Antibody Sequences Research Articles

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

Antibodies are key proteins of the adaptive immune system, and there exists a large body of academic literature and patents dedicated to their study and concomitant conversion into therapeutics, diagnostics, or reagents. These documents often contain extensive functional characterisations of the sets of antibodies they describe. However, leveraging these heterogeneous reports, for example to offer insights into the properties of query antibodies of interest, is currently challenging as there is no central repository through which this wide corpus can be mined by sequence or structure. Here, we present PLAbDab (the Patent and Literature Antibody Database), a self-updating repository containing over 150,000 paired antibody sequences and 3D structural models, of which over 65000 are unique. We describe the methods used to extract, filter, pair, and model the antibodies in PLAbDab, and showcase how PLAbDab can be searched by sequence, structure, or keyword. PLAbDab uses include annotating query antibodies with potential antigen information from similar entries, analysing structural models of existing antibodies to identify modifications that could improve their properties, and facilitating the compilation of bespoke datasets of antibody sequences/structures that bind to a specific antigen. PLAbDab is freely available via Github (https://github.com/oxpig/PLAbDab) and as a searchable webserver (https://opig.stats.ox.ac.uk/webapps/plabdab/).

Antibody drug conjugates in breast cancer in China: Highlights, challenges, and prospects.

Antibody drug conjugates (ADCs) are novel drugs that exert specific cytotoxicity against tumor cells. China approved T-Dxd in May 2023, and their introduction has changed the nation's clinical practice. Although more than 700 ADCs are being investigated worldwide, the challenges that remain in antibody engineering, drug discovery, safety management, resistance, drug selection, and sequencing hinder the further promotion and application of ADCs. Experts in China have discussed the several critical concerns related to clinical practice since 2022. Here, the authors conducted a review of ADCs and then discussed several ADCs explored in China. This study proposes several solutions and strategies to maximize the potential benefit that ADCs can provide to patients with breast cancer.

Artificial Intelligence Approach for the Discovery of Autoantigen Recognition By B-Cell Lymphomas

Artificial Intelligence approach for the discovery of autoantigen recognition by B-cell lymphomas Introduction With a common origin from mature B-cells, B-cell lymphomas express a unique clonal surface immunoglobulin (Ig) that may transmit survival signals autonomously or following binding to cognate antigens. An increasing number of potential autoantigen targets is described for several mature B-cell lymphomas, however, the discovery of novel targets represents a very complex and expensive task. Thanks to the development of artificial intelligence (AI) tools such as AlphaFold and advances in natural language processing methods such as Large Language Models, the study of proteins has benefited, making it possible to accelerate studies of interactions between proteins. In this context, we explored the training of AI models to predict autoantigen recognition of lymphoma-derived Ig from linear protein information. Methods First, 45 lymphoma-derived Ig were sequenced, synthesized as recombinant proteins, and probed onto human proteome arrays generating 370,000 antigens-antibodies interactions. Next, statistical methods were designed and implemented to reduce noise and filter the autoantigen-antibody interactions to be processed. Subsequently, sequence-based methods were explored to implement and validate predictive models of autoantigen-antibody interaction intensity. Lastly, sequence similarity network strategies were analyzed to identify preference relationships between antibodies and autoantigens. Results and Discussion From the 370,000 interactions, designed filters allowed to reduce the noise, generating a total of 270,000 valid interactions, which were used to train predictive models. Large language model methods, amino acid coding strategies via physicochemical properties, and spatial transformation techniques through Fourier transforms were explored as methods for the numerical representation of autoantigen and antibody sequences. Concatenation strategies and linear and non-linear combinations were explored to represent the autoantigen-antibody interaction complexes. More than 1000 predictive models were explored. The best performances were obtained by applying the pre-trained bepler and esm1b models, concatenation strategies, and using Random Forest algorithms as a training strategy. In addition, cross-validation methods with k-fold (k=10) were applied to prevent overfitting. The best results achieve a performance of 0.9 of Pearsons' coefficient and an MSE of 0.08. Alternatively, training strategies based on deep learning architectures such as CNN or GCN were used, although they presented similar results to those achieved by the selected method. Finally, the model was validated using molecular dynamics techniques, studying the affinity of interactions in a selected random sample, with a correlation between the predicted affinity results and the affinity of the interaction complexes measured by molecular dynamics. Conclusions The large language model methods explored for the training of predictive models have been combined with sequence similarity network methods for constructing autoantigen and antibody interaction networks. Interaction networks helped to identify patterns of antibody recognition preferences. In future work, the incorporation of unsupervised learning algorithms, enrichment analysis, and simulation of interactions via the generated predictive model is proposed to build an efficient pattern detection strategy to facilitate the discovery of autoantigen interaction of lymphoma derived Ig.

IgLM: Infilling language modeling for antibody sequence design

Discovery and optimization of monoclonal antibodies for therapeutic applications relies on large sequence libraries but is hindered by developability issues such as low solubility, high aggregation, and high immunogenicity. Generative language models, trained on millions of protein sequences, are a powerful tool for the on-demand generation of realistic, diverse sequences. We present the Immunoglobulin Language Model (IgLM), a deep generative language model for creating synthetic antibody libraries. Compared with prior methods that leverage unidirectional context for sequence generation, IgLM formulates antibody design based on text-infilling in natural language, allowing it to re-design variable-length spans within antibody sequences using bidirectional context. We trained IgLM on 558 million (M) antibody heavy- and light-chain variable sequences, conditioning on each sequence's chain type and species of origin. We demonstrate that IgLM can generate full-length antibody sequences from a variety of species and its infilling formulation allows it to generate infilled complementarity-determining region (CDR) loop libraries with improved in silico developability profiles. A record of this paper's transparent peer review process is included in the supplemental information.

An integrated approach to the characterization of immune repertoires using AIMS: An Automated Immune Molecule Separator.

The adaptive immune system employs an array of receptors designed to respond with high specificity to pathogens or molecular aberrations faced by the host organism. Binding of these receptors to molecular fragments-collectively referred to as antigens-initiates immune responses. These antigenic targets are recognized in their native state on the surfaces of pathogens by antibodies, whereas T cell receptors (TCR) recognize processed antigens as short peptides, presented on major histocompatibility complex (MHC) molecules. Recent research has led to a wealth of immune repertoire data that are key to interrogating the nature of these molecular interactions. However, existing tools for the analysis of these large datasets typically focus on molecular sets of a single type, forcing researchers to separately analyze strongly coupled sequences of interacting molecules. Here, we introduce a software package for the integrated analysis of immune repertoire data, capable of identifying distinct biophysical differences in isolated TCR, MHC, peptide, antibody, and antigen sequence data. This integrated analytical approach allows for direct comparisons across immune repertoire subsets and provides a starting point for the identification of key interaction hotspots in complementary receptor-antigen pairs. The software (AIMS-Automated Immune Molecule Separator) is freely available as an open access package in GUI or command-line form.

“Redirecting an anti-IL-1β antibody to bind a new, unrelated and computationally predicted epitope on hIL-17A”

Antibody engineering technology is at the forefront of therapeutic antibody development. The primary goal for engineering a therapeutic antibody is the generation of an antibody with a desired specificity, affinity, function, and developability profile. Mature antibodies are considered antigen specific, which may preclude their use as a starting point for antibody engineering. Here, we explore the plasticity of mature antibodies by engineering novel specificity and function to a pre-selected antibody template. Using a small, focused library, we engineered AAL160, an anti-IL-1β antibody, to bind the unrelated antigen IL-17A, with the introduction of seven mutations. The final redesigned antibody, 11.003, retains favorable biophysical properties, binds IL-17A with sub-nanomolar affinity, inhibits IL-17A binding to its cognate receptor and is functional in a cell-based assay. The epitope of the engineered antibody can be computationally predicted based on the sequence of the template antibody, as is confirmed by the crystal structure of the 11.003/IL-17A complex. The structures of the 11.003/IL-17A and the AAL160/IL-1β complexes highlight the contribution of germline residues to the paratopes of both the template and re-designed antibody. This case study suggests that the inherent plasticity of antibodies allows for re-engineering of mature antibodies to new targets, while maintaining desirable developability profiles.

Antibody-Free Fluorine-Assisted Metabolic Sequencing of RNA N4-Acetylcytidine.

N4-Acetylcytidine (ac4C) has been found to affect a variety of cellular and biological processes. For a mechanistic understanding of the roles of ac4C in biology and disease, we present an antibody-free, fluorine-assisted metabolic sequencing method to detect RNA ac4C, called "FAM-seq". We successfully applied FAM-seq to profile ac4C landscapes in human 293T, HeLa, and MDA cell lines in parallel with the reported acRIP-seq method. By comparison with the classic ac4C antibody sequencing method, we found that FAM-seq is a convenient and reliable method for transcriptome-wide mapping of ac4C. Because this method holds promise for detecting nascent RNA ac4C modifications, we further investigated the role of ac4C in regulating chemotherapy drug resistance in chronic myeloid leukemia. The results indicated that drug development or combination therapy could be enhanced by appreciating the key role of ac4C modification in cancer therapy.

High-volume hybridoma sequencing on the NeuroMabSeq platform enables efficient generation of recombinant monoclonal antibodies and scFvs for neuroscience research

The Neuroscience Monoclonal Antibody Sequencing Initiative (NeuroMabSeq) is a concerted effort to determine and make publicly available hybridoma-derived sequences of monoclonal antibodies (mAbs) valuable to neuroscience research. Over 30 years of research and development efforts including those at the UC Davis/NIH NeuroMab Facility have resulted in the generation of a large collection of mouse mAbs validated for neuroscience research. To enhance dissemination and increase the utility of this valuable resource, we applied a high-throughput DNA sequencing approach to determine immunoglobulin heavy and light chain variable domain sequences from source hybridoma cells. The resultant set of sequences was made publicly available as a searchable DNA sequence database (neuromabseq.ucdavis.edu) for sharing, analysis and use in downstream applications. We enhanced the utility, transparency, and reproducibility of the existing mAb collection by using these sequences to develop recombinant mAbs. This enabled their subsequent engineering into alternate forms with distinct utility, including alternate modes of detection in multiplexed labeling, and as miniaturized single chain variable fragments or scFvs. The NeuroMabSeq website and database and the corresponding recombinant antibody collection together serve as a public DNA sequence repository of mouse mAb heavy and light chain variable domain sequences and as an open resource for enhancing dissemination and utility of this valuable collection of validated mAbs.

Enhanced efficacy of CD19/CD22 bispecific CAR-T cells with EAAAK linker on B-cell malignancies.

Despite the great success of CD19 CAR-T cell therapy, its clinical efficacy has been greatly hampered by the high relapse rate. In this study, we designed and compared four structures of CD19/CD22 bispecific CAR-T cells with different linkers and different orders of the antibody sequences. We detected the cytotoxicity, cytokine secretion levels, sustainable killing ability, differentiation, exhaustion of these four CAR-T cells in vitro. The optimal Bis-C CAR-T cells were evaluated the efficacy using NSG mice. The two structures of CD19/CD22 bispecific CAR-T cells using (EAAAK)3 as linker had more significant cytotoxicity and cytokine secretion levels. In the process of continuous killing, Bis-C CAR-T cells showed better sustained killing ability, memory phenotype differentiation, and exhaustion. In the in vivo experiment mimicking CD19-negative relapse, Bis-C CAR-T was more able to control the tumor progression of mice in the CD19 low expression or no expression groups than CD19 CAR-T. This study has generated a novel bispecific CAR-T cell that can simultaneously target CD19 or CD22 positive tumor cells, providing a new strategy to address the limitations of single-targeted CAR-T therapy in B-cell tumors (limited response or relapse).

The immunogenicity of human-origin therapeutic antibodies are associated with V gene usage.

Therapeutic antibodies can elicit unwanted immune responses in a subset of patients, which leads to the production of anti-drug antibodies (ADA). Some of these ADAs have been reported to effect the pharmacokinetics, efficacy and/or safety of the therapeutic antibodies. The sequence diversity of antibodies are generated by VDJ recombination and mutagenesis. While the antibody generation process can create a large candidate pool for identifying high-affinity antibodies, it also could produce sequences that are foreign to the human immune system. However, it is not clear how VDJ recombination and mutagenesis impact the clinical ADA rate of therapeutic antibodies. In this study, we identified a positive correlation between the clinical ADA rate and the number of introduced mutations in the antibody sequences. We also found that the use of rare V alleles in human-origin antibody therapeutics is associated with higher risk of immunogenicity. The results suggest that antibody engineering projects should start with frameworks that contain commonly used V alleles and prioritize antibody candidates with low number of mutations to reduce the risk of immunogenicity.

Centromeric repeats in Citrus sinensis provide new insights into centromeric evolution and the distribution of G-quadruplex structures

Centromeres play a crucial role in ensuring the accurate separation of chromosomes during cell division. Despite the three rounds of genome sequencing technology undergone by Citrus sinensis (sweet orange), the presence of numerous repetitive DNA elements in its genome has led to substantial gaps in centromeric genomic mapping, leaving the composition of centromeric repeats unclear. To address this, we employed a combination of chromatin immunoprecipitation sequencing with the C. sinensis centromere-specific histone H3 variant antibody and centromere-specific bacterial artificial chromosome-3a sequencing to precisely locate the centromeres. This approach allowed us to identify a series of centromere-specific repeats, comprising five tandem repeats and nine long terminal repeat retrotransposons. Through comprehensive bioinformatics analysis, we gained valuable insights into potential centromeric evolution events and discovered the presence of DNA G-quadruplex structures of centromeric repeats in C. sinensis. Altogether, our study not only offers a valuable reference for centromeric genome assembly but also sheds light on the structural characteristics of C. sinensis centromeres.

Multimodal, broadly neutralizing antibodies against SARS-CoV-2 identified by high-throughput native pairing of BCRs from bulk B cells

TruAB Discovery is an approach that integrates cellular immunology, high-throughput immunosequencing, bioinformatics, and computational biology in order to discover naturally occurring human antibodies for prophylactic or therapeutic use. We adapted our previously described pairSEQ technology to pair B cell receptor heavy and light chains of SARS-CoV-2 spike protein-binding antibodies derived from enriched antigen-specific memory B cells and bulk antibody-secreting cells. We identified approximately 60,000 productive, in-frame, paired antibody sequences, from which 2,093 antibodies were selected for functional evaluation based on abundance, isotype and patterns of somatic hypermutation. The exceptionally diverse antibodies included RBD-binders with broad neutralizing activity against SARS-CoV-2 variants, and S2-binders with broad specificity against betacoronaviruses and the ability to block membrane fusion. A subset of these RBD- and S2-binding antibodies demonstrated robust protection against challenge in hamster and mouse models. This high-throughput approach can accelerate discovery of diverse, multifunctional antibodies against any target of interest.

Production in Bacteria and Characterization of Engineered Humanized Fab Fragment against the Nodal Protein.

Drug development in recent years is increasingly focused on developing personalized treatments based on blocking molecules selective for therapeutic targets specifically present in individual patients. In this perspective, the specificity of therapeutic targets and blocking agents plays a crucial role. Monoclonal antibodies (mAbs) and their surrogates are increasingly used in this context thanks to their ability to bind therapeutic targets and to inhibit their activity or to transport bioactive molecules into the compartments in which the targets are expressed. Small antibody-like molecules, such as Fabs, are often used in certain clinical settings where small size and better tissue penetration are required. In the wake of this research trend, we developed a murine mAb (3D1) neutralizing the activity of Nodal, an oncofetal protein that is attracting an ever-increasing interest as a selective therapeutic target for several cancer types. Here, we report the preparation of a recombinant Fab of 3D1 that has been humanized through a computational approach starting from the sequence of the murine antibody. The Fab has been expressed in bacterial cells (1 mg/L bacterial culture), biochemically characterized in terms of stability and binding properties by circular dichroism and bio-layer interferometry techniques and tested in vitro on Nodal-positive cancer cells.

How can we discover developable antibody-based biotherapeutics?

Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.

Combining deep mutational scanning to heatmap of HLA class II binding of immunogenic sequences to preserve functionality and mitigate predicted immunogenicity.

Removal of CD4 T cell epitopes from therapeutic antibody sequences is expected to mitigate their potential immunogenicity, but its application is complicated by the location of their T cell epitopes, which mainly overlap with complementarity-determining regions. We therefore evaluated the flexibility of antibody sequences to reduce the predicted affinity of corresponding peptides for HLA II molecules and to maintain antibody binding to its target in order to guide antibody engineering for mitigation of predicted immunogenicity. Permissive substitutions to reduce affinity of peptides for HLA II molecules were identified by establishing a heatmap of HLA class II binding using T-cell epitope prediction tools, while permissive substitutions preserving binding to the target were identified by means of deep mutational scanning and yeast surface display. Combinatorial libraries were then designed to identify active clones. Applied to adalimumab, an anti-TNFα human antibody, this approach identified 200 mutants with a lower HLA binding score than adalimumab. Three mutants were produced as full-length antibodies and showed a higher affinity for TNFα and neutralization ability than adalimumab. This study also sheds light on the permissiveness of antibody sequences with regard to functionality and predicted T cell epitope content.

Direct Mass Spectrometry-Based Detection and Antibody Sequencing of Monoclonal Gammopathy of Undetermined Significance from Patient Serum: A Case Study.

Monoclonal gammopathy of undetermined significance (MGUS) is a plasma cell disorder characterized by the presence of a predominant monoclonal antibody (i.e., M-protein) in serum, without clinical symptoms. Here we present a case study in which we detect MGUS by liquid-chromatography coupled with mass spectrometry (LC-MS) profiling of IgG1 in human serum. We detected a Fab-glycosylated M-protein and determined the full heavy and light chain sequences by bottom-up proteomics techniques using multiple proteases, further validated by top-down LC-MS. Moreover, the composition and location of the Fab-glycan could be determined in CDR1 of the heavy chain. The outlined approach adds to an expanding mass spectrometry-based toolkit to characterize monoclonal gammopathies such as MGUS and multiple myeloma, with fine molecular detail. The ability to detect monoclonal gammopathies and determine M-protein sequences straight from blood samples by mass spectrometry provides new opportunities to understand the molecular mechanisms of such diseases.

REVOLUTIONIZING ANTIBODY DISCOVERY INDUSTRY WITH HIGHLY EFFICIENT AND ACCURATE AI-BASED EPITOPE-SPECIFIC ANTIBODY DE NOVO DESIGN WORKFLOW

Abstract Background and significance The global antibody drug market is worth over $200 billion in 2021 and is expected to reach $380 billion by 2030. Antibody discovery is one of the most critical steps that determine the crucial properties of antibody drugs, such as efficacy, safety, and developability. Traditional methods based on mouse immunization have many drawbacks limiting drug discovery, which include long time periods, high costs, inability to target function-specific epitopes, unsuitable for low immunogenic and difficult-to-prepare antigens, the need to sacrifice mice, the need for further humanization to reduce immunogenicity, and so on. Here we report an antibody de novo design computational workflow that utilizes high-quality internally produced antibody data and advanced AI models. Using this workflow, we can de novo design antibodies that bind to user-specified functional epitopes with high affinity and specificity. Compared with classical wet-lab methods, the entire process is shortened from several months to several days and suitable for low immunogenicity and difficult-to-prepare antigens. It is particularly noteworthy that due to the use of humanized mouse-generated antibodies (Renlite bearing common light chain from Biocytogen) as training data for AI models, the designed antibodies have a high degree of humanization and good developability, effectively avoiding issues such as ADA and aggregation in subsequent processes. Methods First, with the help of Renlite, we comprehensively combined mouse immunization, B cell sorting with FACS, NGS single-cell sequencing, and bioinformatics analysis to internally generate a large amount of high-quality antibody sequence data. Second, we developed AI models for antigen-specific antibody selection and epitope prediction (bioRxiv, 2022: 2022.12. 22.521634.) to mine antigen-specific antibodies and corresponding antigen epitopes in the data. Based on the processed high-quality data, we trained an affinity prediction model that can accurately predict whether an antigen epitope and antibody sequence pair can bind to each other. Besides, using the sequence data, we trained an antibody sequence pre-training language model (bioRxiv, 2023: 2023.01. 19.524683.), which can generate high-quality antibody sequences to simulate the antibodies produced by mouse immunization. Finally, integrating the above AI models, we established an antibody de novo design computational workflow to simulate the biological process of antibody generation and affinity maturation in the mouse immune system, which can be seen as a “DigitalMouse”. Results In a test case, 1 million antibodies were designed aiming at binding to specific epitope of an antigen. 10 antibodies were selected and expressed. Binding affinity was determined using BLI. Two antibodies out of 10 had KD of 194 nM and 336 nM, respectively, with a concentration dependent signal increase on BLI. These antibodies have great potential as the starting point of candidate molecules for further in vitro, in vivo experimental validation and clinical trials. Conclusions The AI-based antibody de novo design workflow will revolutionize the antibody discovery industry paradigm, greatly shorten the antibody discovery phase, reduce R&D costs, and expand antibody discovery to more antigen targets that are difficult with animal immunization. The computational workflow will have a profound impact on the entire biopharmaceutical industry.

GENERATION OF NOVEL ANTIBODY CANDIDATES USING TRANSFORMER AND GAN-BASED DEEP LEARNING ARTIFICIAL INTELLIGENCE

Abstract Introduction Conventional library-based antibody display can only explore a small fraction of the sequences generated from animal immunization, not even to exhaust the potential sequence diversity that can be turned into antibody therapies. This is because screening for antibody is limited to sequences that can be displayed, which only constitute a subset of the entire sequences generated by B cells, whereas screening for antibody directly from single B cells can be costly. Here, we introduce a novel Artificial Intelligence-enabling tool to navigate antibody discovery from a broader range of search space with reduced cost. We trained a transformer-based model from sequences of an immunized library to cluster the clones and a generative adversarial network (GAN)-based model to generate novel sequences that can be potentially developed into antibody therapies. Background and significance One limitation in the early discovery of antibody is the number of functional candidates that can be selected. Our work provides an AI-enabling tool to discover and generate a panel of antibodies of differentiated binding strengths to a broad range of epitopes to ensure functional coverage. Methods & Results We extracted 104 sequences from the FACS-enriched yeast pool from a fully immunized alpaca (Lama pacos) using Next Generation Sequencing, from which we assembled 103 unique sdAb sequences. We fine-tuned a transformer-based deep learning model, which was previously trained from our dataset containing 100,000 antibody sequences, on such pre-processed sdAb sequences giving representation that correlates to the sequence homology for the clustering of clonal types. We postulate such representation also encodes long-range amino acid interactions in the 3D structure, making the accuracy exceeds the performance of bioinformatics-based primary sequence homology analysis. This process is fully automated and optimized to require minima computational resources. We selected 15 candidates from AI-clustered clonal groups and experimentally measured their binding activity. Kd of 12 candidates were of 10−9 affinity and 1 candidates were of 10−8 affinity, the rest one candidate was non-binding (hence a hit rate of 87%). The large sequence diversity of the CDR3 show these nanobodies are potentially good binders for a wide range of epitopes. We generated a CDR-diversifying virtual library (103) of each binding candidate by training a GAN-based models using the sequences of the same clonal group of the binder sequences. This method incorporates the probability of amino acid residues on each specific location that provides a more precise mutagenesis route than PCR-based affinity maturation. The generated sequences provided a wider CDR sequence diversity for the selection of antibodies of differentiated affinity and epitopes, which could generate candidates of different functionality. Conclusion Antibody discovery is a central step in early drug development that identification of a wide range of functional candidates could increase the success rate and reduce risks in later developments. We built an AI-enabling tool for the searching and generation of functional antibodies from animal immunization library. We believe this technology would help deliver candidates of fine-tuned affinity and functionality.

KA-Search, a method for rapid and exhaustive sequence identity search of known antibodies

Antibodies with similar amino acid sequences, especially across their complementarity-determining regions, often share properties. Finding that an antibody of interest has a similar sequence to naturally expressed antibodies in healthy or diseased repertoires is a powerful approach for the prediction of antibody properties, such as immunogenicity or antigen specificity. However, as the number of available antibody sequences is now in the billions and continuing to grow, repertoire mining for similar sequences has become increasingly computationally expensive. Existing approaches are limited by either being low-throughput, non-exhaustive, not antibody specific, or only searching against entire chain sequences. Therefore, there is a need for a specialized tool, optimized for a rapid and exhaustive search of any antibody region against all known antibodies, to better utilize the full breadth of available repertoire sequences. We introduce Known Antibody Search (KA-Search), a tool that allows for the rapid search of billions of antibody variable domains by amino acid sequence identity across either the variable domain, the complementarity-determining regions, or a user defined antibody region. We show KA-Search in operation on the sim 2.4 billion antibody sequences available in the OAS database. KA-Search can be used to find the most similar sequences from OAS within 30 minutes and a representative subset of 10 million sequences in less than 9 seconds. We give examples of how KA-Search can be used to obtain new insights about an antibody of interest. KA-Search is freely available at https://github.com/oxpig/kasearch.

Aging modifies endometrial dendritic cell function and unconventional double negative T cells in the human genital mucosa

BackgroundImmune function in the genital mucosa balances reproduction with protection against pathogens. As women age, genital infections, and gynecological cancer risk increase, however, the mechanisms that regulate cell-mediated immune protection in the female genital tract and how they change with aging remain poorly understood. Unconventional double negative (DN) T cells (TCRαβ + CD4-CD8-) are thought to play important roles in reproduction in mice but have yet to be characterized in the human female genital tract. Using genital tissues from women (27–77 years old), here we investigated the impact of aging on the induction, distribution, and function of DN T cells throughout the female genital tract.ResultsWe discovered a novel site-specific regulation of dendritic cells (DCs) and unconventional DN T cells in the genital tract that changes with age. Human genital DCs, particularly CD1a + DCs, induced proliferation of DN T cells in a TFGβ dependent manner. Importantly, induction of DN T cell proliferation, as well as specific changes in cytokine production, was enhanced in DCs from older women, indicating subset-specific regulation of DC function with increasing age. In human genital tissues, DN T cells represented a discrete T cell subset with distinct phenotypical and transcriptional profiles compared to CD4 + and CD8 + T cells. Single-cell RNA and oligo-tag antibody sequencing studies revealed that DN T cells represented a heterogeneous population with unique homeostatic, regulatory, cytotoxic, and antiviral functions. DN T cells showed relative to CD4 + and CD8 + T cells, enhanced expression of inhibitory checkpoint molecules and genes related to immune regulatory as well as innate-like anti-viral pathways. Flow cytometry analysis demonstrated that DN T cells express tissue residency markers and intracellular content of cytotoxic molecules. Interestingly, we demonstrate age-dependent and site-dependent redistribution and functional changes of genital DN T cells, with increased cytotoxic potential of endometrial DN T cells, but decreased cytotoxicity in the ectocervix as women age, with implications for reproductive failure and enhanced susceptibility to infections respectively.ConclusionsOur deep characterization of DN T cell induction and function in the female genital tract provides novel mechanistic avenues to improve reproductive outcomes, protection against infections and gynecological cancers as women age.