Antibody Sequences Research Articles

ADTGP: correcting single-cell antibody sequencing data using Gaussian process regression.

We present ADTGP, an R package that uses Gaussian process regression to correct droplet-specific technical noise in single-cell protein sequencing data. ADTGP improves the interpretability of the data by modeling the distribution of protein expression, conditioned on equal isotype control counts across cells. ADTGP is written in R and needs only the protein raw counts, isotype control raw counts, and a design matrix to run. ADTGP can be installed from https://github.com/northNomad/ADTGP. It depends on Stan and the R package 'cmdstanr'.

ABTrans: A Transformer-based Model for Predicting Interaction between Anti-Aβ Antibodies and Peptides.

Antibodies against Aβ peptide have been recently approved to treat Alzheimer's disease, underscoring the importance of understanding their interactions for developing more potent treatments. Here we investigated the interaction between anti-Aβ antibodies and various peptides using a deep learning model. Our model, ABTrans, was trained on dodecapeptide sequences from phage display experiments and known anti-Aβ antibody sequences sourced from public sources. It classified the binding ability between anti-Aβ antibodies and dodecapeptides into four levels: not binding, weak binding, medium binding, and strong binding, achieving an accuracy of 0.83. Using ABTrans, we examined the cross-reaction of anti-Aβ antibodies with other human amyloidogenic proteins, revealing that Aducanumab and Donanemab exhibited the least cross-reactivity. Additionally, we systematically screened interactions between eleven selected anti-Aβ antibodies and all human proteins to identify potential off-target candidates.

Addressing the antibody germline bias and its effect on language models for improved antibody design.

The versatile binding properties of antibodies have made them an extremely important class of biotherapeutics. However, therapeutic antibody development is a complex, expensive and time-consuming task, with the final antibody needing to not only have strong and specific binding, but also be minimally impacted by developability issues. The success of transformer-based language models in protein sequence space and the availability of vast amounts of antibody sequences, has led to the development of many antibody-specific language models to help guide antibody design. Antibody diversity primarily arises from V(D)J recombination, mutations within the CDRs, and/or from a few non-germline mutations outside the CDRs. Consequently, a significant portion of the variable domain of all natural antibody sequences remains germline. This affects the pre-training of antibody-specific language models, where this facet of the sequence data introduces a prevailing bias towards germline residues. This poses a challenge, as mutations away from the germline are often vital for generating specific and potent binding to a target, meaning that language models need be able to suggest key mutations away from germline. In this study, we explore the implications of the germline bias, examining its impact on both general-protein and antibody-specific language models. We develop and train a series of new antibody-specific language models optimised for predicting non-germline residues. We then compare our final model, AbLang-2, with current models and show how it suggests a diverse set of valid mutations with high cumulative probability. AbLang-2 is trained on both unpaired and paired data, and is freely available at https://github.com/oxpig/AbLang2.git. Supplementary data are available at Journal Name online.

Learning antibody sequence constraints from allelic inclusion.

Antibodies and B-cell receptors (BCRs) are produced by B cells, and are built of a heavy chain and a light chain. Although each B cell could express two different heavy chains and four different light chains, usually only a unique pair of heavy chain and light chain is expressed-a phenomenon known as allelic exclusion . However, a small fraction of naive-B cells violate allelic exclusion by expressing two productive light chains, one of which has impaired function; this has been called allelic inclusion . We demonstrate that these B cells can be used to learn constraints on antibody sequence. Using large-scale single-cell sequencing data from humans, we find examples of light chain allelic inclusion in thousands of naive-B cells, which is an order of magnitude larger than existing datasets. We train machine learning models to identify the abnormal sequences in these cells. The resulting models correlate with antibody properties that they were not trained on, including polyreactivity, surface expression, and mutation usage in affinity maturation. These correlations are larger than what is achieved by existing antibody modeling approaches, indicating that allelic inclusion data contains useful new information. We also investigate the impact of similar selection forces on the heavy chain in mouse, and observe that pairing with the surrogate light chain significantly restricts heavy chain diversity.

Pre-training with a rational approach for antibody sequence representation.

Antibodies represent a specific class of proteins produced by the adaptive immune system in response to pathogens. Mining the information embedded in antibody amino acid sequences can benefit both antibody property prediction and novel therapeutic development. However, antibodies possess unique features that should be incorporated using specifically designed training methods, leaving room for improvement in pre-training models for antibody sequences. In this study, we present a Pre-trained model of Antibody sequences trained with a Rational Approach for antibodies (PARA). PARA employs a strategy conforming to antibody sequence patterns and an advanced natural language processing self-encoding model structure. This approach addresses the limitations of existing protein pre-training models, which primarily utilize language models without fully considering the differences between protein sequences and language sequences. We demonstrate PARA's performance on several tasks by comparing it to various published pre-training models of antibodies. The results show that PARA significantly outperforms existing models on these tasks, suggesting that PARA has an advantage in capturing antibody sequence information. The antibody latent representation provided by PARA can substantially facilitate studies in relevant areas. We believe that PARA's superior performance in capturing antibody sequence information offers significant potential for both antibody property prediction and the development of novel therapeutics. PARA is available at https://github.com/xtalpi-xic.

Multiplexed Antibody Sequencing and Profiling of the Human Hemagglutinin-specific Memory B Cell Response following Influenza Vaccination.

Influenza virus is a highly contagious respiratory pathogen causing between 9.4 and 41 million infections per year in the United States in the last decade. Annual vaccination is recommended by the World Health Organization, with the goal to reduce influenza severity and transmission. Ag-specific single B cell sequencing methodologies have opened up new avenues into the dissection of the Ab response to influenza virus. The improvement of these methodologies is pivotal to reduce the associated costs and optimize the operational workflow and throughput, especially in the context of multiple samples. In this study, PBMCs and serum samples were collected longitudinally from eight influenza vaccinees either vaccinated yearly for four consecutive influenza seasons or once for one season. Following the serological and B cell profiling of their polyclonal Ab response to a panel of historical, recent, and next-generation influenza vaccine hemagglutinin (HA) and virus strains, a single multiplexed Ag-specific single B cell sequencing run allowed to capture HA-specific memory B cells that were analyzed for preferential Ig H chain/L chain pairing, isotype/subclass usage, and the presence of public BCR clonotypes across participants. Binding and functional profiles of representative private and public clonotypes confirmed their HA specificity, and their overall binding and functional activity were consistent with those observed at the polyclonal level. Collectively, this high-resolution and multiplexed Ab repertoire analysis demonstrated the validity of this optimized methodology in capturing Ag-specific BCR clonotypes, even in the context of a rare B cell population, such as in the case of the peripheral Ag-specific memory B cells.

Antibody sequence determinants of viral antigen specificity.

Throughout life, humans experience repeated exposure to viral antigens through infection and vaccination, resulting in the generation of diverse, antigen-specific antibody repertoires. A paramount feature of antibodies that enables their critical contributions in counteracting recurrent and novel pathogens, and consequently fostering their utility as valuable targets for therapeutic and vaccine development, is the exquisite specificity displayed against their target antigens. Yet, there is still limited understanding of the determinants of antibody-antigen specificity, particularly as a function of antibody sequence. In recent years, experimental characterization of antibody repertoires has led to novel insights into fundamental properties of antibody sequences but has been largely decoupled from at-scale antigen specificity analysis. Here, using the LIBRA-seq technology, we generated a large data set mapping antibody sequence to antigen specificity for thousands of B cells, by screening the repertoires of a set of healthy individuals against 20 viral antigens representing diverse pathogens of biomedical significance. Analysis uncovered virus-specific patterns in variable gene usage, gene pairing, somatic hypermutation, as well as the presence of convergent antiviral signatures across multiple individuals, including the presence of public antibody clonotypes. Notably, our results showed that, for B-cell receptors originating from different individuals but leveraging an identical combination of heavy and light chain variable genes, there is a specific CDRH3 identity threshold above which B cells appear to exclusively share the same antigen specificity. This finding provides a quantifiable measure of the relationship between antibody sequence and antigen specificity and further defines experimentally grounded criteria for defining public antibody clonality.IMPORTANCEThe B-cell compartment of the humoral immune system plays a critical role in the generation of antibodies upon new and repeated pathogen exposure. This study provides an unprecedented level of detail on the molecular characteristics of antibody repertoires that are specific to each of the different target pathogens studied here and provides empirical evidence in support of a 70% CDRH3 amino acid identity threshold in pairs of B cells encoded by identical IGHV:IGL(K)V genes, as a means of defining public clonality and therefore predicting B-cell antigen specificity in different individuals. This is of exceptional importance when leveraging public clonality as a method to annotate B-cell receptor data otherwise lacking antigen specificity information. Understanding the fundamental rules of antibody-antigen interactions can lead to transformative new approaches for the development of antibody therapeutics and vaccines against current and emerging viruses.

Semisynthetic Phage Display Library Construction: Design and Synthesis of Diversified Single-Chain Variable Fragments and Generation of Primary Libraries.

Display of antibody fragments on the surface of M13 filamentous bacteriophages is a well-established approach for the identification of antibodies binding to a target of interest. Here, we describe the first of a three-step method to construct Antibody Libraries for Therapeutic Antibody Discovery (ALTHEA) Libraries. The three-step method involves (1) primary library (PL) construction, (2) filtered library construction, and (3) secondary library construction. The first step, described here, entails design, synthesis, and cloning of four PLs. These PLs are designed with specific properties amenable to therapeutic antibody development using one universal variable heavy (VH) scaffold and four distinct variable light (VL) scaffolds. The scaffolds are diversified in positions that bind both protein and peptide targets identified in antibody-antigen complexes of known structure using the amino acid frequencies found in those positions in known human antibody sequences, avoiding residues that may lead to developability liabilities. The diversified scaffolds are combined with 90 synthetic neutral HCDR3 sequences designed with developable human diversity genes (IGHD) and joining heavy genes (IGHJ) in germline configuration, and assembled as single-chain variable fragments (scFvs) in a VL-linker-VH orientation. The four designed PLs are synthesized using trinucleotide phosphoramidites (TRIMs) and cloned independently into a phagemid vector for M13 pIII display. Quality control of the cloning of the four PLs is also described, which involves sequencing scFvs in each library.

De novo protein sequencing of antibodies for identification of neutralizing antibodies in human plasma post SARS-CoV-2 vaccination

The antibody response to vaccination and infection is a key component of the immune response to pathogens. Sequencing of peripheral B cells may not represent the complete B cell receptor repertoire. Here we present a method for sequencing human plasma-derived polyclonal IgG using a combination of mass spectrometry and B-cell sequencing. We investigate the IgG response to the Moderna Spikevax COVID-19 vaccine. From the sequencing data of the natural polyclonal response to vaccination, we generate 12 recombinant antibodies. Six derived recombinant antibodies, including four generated with de novo protein sequencing, exhibit similar or higher binding affinities than the original natural polyclonal antibody. Neutralization tests reveal that the six antibodies possess neutralizing capabilities against the target antigen. This research provides insights into sequencing polyclonal IgG antibodies and the potential of our approach in generating recombinant antibodies with robust binding affinity and neutralization capabilities. Directly examining the circulating IgG pool is crucial due to potential misrepresentations by B-cell analysis alone.

Analysis of B-cell receptor repertoire to evaluate the immunogenicity of SARS-CoV-2 RBD mRNA vaccine: MAFB-7256a (DS-5670d).

A monovalent Omicron XBB.1.5 mRNA RBD analogue vaccine, MAFB-7256a (DS-5670d), was newly developed and approved in Japan in the Spring of 2024 for the prevention of COVID-19. However, clinical efficacy data for this vaccine are currently lacking. We previously established the Quantification of Antigen-specific Antibody Sequence (QASAS) method to assess the response to SARS-CoV-2 vaccination at the mRNA level using B-cell receptor (BCR) repertoire assays and the Coronavirus Antibody Database (CoV-AbDab). Here, we used this method to evaluate the immunogenicity of MAFB-7256a. We analyzed repeated blood samples using the QASAS method from three healthy volunteers before and after MAFB-7256a vaccination. BCR response increased rapidly one week post-vaccination and then decreased, as with conventional vaccine. Notably, the matched sequences after MAFB-7256a vaccination specifically bound to the receptor-binding domain (RBD), with no sequences binding to other epitopes. These results validate that MAFB-7256a is an effective vaccine that exclusively induces antibodies specific for the RBD, demonstrating its targeted immunogenic effect.

B-246 Pab to mab conversion via mass spectrometry: direct antibody discovery from serum

Abstract Background Navigating the intricacies of discovering unique and functional antibody binders for in vitro diagnostic development comes with several technological challenges, such as limited diversity of antibody repertoires, functional screening challenges, and the absence of suitable in vitro models. A proteomics-driven strategy to antibody discovery is a promising solution to overcome these obstacles by facilitating polyclonal to monoclonal antibody conversion. De novo polyclonal antibody sequencing utilizes mass spectrometry and machine-learning driven bioinformatics to obtain complete antibody sequences from immunoserum or purified protein samples. This approach investigates the entire circulating antibody repertoire at the protein level, thereby broadening the spectrum of potential candidates. It further facilitates functional selection by enabling antibody purification and enrichment techniques. This capability facilitates the identification of antibodies possessing unique functions, crucial for subsequent downstream development. We demonstrate the application and de novo polyclonal antibody sequencing using mass spectrometry and machine learning bioinformatics to enable the next generation of antibody discovery. Methods In this study, we employed a novel mass spectrometry-based approach, REpAb, to de novo sequence polyclonal antibodies directly from the immunized serum of animals or human patients. REpAb integrates mass spectrometry with machine learning-based bioinformatics to de novo sequence and assemble monoclonal antibodies from a polyclonal mixture. Protein lysates underwent analysis using an Orbitrap EclipseTM Series instrument (ThermoFisher Scientific, CA, US) coupled with the LC Evosep One (Evosep, Denmark). Results De novo polyclonal sequencing with REpAb yielded unique antibody binders not found in splenocytes or peripheral blood mononuclear cells (PBMCs). De novo antibody sequencing-discovered antibody sequences from naturally exposed humans showed unrestricted germline usage, compared to restricted germline used in antigen-immunized animals. Additionally, a proteomics-based approach for antibody discovery enables functional antibody selection through standard protein A/G purification, followed by negative and positive selection enrichment strategies to isolate antibody clones specific to the region of interest on the antigen. Conclusions Sequencing antibodies at the protein level directly reflects the circulating antibody repertoires, which expands the antibody diversity from the BCR repertoire. Proteomic-based antibody discovery empowers the functional selection of antibodies via meticulously designed enrichment strategies. This approach allows for the identification of a diverse pool of candidates possessing specific functions, such as anti-idiotypic features.

Sequencing of cellular therapy and bispecific antibodies for the management of diffuse large B-cell lymphoma.

Historically, the management of relapsed or refractory (R/R) diffuse large B-cell lymphoma (DLBCL) following first-line chemoimmunotherapy has been second-line chemotherapy, followed by high-dose chemotherapy and consolidative autologous hematopoietic stem cell transplantation (HSCT), resulting in durable remissions in approximately 40% of patients. In 2017, chimeric antigen receptor (CAR) T-cell therapy changed the landscape of treatment for patients with R/R DLBCL, with complete response rates ranging from 40-58% and long-term disease-free survival of >40% in the highest risk subgroups, including patients who relapsed after autologous HSCT. Since that time further studies have demonstrated improved overall response rates and survival outcomes in patients with primary refractory or early-relapsed (relapse within 1 year) DLBCL treated with CAR T-cell therapy compared with autologous HSCT, advancing CAR T-cell therapy into the second-line setting. However, >50% of patients will relapse in the post-CAR T-cell setting. In the past 2 years, two CD20 x CD3 bispecific antibodies were approved by the Food and Drug Administration for the treatment of R/R DLBCL after two or more lines of systemic therapy. These bispecific antibodies have demonstrated overall response rates exceeding 50% and durable remissions at >2 years of follow-up. Additionally, a notable treatment advantage of bispecific antibodies is their ability to be administered in the community setting, making treatment more accessible for patients. The development and advancement of these novel therapies raise questions regarding the ongoing role of HSCT in the management of R/R DLBCL and the best sequence of cellular and bispecific therapies to optimize patients' outcomes.

Human monoclonal antibody cloning and expression with overlap extension PCR and short DNA fragments

Monoclonal antibodies are powerful therapeutic, diagnostic, and research tools. Methods utilized to generate monoclonal antibodies are evolving rapidly. We created a transfectable linear antibody expression cassette from a 2-h high-fidelity overlapping PCR reaction from synthesized DNA fragments. We coupled heavy and light chains into a single linear sequence with a promoter, self-cleaving peptide, and poly(A) signal to increase the flexibility of swapping variable regions from any sequence available in silico. Transfection of the linear cassette tended to generate similar levels to the two-plasmid system and generated an average of 47 μg (14–98 μg) after 5 days in 2 mL cultures with 15 unique antibody sequences. The levels of antibodies produced were sufficient for most downstream applications in less than a week. The method presented here reduces the time, cost, and complexity of cloning steps.

Comment to "Sequencing of cellular therapy and bispecific antibodies for the management of diffuse large B-cell lymphoma": chimeric antigen receptor T-cell therapy remains effective after exposure to bispecific antibodies.

Not available.

Structural and network analysis of ADAM9 and ADAM10 and its transcript expression in the male reproductive tract of Bos Taurus

A disintegrin and metalloproteinases (ADAMs) are a family of proteins expressed as transmembrane and secreted proteins in tissues. This study investigated the expression of glycoproteins and transcripts of ADAM9 and ADAM10 in different regions of the bovine male reproductive tract (MRT). Fresh bovine samples were collected from the testis, epididymis, and vas deferens of MRT for this study. Special stains were utilized for identifying glycoproteins depending on the type of mucopolysaccharides in the seminiferous tubules of the testis and epithelial cells of the epididymis and vas deferens. Quantitative real-time PCR (qPCR) was used to assess the mRNA concentrations of ADAMs9 and 10. Additionally, structural and network analysis was performed to investigate the similarity between human and bovine ADAM9 and ADAM10. Variable glycoprotein (acidic and neutral mucopolysaccharides) expression was observed in the seminiferous tubules of the testis and epithelial cells of the epididymis and vas deferens. ADAM9 and 10 mRNA were detected in the testis epididymis and vas deferens, with ADAM9 expression being significantly higher than ADAM10 mRNA expression in the testis and tail of epididymis. Significant similarity between the protein sequence of human and bovine ADAM9 (85.75%) and ADAM10 (96.66%) was observed, while the similarity between protein sequence of ADAM 9 and ADAM10 in humans and bovine was <26%. Bovine ADAM9 showed 4 binding sites, while bovine ADAM10, human ADAM9, and human ADAM10 showed 7 binding pockets, each with considerable variations in the affinity of specific antibody sequences to these binding pockets. Network analysis confirmed that ADAM9 and ADAM10 have critical roles in physiological processes involving cell migration, protease activity, and secretase-related functions. In addition, they have large numbers of hydrogen bonds to most other members of the ADAM family. Our study reports similarities in the expression of glycoproteins and ADAM9 and ADAM10 mRNA in the MRT of Bos Taurus. Structural and network analysis of ADAM9 and ADAM10 suggested functional similarities in these proteins, which are likely to be in facilitating spermatogenesis, transcervical sperm migration, egg-sperm fusion, and fertilization. Further, the similarity between human and bovine ADAM9 and ADAM10 proteins suggests that bovine sperms can be considered as a model system for comparative medicine studies.

Guiding Diffusion Models for Antibody Sequence and Structure Co-design with Developability Properties

Recent advances in deep generative methods have allowed antibody sequence and structure co-design. This study addresses the challenge of tailoring the highly variable complementarity-determining regions (CDRs) in antibodies to fulfill developability requirements. We introduce a guidance approach that integrates property information into the antibody design process using diffusion probabilistic models. This approach allows us to simultaneously design CDRs conditioned on antigen structures while considering critical properties like solubility and folding stability. Our property-guided diffusion model offers versatility by accommodating diverse property constraints, presenting a promising avenue for computational antibody design in therapeutic applications. Published by the American Physical Society 2024

The Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning.

Therapeutic antibodies such as monoclonal antibodies (mAbs), bispecific and multispecific antibodies are pivotal in therapeutic protein development and have transformed disease treatments across various therapeutic areas. The integrity of therapeutic antibodies, however, is compromised by sequence liabilities, notably deamidation, where asparagine (N) and glutamine (Q) residues undergo chemical degradations. Deamidation negatively impacts the efficacy, stability, and safety of diverse classes of antibodies, thus necessitating the critical need for the early and accurate identification of vulnerable sites. In this article, a comprehensive antibody deamidation-specific dataset (n = 2285) of varied modalities was created by using high-throughput automated peptide mapping followed by supervised machine learning to predict the deamidation propensities, as well as the extents, throughout the entire antibody sequences. We propose a novel chimeric deep learning model, integrating protein language model (pLM)-derived embeddings with local sequence information for enhanced deamidation predictions. Remarkably, this model requires only sequence inputs, eliminating the need for laborious feature engineering. Our approach demonstrates state-of-the-art performance, offering a streamlined workflow for high-throughput automated peptide mapping and deamidation prediction, with the potential of broader applicability to other antibody sequence liabilities.

EpiScan: accurate high-throughput mapping of antibody-specific epitopes using sequence information

The identification of antibody-specific epitopes on virus proteins is crucial for vaccine development and drug design. Nonetheless, traditional wet-lab approaches for the identification of epitopes are both costly and labor-intensive, underscoring the need for the development of efficient and cost-effective computational tools. Here, EpiScan, an attention-based deep learning framework for predicting antibody-specific epitopes, is presented. EpiScan adopts a multi-input and single-output strategy by designing independent blocks for different parts of antibodies, including variable heavy chain (VH), variable light chain (VL), complementary determining regions (CDRs), and framework regions (FRs). The block predictions are weighted and integrated for the prediction of potential epitopes. Using multiple experimental data samples, we show that EpiScan, which only uses antibody sequence information, can accurately map epitopes on specific antigen structures. The antibody-specific epitopes on the receptor binding domain (RBD) of SARS coronavirus 2 (SARS-CoV-2) were located by EpiScan, and the potentially valuable vaccine epitope was identified. EpiScan can expedite the epitope mapping process for high-throughput antibody sequencing data, supporting vaccine design and drug development. Availability: For the convenience of related wet-experimental researchers, the source code and web server of EpiScan are publicly available at https://github.com/gzBiomedical/EpiScan.

AI-accelerated therapeutic antibody development: practical insights

Antibodies represent the largest class of biotherapeutics thanks to their high target specificity, binding affinity and versatility. Recent breakthroughs in Artificial Intelligence (AI) have enabled information-rich in silico representations of antibodies, accurate prediction of antibody structure from sequence, and the generation of novel antibodies tailored to specific characteristics to optimize for developability properties. Here we summarize state-of-the-art methods for antibody analysis. This valuable resource will serve as a reference for the application of AI methods to the analysis of antibody sequencing datasets.

Antibody-SGM, a Score-Based Generative Model for Antibody Heavy-Chain Design.

Traditional computational methods for antibody design involved random mutagenesis followed by energy function assessment for candidate selection. Recently, diffusion models have garnered considerable attention as cutting-edge generative models, lauded for their remarkable performance. However, these methods often focus solely on the backbone or sequence, resulting in the incomplete depiction of the overall structure and necessitating additional techniques to predict the missing component. This study presents Antibody-SGM, an innovative joint structure-sequence diffusion model that addresses the limitations of existing protein backbone generation models. Unlike previous models, Antibody-SGM successfully integrates sequence-specific attributes and functional properties into the generation process. Our methodology generates full-atom native-like antibody heavy chains by refining the generation to create valid pairs of sequences and structures, starting with random sequences and structural properties. The versatility of our method is demonstrated through various applications, including the design of full-atom antibodies, antigen-specific CDR design, antibody heavy chains optimization, validation with Alphafold3, and the identification of crucial antibody sequences and structural features. Antibody-SGM also optimizes protein function through active inpainting learning, allowing simultaneous sequence and structure optimization. These improvements demonstrate the promise of our strategy for protein engineering and significantly increase the power of protein design models.