Antibody Variable Region Research Articles

A multispecific antibody against SARS-CoV-2 prevents immune escape in vitro and confers prophylactic protection in vivo.

Despite effective countermeasures, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) persists worldwide because of its ability to diversify and evade human immunity. This evasion stems from amino acid substitutions, particularly in the receptor binding domain (RBD) of the spike protein that confers resistance to vaccine-induced antibodies and antibody therapeutics. To constrain viral escape through resistance mutations, we combined antibody variable regions that recognize different RBD sites into multispecific antibodies. Here, we describe multispecific antibodies, including a trivalent trispecific antibody that potently neutralized diverse SARS-CoV-2 variants and prevented virus escape more effectively than single antibodies or mixtures of the parental antibodies. Despite being generated before the appearance of Omicron, this trispecific antibody neutralized all major Omicron variants through BA.4/BA.5 at nanomolar concentrations. Negative stain electron microscopy suggested that synergistic neutralization was achieved by engaging different epitopes in specific orientations that facilitated binding across more than one spike protein. Moreover, a tetravalent trispecific antibody containing the same variable regions as the trivalent trispecific antibody also protected Syrian hamsters against Omicron variants BA.1, BA.2, and BA.5 challenge, each of which uses different amino acid substitutions to mediate escape from therapeutic antibodies. These results demonstrated that multispecific antibodies have the potential to provide broad SARS-CoV-2 coverage, decrease the likelihood of escape, simplify treatment, and provide a strategy for antibody therapies that could help eliminate pandemic spread for this and other pathogens.

Functional characterization of a nanobody-based glycoprotein VI-specific platelet agonist

BackgroundGlycoprotein (GP)VI is a platelet-specific collagen receptor required for platelet activation during hemostasis. Platelet reactivity toward collagen is routinely assessed during diagnostic workup of platelet disorders. GPVI can be activated by inducing receptor clustering with suspensions of fibrillar collagen or synthetic cross-linked collagen-related peptide (CRP-XL). However, these suspensions are poorly standardized or difficult to produce. Nanobodies are small recombinant camelid-derived heavy-chain antibody variable regions. They are highly stable, specific, and ideal candidates for developing a stable GPVI agonist for diagnostic assays. ObjectivesDevelop a stable nanobody-based GPVI agonist. MethodsNanobody D2 (NbD2) was produced as dimers and purified. Tetramers were generated via C-terminal fusion of dimers with click chemistry. Nanobody constructs were functionally characterized with light transmission aggregometry (LTA) in platelet-rich plasma and whole blood flow cytometry. Diagnostic performance was assessed in patients with inherited platelet function disorders with LTA and flow cytometry. ResultsNbD2 was specific for human platelet GPVI. Dimers did not result in platelet activation in LTA or flow cytometry settings and fully inhibited CRP-XL-induced P-selectin expression and fibrinogen binding in whole blood and attenuated collagen-induced platelet aggregation in platelet-rich plasma. However, NbD2 tetramers caused full platelet aggregation, as well as P-selectin expression and fibrinogen binding. NbD2 tetramers were able to discriminate between inherited platelet function disorder patients and healthy controls based on fibrinogen binding, similar to CRP-XL. ConclusionNanobody tetramers to GPVI induce platelet activation and can be used to assess the GPVI pathway in diagnostic assays.

DeepSP: Deep learning-based spatial properties to predict monoclonal antibody stability

Therapeutic antibody development faces challenges due to high viscosities and aggregation tendencies. The spatial charge map (SCM) and spatial aggregation propensity (SAP) are computational techniques that aid in predicting viscosity and aggregation, respectively. These methods rely on structural data derived from molecular dynamics (MD) simulations, which are computationally demanding. DeepSCM, a deep learning surrogate model based on sequence information to predict SCM, was recently developed to screen high-concentration antibody viscosity. This study further utilized a dataset of 20,530 antibody sequences to train a convolutional neural network deep learning surrogate model called Deep Spatial Properties (DeepSP). DeepSP directly predicts SAP and SCM scores in different domains of antibody variable regions based solely on their sequences without performing MD simulations. The linear correlation coefficient between DeepSP scores and MD-derived scores for 30 properties achieved values between 0.76 and 0.96 with an average of 0.87. DeepSP descriptors were employed as features to build machine learning models to predict the aggregation rate of 21 antibodies, and the performance is similar to the results obtained from the previous study using MD simulations. This result demonstrates that the DeepSP approach significantly reduces the computational time required compared to MD simulations. The DeepSP model enables the rapid generation of 30 structural properties that can also be used as features in other research to train machine learning models for predicting various antibody stability using sequences only. DeepSP is freely available as an online tool via https://deepspwebapp.onrender.com and the codes and parameters are freely available at https://github.com/Lailabcode/DeepSP.

Identification of an IGHV3-53-Encoded RBD-Targeting Cross-Neutralizing Antibody from an Early COVID-19 Convalescent.

Since November 2021, Omicron has emerged as the dominant severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant, and its sublineages continue to appear one after another, significantly reducing the effectiveness of existing therapeutic neutralizing antibodies (NAbs). It is urgent to develop effective NAbs against circulating Omicron variants. Here, we isolated receptor binding domain (RBD)-specific single memory B cells via flow cytometry from a COVID-19 convalescent. The antibody variable region genes of the heavy chain (VHs) and light chain (VLs) were amplified and cloned into expression vectors. After antibody expression, ELISA screening and neutralizing activity detection, we obtained an IGHV3-53-encoded RBD-targeting cross-neutralizing antibody D6, whose VL originated from the IGKV1-9*01 germlines. D6 could potently neutralize circulating Omicron variants (BA.1, BA.2, BA.4/5 and BF.7), with IC50 values of less than 0.04 μg/mL, and the neutralizing ability against XBB was reduced but still effective. The KD values of D6 binding with RBD of the prototype and BA.1 were both less than 1.0 × 10-12 M. The protein structure of the D6-RBD model indicates that D6 interacts with the RBD external subdomain and belongs to the RBD-1 community. The sufficient contact and deep interaction of D6 HCDR3 and LCDR3 with RBD may be the crucial reason for its cross-neutralizing activity. The sorting and analysis of mAb D6 will provide important information for the development of anti-COVID-19 reagents.

AbImmPred: An immunogenicity prediction method for therapeutic antibodies using AntiBERTy-based sequence features.

Due to the unnecessary immune responses induced by therapeutic antibodies in clinical applications, immunogenicity is an important factor to be considered in the development of antibody therapeutics. To a certain extent, there is a lag in using wet-lab experiments to test the immunogenicity in the development process of antibody therapeutics. Developing a computational method to predict the immunogenicity at once the antibody sequence is designed, is of great significance for the screening in the early stage and reducing the risk of antibody therapeutics development. In this study, a computational immunogenicity prediction method was proposed on the basis of AntiBERTy-based features of amino sequences in the antibody variable region. The AntiBERTy-based sequence features were first calculated using the AntiBERTy pre-trained model. Principal component analysis (PCA) was then applied to reduce the extracted feature to two dimensions to obtain the final features. AutoGluon was then used to train multiple machine learning models and the best one, the weighted ensemble model, was obtained through 5-fold cross-validation on the collected data. The data contains 199 commercial therapeutic antibodies, of which 177 samples were used for model training and 5-fold cross-validation, and the remaining 22 samples were used as an independent test dataset to evaluate the performance of the constructed model and compare it with other prediction methods. Test results show that the proposed method outperforms the comparison method with 0.7273 accuracy on the independent test dataset, which is 9.09% higher than the comparison method. The corresponding web server is available through the official website of GenScript Co., Ltd., https://www.genscript.com/tools/antibody-immunogenicity.

Detection of calcitonin gene-related peptide based on increased antigen-driven interaction with antibody variable regions.

Introduction: Calcitonin gene-related peptide (CGRP) is involved in trigeminal neuralgia and migraine, and measuring the CGRP concentration in the serum is crucial for the early prediction of these conditions. Current methods for CGRP detection are primarily radioimmunoassay, which needs radioactive substances and enzyme-linked immunosorbent assays (ELISAs) which need long detection time and some have a narrow detection range. Methods: The genes of anti-CGRP antibody variable regions were cloned into pDong1 vector to obtain pDong1/Fab-CGRP, with which phage-Fab was prepared, and the concentration of CGRP was detected by competitive ELISA. The pDong1/Fab-CGRP was modified to obtain pDong1/OS-CGRP, with which the co-expression solution containing phage-displayed heavy chain variable fragments (phage-VH) and light chain was obtained. CGRP was detected by OS-ELISA based on phage-VH, antibody light chain, and anti-light chain antibody. The VL gene was cloned into the pMAL vector to obtain pMAL-VL (CGRP), with which maltose binding protein fused with VL (MBP-VL) was prepared. CGRP was detected by OS-ELISA employing MBP-VL and phage-VH. Results: OS-ELISAs that measure the CGRP concentration by quantifying the interaction between variable regions were investigated. OS-ELISA using phage-VH and secreted light chains in the same culture system exhibited a limit of detection (LOD) of 0.05nM, offering higher sensitivity than competitive assay with an LOD of 0.75nM, whereas using phage-VH and separately prepared MBP-VL exhibited an LOD of 0.15nM and a broader detection range of 0.15-500nM than competitive ELISA, whose detection range was 0.75-10nM. Discussion: The combination of the two OS assays achieved high sensitivity and a broad detection range for CGRP, which may have significance in clinical applications.

Phage display assisted discovery of a pH-dependent anti-α-cobratoxin antibody from a natural variable domain library.

RecyclingIgG antibodies bind to their target antigen at physiological pH in the blood stream and release them upon endocytosis when pH levels drop, allowing the IgG antibodies to be recycled into circulation via FcRn-mediated cellular pathways, while the antigens undergo lysosomal degradation. This enables recycling antibodies to achieve comparable therapeutic effect at lower doses than their non-recycling counterparts. The development of such antibodies is typically achieved by histidine doping of their variable regions or by performing in vitro antibody selection campaigns utilizing histidine doped libraries. Both are strategies that may introduce sequence liabilities. Here, we present a methodology that employs a naïve antibody phage display library, consisting of natural variable domains, to discover antibodies that bind α-cobratoxin from the venom of Naja kaouthia in a pH-dependent manner. As a result, an antibody was discovered that exhibits a 7-fold higher off-rate at pH 5.5 than pH 7.4 in bio-layer interferometry experiments. Interestingly, no histidine residues were found in its variable domains, and in addition, the antibody showed pH-dependent binding to a histidine-devoid antigen mutant. As such, the results demonstrate that pH-dependent antigen-antibody binding may not always be driven by histidine residues. By employing molecular dynamics simulations, different protonation states of titratable residues were found, which potentially could be responsible for the observed pH-dependent antigen binding properties of the antibody. Finally, given the typically high diversity of naïve antibody libraries, the methodology presented here can likely be applied to discover recycling antibodies against different targets ab initio without the need for histidine doping.

Antibody sequence-based prediction of pH gradient elution in multimodal chromatography

Multimodal chromatography has emerged as a promising technique for antibody purification, owing to its capacity to selectively capture and separate target molecules. However, the optimization of chromatography parameters remains a challenge due to the intricate nature of protein-ligand interactions. To tackle this issue, efficient predictive tools are essential for the development and optimization of multimodal chromatography processes. In this study, we introduce a methodology that predicts the elution behavior of antibodies in multimodal chromatography based on their amino acid sequences. We analyzed a total of 64 full-length antibodies, including IgG1, IgG4, and IgG-like multispecific formats, which were eluted using linear pH gradients from pH 9.0 to 4.0 on the anionic mixed-mode resin Capto adhere. Homology models were constructed, and 1312 antibody-specific physicochemical descriptors were calculated for each molecule. Our analysis identified six key structural features of the multimodal antibody interaction, which were correlated with the elution behavior, emphasizing the antibody variable region. The results show that our methodology can predict pH gradient elution for a diverse range of antibodies and antibody formats, with a test set R² of 0.898. The developed model can inform process development by predicting initial conditions for multimodal elution, thereby reducing trial and error during process optimization. Furthermore, the model holds the potential to enable an in silico manufacturability assessment by screening target antibodies that adhere to standardized purification conditions. In conclusion, this study highlights the feasibility of using structure-based prediction to enhance antibody purification in the biopharmaceutical industry. This approach can lead to more efficient and cost-effective process development while increasing process understanding.

Development of a 1:1-binding biparatopic anti-TNFR2 antagonist by reducing signaling activity through epitope selection

Conventional bivalent antibodies against cell surface receptors often initiate unwanted signal transduction by crosslinking two antigen molecules. Biparatopic antibodies (BpAbs) bind to two different epitopes on the same antigen, thus altering crosslinking ability. In this study, we develop BpAbs against tumor necrosis factor receptor 2 (TNFR2), which is an attractive immune checkpoint target. Using different pairs of antibody variable regions specific to topographically distinct TNFR2 epitopes, we successfully regulate the size of BpAb–TNFR2 immunocomplexes to result in controlled agonistic activities. Our series of results indicate that the relative positions of the two epitopes recognized by the BpAb are critical for controlling its signaling activity. One particular antagonist, Bp109-92, binds TNFR2 in a 1:1 manner without unwanted signal transduction, and its structural basis is determined using cryo-electron microscopy. This antagonist suppresses the proliferation of regulatory T cells expressing TNFR2. Therefore, the BpAb format would be useful in designing specific and distinct antibody functions.

Structural modeling of antibody variable regions using deep learning-progress and perspectives on drug discovery.

AlphaFold2 has hallmarked a generational improvement in protein structure prediction. In particular, advances in antibody structure prediction have provided a highly translatable impact on drug discovery. Though AlphaFold2 laid the groundwork for all proteins, antibody-specific applications require adjustments tailored to these molecules, which has resulted in a handful of deep learning antibody structure predictors. Herein, we review the recent advances in antibody structure prediction and relate them to their role in advancing biologics discovery.

The influence of variable-heavy chain families on IgG2, 3, 4, FcγRs and B-cell superantigens protein G and L binding using biolayer interferometry.

As the most abundant immunoglobulin in blood and the most common human isotype used for therapeutic monoclonal antibodies, the engagement and activation of its Fc receptors by IgGs are crucial for antibody function. Assumed to be relatively constant within subtypes, recent studies reveal that antibody variable regions exert distal effects of modulating antibody-receptor interactions on antibody isotypes. These variable (V)-region distal effects are also expected for the IgG subtypes. With an in-depth understanding of the V-region effects, researchers can make a more informed antibody engineering approach and antibody purification strategy accounting for the functions of microbial immune evasion . In this study, we created a panel of IgG2/IgG3/IgG4 antibodies by changing the VH family (VH1-7) frameworks while retaining the complementary determining regions of pertumuzab and measured their interactions with FcγRIa, FcγRIIaH167, FcγRIIaR167, FcγRIIb/c, FcγRIIIaF176, FcγRIIIaV176, FcγRIIIbNA1 and FcγRIIIbNA2 receptors alongside B-cell superantigens Protein L and G using biolayer interferometry. The panel of 21 IgGs demonstrated that the VH frameworks influenced receptor binding sites on the constant region in a non-canonical manner. However, there was minimal influence on the binding of bacterial B-cell superantigens Proteins L and Protein G on the IgGs, showing their robustness against V-region effects. These results demonstrate the role of V-regions during the humanization of therapeutic antibodies that can influence FcR-dependent immune responses while retaining binding by bacterial B-cell superantigens for antibody purification. These in vitro measurements provide a clue to detailed antibody engineering and understanding of antibody superantigen functions that would be relevant with in vivo validation.

An oncolytic herpes simplex virus type 1 strain expressing a single-chain variable region antibody fragment against PD-1 and a PI3K inhibitor synergize to elicit antitumor immunity in ovarian cancer

Due to recurrence and resistance to chemotherapy, the current standard therapeutics are not fully effective against ovarian cancer. Therefore, we aimed to find an effective approach to improve the prognosis and therapy of ovarian cancer. NG34ScFvPD-1 is a modified oncolytic herpes simplex virus NG34 strain that expresses a single-chain antibody against programmed cell death protein 1 (PD-1) (ScFvPD-1). We assessed its efficacy and its regulatory mechanism in a mouse model of ovarian cancer. Enzyme-linked immunosorbent assay and western blot techniques were used to measure protein expression. Oncolysis caused by NG34ScFvPD-1 was examined using cytotoxicity and replication assays. The mechanism by which NG34ScFvPD-1 regulates apoptosis of ovarian cancer cells in vitro was also evaluated. We assessed the antitumor immunity and therapeutic potency of NG34ScFvPD-1 in combination with a phosphoinositide 3-kinase (PI3K) inhibitor. We found that NG34ScFvPD-1-infected ovarian cancer cells expressed and secreted ScFvPD-1, which bound mouse PD-1. The insertion of the ScFvPD-1 sequence did not inhibit the oncolytic activity of NG34ScFvPD-1, which induced apoptosis of ovarian cancer cells via the caspase-dependent pathway in vitro and activated the PI3K/AKT signaling pathway. Synergy was observed between NG34ScFvPD-1 and a PI3K inhibitor, and the combination was able to suppress tumor development, to prolong survival, and to elicit potent antitumor immunity. Thus, inhibition of PI3K enhanced the potent antitumor immunity induced by NG34ScFvPD-1 against ovarian cancer.

Novel method to specifically determine the structures of non-N297 glycans in IgGs.

Monoclonal antibodies comprise a major class of biologic therapeutics and are also extensively studied in immunology. Given the importance of glycans on antibodies, fluorescent labeling of enzymatically released glycans and their LC/MS analysis is routinely used for in-depth characterization of antibody glycosylation. In this technical note, we propose a method for facile characterization of glycans in the variable region of antibodies using sequential enzymatic digests with Endoglycosidase-S2 and RapidTM Peptide-N-Glycosidase-F followed by labeling with a fluorescent dye carrying an NHS-carbamate moiety. The results and proposed mechanism also suggest that the choice of glycosidases along with the labeling chemistry is critical for accurate glycan analysis for a desired application.

Low-data interpretable deep learning prediction of antibody viscosity using a biophysically meaningful representation

Deep learning, aided by the availability of big data sets, has led to substantial advances across many disciplines. However, many scientific problems of practical interest lack sufficiently large datasets amenable to deep learning. Prediction of antibody viscosity is one such problem where deep learning methods have not yet been explored due to the relative scarcity of relevant training data. In this work, we overcome this limitation using a biophysically meaningful representation that enables us to develop generalizable models even under limited training data. We present, PfAbNet-viscosity, a 3D convolutional neural network architecture, to predict high-concentration viscosity of therapeutic antibodies. We show that with the electrostatic potential surface of the antibody variable region as the only input to the network, the models trained on as few as couple dozen datapoints can generalize with high accuracy. Our feature attribution analysis shows that PfAbNet-viscosity has learned key biophysical drivers of viscosity. The applicability of our approach to other biological systems is discussed.

A SARS-CoV-2 neutralizing antibody discovery by single cell sequencing and molecular modeling.

Although vaccines have been developed, mutations of SARS-CoV-2, especially the dominant B.1.617.2 (delta) and B.1.529 (omicron) strains with more than 30 mutations on their spike protein, have caused a significant decline in prophylaxis, calling for the need for drug improvement. Antibodies are drugs preferentially used in infectious diseases and are easy to get from immunized organisms. The current study combined molecular modeling and single memory B cell sequencing to assess candidate sequences before experiments, providing a strategy for the fabrication of SARS-CoV-2 neutralizing antibodies. A total of 128 sequences were obtained after sequencing 196 memory B cells, and 42 sequences were left after merging extremely similar ones and discarding incomplete ones, followed by homology modeling of the antibody variable region. Thirteen candidate sequences were expressed, of which three were tested positive for receptor binding domain recognition but only one was confirmed as having broad neutralization against several SARS-CoV-2 variants. The current study successfully obtained a SARS-CoV-2 antibody with broad neutralizing abilities and provided a strategy for antibody development in emerging infectious diseases using single memory B cell BCR sequencing and computer assistance in antibody fabrication.

Homology Modeling of Antibody Variable Regions: Methods and Applications.

Adaptive immunity specifically protects us from antigenic challenges. Antibodies are key effector proteins of adaptive immunity, and they are remarkable in their ability to recognize a virtually limitless number of antigens. Fragment variable (FV), the antigen-binding region of antibodies, can be split into two main components, namely, framework and complementarity determining regions. The framework (FR) consists of light-chain framework (FRL) and heavy-chain framework (FRH). Similarly, the complementarity determining regions (CDRs) comprises of light-chain CDRs 1-3 (CDRs L1-3) and heavy-chain CDRs 1-3 (CDRs H1-3). While FRs are relatively constant in sequence and structure across diverse antibodies, sequence variation in CDRs leading to differential conformations of CDR loops accounts for the distinct antigenic specificities of diverse antibodies. The conserved structural features in FRs and conformity of CDRs to a limited set of standard conformations allow for the accurate prediction of FV models using homology modeling techniques. Antibody structure prediction from its amino acid sequence has numerous important applications including prediction of antibody-antigen interaction interfaces and redesign of therapeutically and biotechnologically useful antibodies with improved affinity. This chapter summarizes the current practices employed in the successful homology modeling of antibody variable regions and the potential applications of the generated homology models.

De novo design and Rosetta-based assessment of high-affinity antibody variable regions (Fv) against the SARS-CoV-2 spike receptor binding domain (RBD).

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 antibody 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‐2.0.2 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 antibodies” based on human string content score74. We generated 106 different 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‐2.01 using a Rosetta‐based approach. We used Rosetta SnugDock 2,3 for local docking of the designs to evaluate their potential to bind the spike RBD and performed “forward folding” with DeepAb 4 to assess their potential to fold into the designed structures. Ultimately, our results identified one designed antibody 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 antibodies that can quickly be adapted to target spike epitopes of emerging SARS‐CoV‐2 variants or other antigenic targets.

Prediction of HIV sensitivity to monoclonal antibodies using aminoacid sequences and deep learning.

MotivationKnowing the sensitivity of a viral strain versus a monoclonal antibody is of interest for HIV vaccine development and therapy. The HIV strains vary in their resistance to antibodies, and the accurate prediction of virus-antibody sensitivity can be used to find potent antibody combinations that broadly neutralize multiple and diverse HIV strains. Sensitivity prediction can be combined with other methods such as generative algorithms to design novel antibodies in silico or with feature selection to uncover the sites of interest in the sequence. However, these tools are limited in the absence of in silico accurate prediction methods.ResultsOur method leverages the CATNAP dataset, probably the most comprehensive collection of HIV-antibodies assays, and predicts the antibody-virus sensitivity in the form of binary classification. The methods proposed by others focus primarily on analyzing the virus sequences. However, our article demonstrates the advantages gained by modeling the antibody-virus sensitivity as a function of both virus and antibody sequences. The input is formed by the virus envelope and the antibody variable region aminoacid sequences. No structural features are required, which makes our system very practical, given that sequence data is more common than structures. We compare with two other state-of-the-art methods that leverage the same dataset and use sequence data only. Our approach, based on neuronal networks and transfer learning, measures increased predictive performance as measured on a set of 31 specific broadly neutralizing antibodies.Availability and implementation https://github.com/vlad-danaila/deep_hiv_ab_pred/tree/fc-att-fix

Antibody variable region engineering for improving cancer immunotherapy.

The efficacy and specificity of conventional monoclonal antibody (mAb) drugs in the clinic require further improvement. Currently, the development and application of novel antibody formats for improving cancer immunotherapy have attracted much attention. Variable region‐retaining antibody fragments, such as antigen‐binding fragment (Fab), single‐chain variable fragment (scFv), bispecific antibody, and bi/trispecific cell engagers, are engineered with humanization, multivalent antibody construction, affinity optimization and antibody masking for targeting tumor cells and killer cells to improve antibody‐based therapy potency, efficacy and specificity. In this review, we summarize the application of antibody variable region engineering and discuss the future direction of antibody engineering for improving cancer therapies.

A new strategy: identification of specific antibodies for neutralizing epitope on SARS-CoV-2 S protein by LC-MS/MS combined with immune repertoire

Although the SARS-CoV-2 vaccine has been widely used worldwide, not all individuals can produce neutralization antibodies, so it is still urgent to find and prepare neutralization antibodies for COVID-19 prevention or treatment. In this study, we created a new strategy to effectively obtain neutralizing antibodies or complementary determining region 3 (CDR3) of neutralizing antibodies against SARS-CoV-2. We first predicted and synthesized several B cell epitopes on RBD and adjacent RBD of S protein, then the B cell epitopes were used to prepare affinity chromatography columns respectively and purify the binding IgG from serum samples of convalescent COVID-19 patients. After these IgGs were identified to have neutralizing activity, the peptide sequences of the antigen-binding regions (variable region) of neutralizing antibodies were analyzed by protein mass spectrometry. Subsequently, the B cells from the same individual were sorted and used to obtain their full BCR repertoire by 5′ RACE combined with high-throughput of PacBio sequencing method. Then, the peptide sequence of neutralizing antibody variable region by protein mass spectrometry was mapped to the full BCR repertoire and found the full variable region sequence of neutralizing antibodies. Finally, we obtained and synthesized numerous CDR3 peptides of neutralizing antibodies to confirm the neutralizing activity for SARS-CoV-2 infection. Our results indicate that the novel scheme will be suitable for rapid screening of neutralizing antibodies, including screening neutralizing antibodies against SARS-CoV-2 and other pathogenic microorganisms.