2 infection and suggests the design of highly stable closed-form spike proteins as next-generation vaccine immunogens.

Computational design of a neutralizing antibody with picomolar binding affinity for all concerning SARS-CoV-2 variants.

Coronavirus disease 2019, caused by SARS-CoV-2, remains an on-going pandemic, partly due to the emergence of variant viruses that can "break-through" the protection of the current vaccines and neutralizing antibodies (nAbs), highlighting the needs for broadly nAbs and next-generation vaccines. We report an antibody that exhibits breadth and potency in binding the receptor-binding domain (RBD) of the virus spike glycoprotein across SARS coronaviruses. Initially, a lead antibody was computationally discovered and crystallographically validated that binds to a highly conserved surface of the RBD of wild-type SARS-CoV-2. Subsequently, through experimental affinity enhancement and computational affinity maturation, it was further developed to bind the RBD of all concerning SARS-CoV-2 variants, SARS-CoV-1 and pangolin coronavirus with pico-molar binding affinities, consistently exhibited strong neutralization activity against wild-type SARS-CoV-2 and the Alpha and Delta variants. These results identify a vulnerable target site on coronaviruses for development of pan-sarbecovirus nAbs and vaccines.

A Multifunctional Neutralizing Antibody-Conjugated Nanoparticle Inhibits and Inactivates SARS-CoV-2.

The outbreak of 2019 coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in a global pandemic. Despite intensive research, the current treatment options show limited curative efficacies. Here the authors report a strategy incorporating neutralizing antibodies conjugated to the surface of a photothermal nanoparticle (NP) to capture and inactivate SARS-CoV-2. The NP is comprised of a semiconducting polymer core and a biocompatible polyethylene glycol surface decorated with high-affinity neutralizing antibodies. The multifunctional NP efficiently captures SARS-CoV-2 pseudovirions and completely blocks viral infection to host cells in vitro through the surface neutralizing antibodies. In addition to virus capture and blocking function, the NP also possesses photothermal function to generate heat following irradiation for inactivation of virus. Importantly, the NPs described herein significantly outperform neutralizing antibodies at treating authentic SARS-CoV-2 infection in vivo. This multifunctional NP provides a flexible platform that can be readily adapted to other SARS-CoV-2 antibodies and extended to novel therapeutic proteins, thus it is expected to provide a broad range of protection against original SARS-CoV-2 and its variants.

Unconstrained generation of synthetic antibody-antigen structures to guide machine learning methodology for antibody specificity prediction.

Machine learning (ML) is a key technology for accurate prediction of antibody-antigen binding. Two orthogonal problems hinder the application of ML to antibody-specificity prediction and the benchmarking thereof: the lack of a unified ML formalization of immunological antibody-specificity prediction problems and the unavailability of large-scale synthetic datasets to benchmark real-world relevant ML methods and dataset design. Here we developed the Absolut! software suite that enables parameter-based unconstrained generation of synthetic lattice-based three-dimensional antibody-antigen-binding structures with ground-truth access to conformational paratope, epitope and affinity. We formalized common immunological antibody-specificity prediction problems as ML tasks and confirmed that for both sequence- and structure-based tasks, accuracy-based rankings of ML methods trained on experimental data hold for ML methods trained on Absolut!-generated data. The Absolut! framework has the potential to enable real-world relevant development and benchmarking of ML strategies for biotherapeutics design.

Epitope profiling using computational structural modelling demonstrated on coronavirus-binding antibodies.

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.

Monoclonal Antibodies against Nucleocapsid Protein of SARS-CoV-2 Variants for Detection of COVID-19.

Mitigation strategies of the coronavirus disease 2019 (COVID-19) pandemic have been greatly hindered by the continuous emergence of SARS-CoV-2 variants. New sensitive, rapid diagnostic tests for the wide-spectrum detection of viral variants are needed. We generated a panel of 41 monoclonal antibodies against the SARS-CoV-2 nucleocapsid protein (NP) by using mice hybridoma techniques. Of these mAbs, nine exhibited high binding activities and were applied in latex-based lateral flow immunoassays (LFIAs). The LFIAs utilizing NP-mAb-7 and -40 had the best sensitivity and lowest limit of detection: 8 pg for purified NP and 625 TCID50/mL for the authentic virus (hCoV-19/Taiwan/4/2020). The specificity tests showed that the NP-mAb-40/7 LFIA strips did not cross-react with five human coronavirus strains or 20 other common respiratory pathogens. Importantly, we found that 10 NP mutants, including alpha (B.1.1.7), beta (B.1.351), gamma (P.1), and delta (B.1.617.2) variants, could be detected by NP-mAb-40/7 LFIA strips. A clinical study (n = 60) of the NP-mAb-40/7 LFIA strips demonstrated a specificity of 100% and sensitivity of 90% in infected individuals with cycle threshold (Ct) values < 29.5. These anti-NP mAbs have strong potential for use in the clinical detection of SARS-CoV-2 infection, whether the virus is wild-type or a variant of concern.

Key role of a structural water molecule for the specificity of 14F7-An antitumor antibody targeting the NeuGc GM3 ganglioside.

Tumor-associated glycolipids such as NeuGc GM3 are auspicious molecular targets in antineoplastic therapies and vaccine strategies. 14F7 is a monoclonal IgG1 with high clinical potential in cancer immunotherapy as it displays extraordinary specificity for NeuGc GM3, while it does not recognize the very similar, ubiquitous NeuAc GM3. Here we present the 2.3 Å crystal structure of the 14F7 antigen-binding domain (14F7 scFv) in complex with the NeuGc GM3 trisaccharide. Modeling analysis and previous mutagenesis data suggest that 14F7 may also bind to an alternative NeuGc GM3 conformation, not observed in the crystal structure. The most intriguing finding, however, was that a water molecule centrally placed in the complementarity-determining region directly mediates the specificity of 14F7 to NeuGc GM3. This has profound impact on the complexity of engineering in the binding site and provides an excellent example of the importance in understanding the water structure in antibody-antigen interactions.