Next-Generation Sequencing of Antibody Display Repertoires

Principles of Affinity Selection

The most common application of phage-display technology is the discovery of peptides or proteins that specifically bind some molecule or other substance of interest-for example, antibodies that specifically bind an antigen. The discovery process starts with a library encompassing a very large array of proteins or peptides with a great diversity of binding specificities-for example, single-chain antibodies with a great diversity of antigen-binding sites. Each member of the array is displayed on the surface of hundreds to billions of identical virus particles (virions) belonging to a single-phage clone; the library as a whole comprises millions to billions of such clones, all mixed together in a single vessel. Affinity selection is the process by which a molecule or substance of interest-generically called the selector-is used to select very rare clones in the library displaying proteins or peptides that happen to bind the selector with high affinity and selectivity. Here, I explain general principles guiding a successful affinity-selection project-principles grounded in phage biology, kinetics of reversible binding, technological advances, and the practical experience of thousands of investigators around the globe.

Deep mining of antibody phage-display selections using Oxford Nanopore Technologies and Dual Unique Molecular Identifiers

Antibody phage-display technology identifies antibody-antigen interactions through multiple panning rounds, but traditional screening gives no information on enrichment or diversity throughout the process. This results in the loss of valuable binders. Next Generation Sequencing can overcome this problem. We introduce a high accuracy long-read sequencing method based on the recent Oxford Nanopore Technologies (ONT) Q20 + chemistry in combination with dual unique molecular identifiers (UMIs) and an optimized bioinformatic analysis pipeline to monitor the selections. We identified binders from two single-domain antibody libraries selected against a model protein. Traditional colony-picking was compared with our ONT-UMI method. ONT-UMI enabled monitoring of diversity and enrichment before and after each selection round. By combining phage antibody selections with ONT-UMIs, deep mining of output selections is possible. The approach provides an alternative to traditional screening, enabling diversity quantification after each selection round and rare binder recovery, even when the dominating binder was > 99% abundant. Moreover, it can give insights on binding motifs for further affinity maturation and specificity optimizations. Our results demonstrate a platform for future data guided selection strategies.

Development of a new affinity maturation protocol for the construction of an internalizing anti-nucleolin antibody library

Over the last decades, monoclonal antibodies have substantially improved the treatment of several conditions. The continuous search for novel therapeutic targets and improvements in antibody's structure, demands for a constant optimization of their development. In this regard, modulation of an antibody's affinity to its target has been largely explored and culminated in the discovery and optimization of a variety of molecules. It involves the creation of antibody libraries and selection against the target of interest. In this work, we aimed at developing a novel protocol to be used for the affinity maturation of an antibody previously developed by our group. An antibody library was constructed using an in vivo random mutagenesis approach that, to our knowledge, has not been used before for antibody development. Then, a cell-based phage display selection protocol was designed to allow the fast and simple screening of antibody clones capable of being internalized by target cells. Next generation sequencing coupled with computer analysis provided an extensive characterization of the created library and post-selection pool, that can be used as a guide for future antibody development. With a single selection step, an enrichment in the mutated antibody library, given by a decrease in almost 50% in sequence diversity, was achieved, and structural information useful in the study of the antibody-target interaction in the future was obtained.

Development, High-Throughput Profiling, and Biopanning of a Large Phage Display Single-Domain Antibody Library

Immunoglobulin G-based monoclonal antibodies (mAbs) have been effective in treating various diseases, but their large molecular size can limit their penetration of tissue and efficacy in multifactorial diseases, necessitating the exploration of alternative forms. In this study, we constructed a phage display library comprising single-domain antibodies (sdAbs; or "VHHs"), known for their small size and remarkable stability, using a total of 1.6 × 109 lymphocytes collected from 20 different alpacas, resulting in approximately 7.16 × 1010 colonies. To assess the quality of the constructed library, next-generation sequencing-based high-throughput profiling was performed, analyzing approximately 5.65 × 106 full-length VHH sequences, revealing 92% uniqueness and confirming the library's diverse composition. Systematic characterization of the library revealed multiple sdAbs with high affinity for three therapeutically relevant antigens. In conclusion, our alpaca sdAb phage display library provides a versatile resource for diagnostics and therapeutics. Furthermore, the library's vast natural VHH antibody repertoire offers insights for generating humanized synthetic sdAb libraries, further advancing sdAb-based therapeutics.

Rapid discovery of high-affinity antibodies via massively parallel sequencing, ribosome display and affinity screening

Developing therapeutic antibodies is laborious and costly. Here we report a method for antibody discovery that leverages the Illumina HiSeq platform to, within 3 days, screen in the order of 108 antibody-antigen interactions. The method, which we named 'deep screening', involves the clustering and sequencing of antibody libraries, the conversion of the DNA clusters into complementary RNA clusters covalently linked to the instrument's flow-cell surface on the same location, the in situ translation of the clusters into antibodies tethered via ribosome display, and their screening via fluorescently labelled antigens. By using deep screening, we discovered low-nanomolar nanobodies to a model antigen using 4 × 106 unique variants from yeast-display-enriched libraries, and high-picomolar single-chain antibody fragment leads for human interleukin-7 directly from unselected synthetic repertoires. We also leveraged deep screening of a library of 2.4 × 105 sequences of the third complementarity-determining region of the heavy chain of an anti-human epidermal growth factor receptor 2 (HER2) antibody as input for a large language model that generated new single-chain antibody fragment sequences with higher affinity for HER2 than those in the original library.

Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances

Synthetic antibodies (Abs) represent a category of engineered proteins meticulously crafted to replicate the functions of their natural counterparts. Such Abs are generated in vitro, enabling advanced molecular alterations associated with antigen recognition, paratope site engineering, and biochemical refinements. In a parallel realm, deep sequencing has brought about a paradigm shift in molecular biology. It facilitates the prompt and cost-effective high-throughput sequencing of DNA and RNA molecules, enabling the comprehensive big data analysis of Ab transcriptomes, including specific regions of interest. Significantly, the integration of artificial intelligence (AI), based on machine- and deep- learning approaches, has fundamentally transformed our capacity to discern patterns hidden within deep sequencing big data, including distinctive Ab features and protein folding free energy landscapes. Ultimately, current AI advances can generate approximations of the most stable Ab structural configurations, enabling the prediction of de novo synthetic Abs. As a result, this manuscript comprehensively examines the latest and relevant literature concerning the intersection of deep sequencing big data and AI methodologies for the design and development of synthetic Abs. Together, these advancements have accelerated the exploration of antibody repertoires, contributing to the refinement of synthetic Ab engineering and optimizations, and facilitating advancements in the lead identification process.

Peptide Antibodies: Current Status

Peptide antibodies have become one of the most important classes of reagents in molecular biology and clinical diagnostics. For this reason, methods for their production and characterization continue to be developed, including basic peptide synthesis protocols, peptide-conjugate production and characterization, conformationally restricted peptides, immunization procedures, etc. Detailed mapping of peptide antibody epitopes has yielded important information on antibody-antigen interaction in general and specifically in relation to antibody cross-reactivity and theories of molecular mimicry. This information is essential for detailed understanding of paratope-epitope dynamics, design of antibodies for research, design of peptide-based vaccines, development of therapeutic peptide antibodies, and de novo design of antibodies with predetermined specificity.

The use of peptides, aptamers, and variable domains of heavy chain only antibodies in tissue engineering and regenerative medicine

Over the years, much research has been focused on the use of small molecules such as peptides or aptamers or more recently on the use of variable antigen-binding domain of heavy chain only antibodies in the field of tissue engineering and regenerative medicine. The use of these molecules originated as an alternative for the larger conventional antibodies, of which most drawbacks are derived from their size and complex structure. In the field of tissue engineering and regenerative medicine, biological functionalities are often conjugated to biomaterials in order to (re-)create an in vivo like situation, especially when bioinert biomaterials are used. Those biomaterials are functionalized with these functionalities for instance for the purpose of cell attachment or cell targeting for targeted drug delivery but also for local enrichment or blocking of ligands such as growth factors or cytokines on the biomaterial surface. In this review, we further refer to peptides, aptamers, and variable antigen-binding domain of heavy chain only antibodies as biological functionalities. Here, we compare these biological functionalities within the field of tissue engineering and regenerative medicine and give an overview of recent work in which these biological functionalities have been explored. We focus on the previously mentioned purposes of the biological functionalities. We will compare structural differences, possible modifications and (chemical) conjugation strategies. In addition, we will provide an overview of biologicals that are, or have been, involved in clinical trials. Finally, we will highlight the challenges of each of these biologicals. STATEMENT OF SIGNIFICANCE: In the field of tissue engineering there is broad application of functionalized biomaterials for cell attachment, targeted drug delivery and local enrichment or blocking of growth factors. This was previously mostly done via conventional antibodies, but their large size and complex structure impose various challenges with respect of retaining biological functionality. Peptides, aptamers and VHHs may provide an alternative solution for the use of conventional antibodies. This review discusses the use of these molecules for biological functionalization of biomaterials. For each of the molecules, their characteristics, conjugation possibilities and current use in research and clinical trials is described. Furthermore, this review sets out the benefits and challenges of using these types of molecules for different fields of application.

AI/ML combined with next-generation sequencing of VHH immune repertoires enables the rapid identification of de novo humanized and sequence-optimized single domain antibodies: a prospective case study

Introduction: In this study, we demonstrate the feasibility of yeast surface display (YSD) and nextgeneration sequencing (NGS) in combination with artificial intelligence and machine learning methods (AI/ML) for the identification of de novo humanized single domain antibodies (sdAbs) with favorable early developability profiles. Methods: The display library was derived from a novel approach, in which VHH-based CDR3 regions obtained from a llama (Lama glama), immunized against NKp46, were grafted onto a humanized VHH backbone library that was diversified in CDR1 and CDR2. Following NGS analysis of sequence pools from two rounds of fluorescence-activated cell sorting we focused on four sequence clusters based on NGS frequency and enrichment analysis as well as in silico developability assessment. For each cluster, long short-term memory (LSTM) based deep generative models were trained and used for the in silico sampling of new sequences. Sequences were subjected to sequence- and structure-based in silico developability assessment to select a set of less than 10 sequences per cluster for production. Results: As demonstrated by binding kinetics and early developability assessment, this procedure represents a general strategy for the rapid and efficient design of potent and automatically humanized sdAb hits from screening selections with favorable early developability profiles.

VNAR development through antigen immunization of Japanese topeshark (Hemitriakis japanica)

The VNAR (Variable New Antigen Receptor) is the smallest single-domain antibody derived from the variable domain of IgNAR of cartilaginous fishes. Despite its biomedical and diagnostic potential, research on VNAR has been limited due to the difficulties in obtaining and maintaining immune animals and the lack of research tools. In this study, we investigated the Japanese topeshark as a promising immune animal for the development of VNAR. This shark is an underutilized fishery resource readily available in East Asia coastal waters and can be safely handled without sharp teeth or venomous stingers. The administration of Venus fluorescent protein to Japanese topesharks markedly increased antigen-specific IgM and IgNAR antibodies in the blood. Both the phage-display library and the yeast-display library were constructed using RNA from immunized shark splenocytes. Each library was enriched by biopanning, and multiple antigen-specific VNARs were acquired. The obtained antibodies had affinities of 1 × 10-8 M order and showed high plasticity, retaining their binding activity even after high-temperature or reducing-agent treatment. The dissociation rate of a low-affinity VNAR was significantly improved via dimerization. These results demonstrate the potential utility of the Japanese topeshark for the development of VNAR. Furthermore, we conducted deep sequencing analysis to reveal the quantitative changes in the CDR3-coding sequences, revealing distinct enrichment bias between libraries. VNARs that were primarily enriched in the phage display had CDR3 coding sequences with fewer E. coli rare codons, suggesting translation machinery on the selection and enrichment process during biopanning.

Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data

MOTIVATION

Deep sequencing of antibody and related protein libraries after phage or yeast-surface display sorting is widely used to identify variants with increased affinity, specificity, and/or improvements in key biophysical properties. Conventional approaches for identifying optimal variants typically use the frequencies of observation in enriched libraries or the corresponding enrichment ratios. However, these approaches disregard the vast majority of deep sequencing data and often fail to identify the best variants in the libraries.

RESULTS

Here, we present a method, Position-Specific Enrichment Ratio Matrix (PSERM) scoring, that uses entire deep sequencing datasets from pre- and post-selections to score each observed protein variant. The PSERM scores are the sum of the site-specific enrichment ratios observed at each mutated position. We find that PSERM scores are much more reproducible and correlate more strongly with experimentally measured properties than frequencies or enrichment ratios, including for multiple antibody properties (affinity and non-specific binding) for a clinical-stage antibody (emibetuzumab). We expect that this method will be broadly applicable to diverse protein engineering campaigns.

AVAILABILITY AND IMPLEMENTATION

All deep sequencing datasets and code to perform the analyses presented within are available via https://github.com/Tessier-Lab-UMich/PSERM_paper.

Massively parallel protein-protein interaction measurement by sequencing (MP3-seq) enables rapid screening of protein heterodimers

Protein-protein interactions (PPIs) regulate many cellular processes, and engineered PPIs have cell and gene therapy applications. Here we introduce massively parallel protein-protein interaction measurement by sequencing (MP3-seq), an easy-to-use and highly scalable yeast-two-hybrid approach for measuring PPIs. In MP3-seq, DNA barcodes are associated with specific protein pairs, and barcode enrichment can be read by sequencing to provide a direct measure of interaction strength. We show that MP3-seq is highly quantitative and scales to over 100,000 interactions. We apply MP3-seq to characterize interactions between families of rationally designed heterodimers and to investigate elements conferring specificity to coiled-coil interactions. Finally, we predict coiled heterodimer structures using AlphaFold-Multimer (AF-M) and train linear models on physics simulation energy terms to predict MP3-seq values. We find that AF-M and AF-M complex prediction-based models could be valuable for pre-screening interactions, but that measuring interactions experimentally remains necessary to rank their strengths quantitatively.

Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data

Motivation

Deep sequencing of antibody and related protein libraries after phage or yeast-surface display sorting is widely used to identify variants with increased affinity, specificity and/or improvements in key biophysical properties. Conventional approaches for identifying optimal variants typically use the frequencies of observation in enriched libraries or the corresponding enrichment ratios. However, these approaches disregard the vast majority of deep sequencing data and often fail to identify the best variants in the libraries.

Results

Here, we present a method, Position-Specific Enrichment Ratio Matrix (PSERM) scoring, that uses entire deep sequencing datasets from pre- and post-selections to score each observed protein variant. The PSERM scores are the sum of the site-specific enrichment ratios observed at each mutated position. We find that PSERM scores are much more reproducible and correlate more strongly with experimentally measured properties than frequencies or enrichment ratios, including for multiple antibody properties (affinity and non-specific binding) for a clinical-stage antibody (emibetuzumab). We expect that this method will be broadly applicable to diverse protein engineering campaigns.

Availability

All deep sequencing datasets and code to do the analyses presented within are available via GitHub.

Contact

Peter Tessier, ptessier@umich.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Accurate profiling of full-length Fv in highly homologous antibody libraries using UMI tagged short reads

Deep parallel sequencing (NGS) is a viable tool for monitoring scFv and Fab library dynamics in many antibody engineering high-throughput screening efforts. Although very useful, the commonly used Illumina NGS platform cannot handle the entire sequence of scFv or Fab in a single read, usually focusing on specific CDRs or resorting to sequencing VH and VL variable domains separately, thus limiting its utility in comprehensive monitoring of selection dynamics. Here we present a simple and robust method for deep sequencing repertoires of full length scFv, Fab and Fv antibody sequences. This process utilizes standard molecular procedures and unique molecular identifiers (UMI) to pair separately sequenced VH and VL. We show that UMI assisted VH-VL matching allows for a comprehensive and highly accurate mapping of full length Fv clonal dynamics in large highly homologous antibody libraries, as well as identification of rare variants. In addition to its utility in synthetic antibody discovery processes, our method can be instrumental in generating large datasets for machine learning (ML) applications, which in the field of antibody engineering has been hampered by conspicuous paucity of large scale full length Fv data.

Concerted Antibody and Antigen Discovery by Differential Whole-cell Phage Display Selections and Multi-omic Target Deconvolution

Monoclonal antibody (mAb)-based biologics are well established treatments of cancer. Antibody discovery campaigns are typically directed at a single target of interest, which inherently limits the possibility of uncovering novel antibody specificities or functionalities. Here, we present a target-unbiased approach for antibody discovery that relies on generating mAbs against native target cell surfaces via phage display. This method combines a previously reported method for improved whole-cell phage display selections with next-generation sequencing analysis to efficiently identify mAbs with the desired target cell reactivity. Applying this method to multiple myeloma cells yielded a panel of >50 mAbs with unique sequences and diverse reactivities. To uncover the identities of the cognate antigens recognized by this panel, representative mAbs from each unique reactivity cluster were used in a multi-omic target deconvolution approach. From this, we identified and validated three cell surface antigens: PTPRG, ICAM1, and CADM1. PTPRG and CADM1 remain largely unstudied in the context of multiple myeloma, which could warrant further investigation into their potential as therapeutic targets. These results highlight the utility of optimized whole-cell phage display selection methods and could motivate further interest in target-unbiased antibody discovery workflows.

Generation and Next-Generation Sequencing-Based Characterization of a Large Human Combinatorial Antibody Library

Antibody phage display is a key technology for the discovery and development of target-specific monoclonal antibodies (mAbs) for use in research, diagnostics, and therapy. The construction of a high-quality antibody library, with larger and more diverse antibody repertoires, is essential for the successful development of phage display-derived mAbs. In this study, a large human combinatorial single-chain variable fragment library (1.5 × 10¹¹ colonies) was constructed from Epstein-Barr virus-infected human peripheral blood mononuclear cells stimulated with a combination of two of the activators of human B cells, the Toll-like receptor 7/8 agonist R848 and interleukin-2. Next-generation sequencing analysis with approximately 1.9 × 10⁶ and 2.7 × 10⁶ full-length sequences of heavy chain variable (VH) and κ light chain variable (Vκ) domains, respectively, revealed that the library consists of unique VH (approximately 94%) and Vκ (approximately 91%) sequences with greater diversity than germline sequences. Lastly, multiple unique mAbs with high affinity and broad cross-species reactivity could be isolated from the library against two therapeutically relevant target antigens, validating the library quality. These findings suggest that the novel antibody library we have developed may be useful for the rapid development of target-specific phage display-derived recombinant human mAbs for use in therapeutic and diagnostic applications.

The rapid and highly parallel identification of antibodies with defined biological activities by SLISY

The therapeutic applications of antibodies are manifold and the emergence of SARS-CoV-2 provides a cogent example of the value of rapidly identifying biologically active antibodies. We describe an approach called SLISY (Sequencing-Linked ImmunoSorbent assaY) that in a single experiment can assess the binding specificity of millions of clones, be applied to any screen that links DNA sequence to a potential binding moiety, and requires only a single round of biopanning. We demonstrate this approach using an scFv library applied to cellular and protein targets to identify specific or broadly reacting antibodies. For a cellular target, we use paired HLA knockout cell lines to identify a panel of antibodies specific to HLA-A3. For a protein target, SLISY identifies 1279 clones that bound to the Receptor Binding Domain of the SARS-CoV-2 spike protein, with >40% of tested clones also neutralizing its interaction with ACE2 in in vitro assays. Using a multi-comparison SLISY against the Beta, Gamma, and Delta variants, we recovered clones that exhibited broad-spectrum neutralizing potential in vitro. By evaluating millions of scFvs simultaneously against multiple targets, SLISY allows the rapid identification of candidate scFvs with defined binding profiles facilitating the identification of antibodies with the desired biological activity.

Significance of antibody numbering systems in the development of antibody engineering

Immunotherapy has become increasingly popular in recent years for treating a variety of diseases including inflammatory, neurological, oncological, and auto-immune disorders. The significant interest in antibody development is due to the high binding affinity and specificity of an antibody against a specific antigen. Recent advances in antibody engineering have provided a different view on how to engineer antibodies in silico for therapeutic and diagnostic applications. In order to improve the clinical utility of therapeutic antibodies, it is of paramount importance to understand the various molecular properties which impact antigen targeting and its potency. In antibody engineering, antibody numbering (AbN) systems play an important role to identify the complementarity determining regions (CDRs) and the framework regions (FR). Hence, it is crucial to accurately define and understand the CDR, FR and the crucial residues of heavy and light chains that aid in the binding of the antibody to the antigenic site. Detailed understanding of amino acids positions are useful for modifying the binding affinity, specificity, physicochemical features, and half-life of an antibody. In this review, we have summarized the different antibody numbering systems that are widely used in antibody engineering and highlighted their significance. Here, we have systematically explored and mentioned the various tools and servers that harness different AbN systems.

Selection of Affibody Affinity Proteins from Phagemid Libraries

Herein, we describe a general protocol for the selection of target-binding affinity protein molecules from a phagemid-encoded library. The protocol is based on our experience with phage display selections of non-immunoglobulin affibody affinity proteins but can in principle be applied to perform biopanning experiments from any phage-displayed affinity protein library available in a similar phagemid vector. The procedure begins with an amplification of the library from frozen bacterial glycerol stocks via cultivation and helper phage superinfection, followed by a step-by-step instruction of target protein preparation, selection cycles, and post-selection analyses. The described procedures in this standard protocol are relatively conservative and rely on ordinary reagents and equipment available in most molecular biology laboratories.

Structural Investigation of Therapeutic Antibodies Using Hydroxyl Radical Protein Footprinting Methods

Commercial monoclonal antibodies are growing and important components of modern therapies against a multitude of human diseases. Well-known high-resolution structural methods such as protein crystallography are often used to characterize antibody structures and to determine paratope and/or epitope binding regions in order to refine antibody design. However, many standard structural techniques require specialized sample preparation that may perturb antibody structure or require high concentrations or other conditions that are far from the conditions conducive to the accurate determination of antigen binding or kinetics. We describe here in this minireview the relatively new method of hydroxyl radical protein footprinting, a solution-state method that can provide structural and kinetic information on antibodies or antibody-antigen interactions useful for therapeutic antibody design. We provide a brief history of hydroxyl radical footprinting, examples of current implementations, and recent advances in throughput and accessibility.

A megadiverse naïve library derived from numerous camelids for efficient and rapid development of VHH antibodies

The field of antibody development is under pressure to meet rising demands for speed, cost-effectiveness, efficacy, reliability, and large-scale production. It is costly and time-consuming to immunize animals and build a single-domain antibody (sdAb) library for each target. Using the variable domain (VHH) of heavy-chain only antibodies (HcAbs) derived from blood samples of 75 non-immunized camelid animals (51 alpacas, 13 llamas, 11 Bactrian camels), and spleens from two Bactrian camels, a naïve sdAb library with extensive megadiversity and reusability was constructed. The library was evaluated using next-generation DNA sequencing (NGS) and was found to contain hundreds of billions of unique clones. To confirm the availability of target-specific VHHs, a naive library was screened for a variety of targets. At least two VHH candidates were extracted for each target using a 20-day selection pipeline. Some binders had ultrahigh potencies, with binding affinities in the nanomolar range. This naïve library, in particular, offers the possibility of acquiring unique antibodies targeting antigens of interest with low feasible dissociation constant (kD) without the time, effort, and price associated in producing antibodies in animals via antigen injection. Overall, the study shows that the megadiverse naïve library provides a rapid, adaptable, and easy platform for antibody creation, emphasizing its therapeutic and diagnostic implications.

Application of recombinant antibodies for treatment of Clostridioides difficile infection: Current status and future perspective

Clostridioides difficile (C. difficile), known as the major cause of antibiotic-associated diarrhea, is regarded as one of the most common healthcare-associated bacterial infections worldwide. Due to the emergence of hypervirulent strains, development of new therapeutic methods for C. difficile infection (CDI) has become crucially important. In this context, antibodies have been introduced as valuable tools in the research and clinical environments, as far as the effectiveness of antibody therapy for CDI was reported in several clinical investigations. Hence, production of high-performance antibodies for treatment of CDI would be precious. Traditional approaches of antibody generation are based on hybridoma technology. Today, application of in vitro technologies for generating recombinant antibodies, like phage display, is considered as an appropriate alternative to hybridoma technology. These techniques can circumvent the limitations of the immune system and they can be exploited for production of antibodies against different types of biomolecules in particular active toxins. Additionally, DNA encoding antibodies is directly accessible in in vitro technologies, which enables the application of antibody engineering in order to increase their sensitivity and specificity. Here, we review the application of antibodies for CDI treatment with an emphasis on recombinant fragment antibodies. Also, this review highlights the current and future prospects of the aforementioned approaches for antibody-mediated therapy of CDI.

Machine Learning Approaches to TCR Repertoire Analysis

Sparked by the development of genome sequencing technology, the quantity and quality of data handled in immunological research have been changing dramatically. Various data and database platforms are now driving the rapid progress of machine learning for immunological data analysis. Of various topics in immunology, T cell receptor repertoire analysis is one of the most important targets of machine learning for assessing the state and abnormalities of immune systems. In this paper, we review recent repertoire analysis methods based on machine learning and deep learning and discuss their prospects.

Understanding and Modulating Antibody Fine Specificity: Lessons from Combinatorial Biology

Combinatorial biology methods such as phage and yeast display, suitable for the generation and screening of huge numbers of protein fragments and mutated variants, have been useful when dissecting the molecular details of the interactions between antibodies and their target antigens (mainly those of protein nature). The relevance of these studies goes far beyond the mere description of binding interfaces, as the information obtained has implications for the understanding of the chemistry of antibody–antigen binding reactions and the biological effects of antibodies. Further modification of the interactions through combinatorial methods to manipulate the key properties of antibodies (affinity and fine specificity) can result in the emergence of novel research tools and optimized therapeutics.

Computational epitope binning reveals functional equivalence of sequence-divergent paratopes

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

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

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

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

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

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

High-Throughput Monoclonal Antibody Discovery from Phage Libraries: Challenging the Current Preclinical Pipeline to Keep the Pace with the Increasing mAb Demand

Simple Summary

Monoclonal antibodies are increasingly used for a broad range of diseases. Rising demand must face with time time-consuming and laborious processes to isolate novel monoclonal antibodies. Next-generation sequencing coupled to phage display provides timely and sustainable high throughput selection strategy to rapidly access novel target. Here, we describe the current NGS-guided strategies to identify potential binders from enriched sub-libraires by applying a user-friendly informatic pipeline to identify and discard false positive clones. Rescue step and strategies to boost mAb yield are also discussed to improve the limiting selection and screening steps.

Abstract

Monoclonal antibodies are among the most powerful therapeutics in modern medicine. Since the approval of the first therapeutic antibody in 1986, monoclonal antibodies keep holding great expectations for application in a range of clinical indications, highlighting the need to provide timely and sustainable access to powerful screening options. However, their application in the past has been limited by time-consuming and expensive steps of discovery and production. The screening of antibody repertoires is a laborious step; however, the implementation of next-generation sequencing-guided screening of single-chain antibody fragments has now largely overcome this issue. This review provides a detailed overview of the current strategies for the identification of monoclonal antibodies from phage display-based libraries. We also discuss the challenges and the possible solutions to improve the limiting selection and screening steps, in order to keep pace with the increasing demand for monoclonal antibodies.

Retrospective analysis of the preparation and application of immunotherapy in cancer treatment (Review)

Monoclonal antibody technology plays a vital role in biomedical and immunotherapy, which greatly promotes the study of the structure and function of genes and proteins. To date, monoclonal antibodies have gone through four stages: murine monoclonal antibody, chimeric monoclonal antibody, humanised monoclonal antibody and fully human monoclonal antibody; thousands of monoclonal antibodies have been used in the fields of biology and medicine, playing a special role in the pathogenesis, diagnosis and treatment of disease. In this review, we compare the advantages and disadvantages of hybridoma technology, phage display technology, ribosome display technology, transgenic mouse technology, single B cell monoclonal antibody generation technologies, and forecast the promising applications of these technologies in clinical medicine, disease diagnosis and tumour treatment.

Yeast Surface Display: New Opportunities for a Time-Tested Protein Engineering System

Yeast surface display has proven to be a powerful tool for the discovery of antibodies and other novel binding proteins and for engineering the affinity and selectivity of existing proteins for their targets. In the decades since the first demonstrations of the approach, the range of yeast display applications has greatly expanded to include many different protein targets and has grown to encompass methods for rapid protein characterization. Here, we briefly summarize the development of yeast display methodologies and highlight several selected examples of recent applications to timely and challenging protein engineering and characterization problems.

Reconstruction of full antibody sequences in NGS datasets and accurate VL:VH coupling by cluster coordinate matching of non-overlapping reads

Next-generation sequencing (NGS) is an indispensable tool in antibody discovery projects. However, the limits on NGS read length make it difficult to reconstruct full antibody sequences from the sequencing runs, especially if the six CDRs are randomized. To overcome that, we took advantage of Illumina’s cluster mapping capabilities to pair non-overlapping reads and reconstruct full Fab sequences with accurate V_L:V_H pairings. The method relies on in silico cluster coordinate information, and not on extensive in vitro manipulation, making the protocol easily deployable and less prone to PCR-derived errors. This work maintains the throughput necessary for antibody discovery campaigns, and a high degree of fidelity, which potentiates not only phage-display and synthetic library-based discovery methods, but also the NGS-driven analysis of naïve and immune libraries.

Deep mutational scanning for therapeutic antibody engineering

The biophysical and functional properties of monoclonal antibody (mAb) drug candidates are often improved by protein engineering methods to increase the probability of clinical efficacy. One emerging method is deep mutational scanning (DMS) which combines the power of exhaustive protein mutagenesis and functional screening with deep sequencing and bioinformatics. The application of DMS has yielded significant improvements to the affinity, specificity, and stability of several preclinical antibodies alongside novel applications such as introducing multi-specific binding properties. DMS has also been applied directly on target antigens to precisely map antibody-binding epitopes and notably to profile the mutational escape potential of viral targets (e.g., SARS-CoV-2 variants). Finally, DMS combined with machine learning is enabling advances in the computational screening and engineering of therapeutic antibodies.

Building Personalized Cancer Therapeutics through Multi-Omics Assays and Bacteriophage-Eukaryotic Cell Interactions

Bacteriophage-eukaryotic cell interaction provides the biological foundation of Phage Display technology, which has been widely adopted in studies involving protein-protein and protein-peptide interactions, and it provides a direct link between the proteins and the DNA encoding them. Phage display has also facilitated the development of new therapeutic agents targeting personalized cancer mutations. Proteins encoded by mutant genes in cancers can be processed and presented on the tumor cell surface by human leukocyte antigen (HLA) molecules, and such mutant peptides are called Neoantigens. Neoantigens are naturally existing tumor markers presented on the cell surface. In clinical settings, the T-cell recognition of neoantigens is the foundation of cancer immunotherapeutics. This year, we utilized phage display to successfully develop the 1st antibody-based neoantigen targeting approach for next-generation personalized cancer therapeutics. In this article, we discussed the strategies for identifying neoantigens, followed by using phage display to create personalized cancer therapeutics-a complete pipeline for personalized cancer treatment.

Short Read-Length Next Generation DNA Sequencing of Antibody CDR Combinations from Phage Selection Outputs

Phage display is commonly used to select target-binding antibody fragments from large libraries containing billions of unique antibody clones. In practice, selection outputs are often highly heterogenous, making it desirable to recover sequence information from the selected pool. Next Generation DNA Sequencing (NGS) enables the acquisition of sufficient sequencing reads to cover the pool diversity, however read-lengths are typically too short to capture paired antibody complementarity-determining regions (CDRs), which is needed to reconstruct target-binding antibody fragments. Here, we describe a simple in vitro protocol to bring the DNA encoding the antibody CDRs closer together. The final PCR product referred to as a "CDR strip" is suitable for short read-length NGS. In this method, phagemid ssDNA is recovered from antibody phage display biopanning and used as a template to create a heteroduplex with deletions between CDRs of interest. The shorter strand in the heteroduplex is preferentially PCR amplified to generate a CDR strip that is sequenced using NGS. We have also included a bioinformatics approach to analyze the CDR strip populations so that single antibody clones can be created from paired CDR sequences.

Affinity maturation of antibodies by combinatorial codon mutagenesis versus error-prone PCR

IN VITRO

affinity maturation of therapeutic monoclonal antibodies is commonly applied to achieve desired properties, such as improved binding kinetics and affinity. Currently there are no universally accepted protocols for generation of variegated antibody libraries or selection thereof. Here, we performed affinity maturation using a yeast-based single-chain variable fragment (scFv) expression system to compare two mutagenesis methods: random mutagenesis across the entire V(D)J region by error-prone PCR, and a novel combinatorial mutagenesis process limited to the complementarity-determining regions (CDRs). We applied both methods of mutagenesis to four human antibodies against well-known immuno-oncology target proteins. Detailed sequence analysis showed an even mutational distribution across the entire length of the scFv for the error-prone PCR method and an almost exclusive targeting of the CDRs for the combinatorial method. Though there were distinct mutagenesis profiles for each target antibody and mutagenesis method, we found that both methods improved scFv affinity with similar efficiency. When a subset of the affinity-matured antibodies was expressed as full-length immunoglobulin, the measured affinity constants were mostly comparable to those of the respective scFv, but the full-length antibodies were inferior to their scFv counterparts for one of the targets. Furthermore, we found that improved affinity for the full-length antibody did not always translate into enhanced binding to cell-surface expressed antigen or improved immune checkpoint blocking ability, suggesting that screening with full-length antibody or antigen-binding fragment formats might be advantageous and the subject of a future study.

Biological controls for standardization and interpretation of adaptive immune receptor repertoire profiling

Use of adaptive immune receptor repertoire sequencing (AIRR-seq) has become widespread, providing new insights into the immune system with potential broad clinical and diagnostic applications. However, like many high-throughput technologies, it comes with several problems, and the AIRR Community was established to understand and help solve them. We, the AIRR Community’s Biological Resources Working Group, have surveyed scientists about the need for standards and controls in generating and annotating AIRR-seq data. Here, we review the current status of AIRR-seq, provide the results of our survey, and based on them, offer recommendations for developing AIRR-seq standards and controls, including future work.

High-Throughput Sequencing of Phage Display Libraries Reveals Parasitic Enrichment of Indel Mutants Caused by Amplification Bias

The combination of phage display technology with high-throughput sequencing enables in-depth analysis of library diversity and selection-driven dynamics. We applied short-read sequencing of the mutagenized region on focused display libraries of two homologous nucleic acid modification eraser proteins-AlkB and FTO-biopanned against methylated DNA. This revealed enriched genotypes with small indels and concomitant doubtful amino acid motifs within the FTO library. Nanopore sequencing of the entire display vector showed additional enrichment of large deletions overlooked by region-specific sequencing, and further impacted the interpretation of the obtained amino acid motifs. We could attribute enrichment of these corrupted clones to amplification bias due to arduous FTO display slowing down host cell growth as well as phage production. This amplification bias appeared to be stronger than affinity-based target selection. Recommendations are provided for proper sequence analysis of phage display data, which can improve motive discovery in libraries of proteins that are difficult to display.

High-efficacy, high-manufacturability human VH domain antibody therapeutics from transgenic sources

Interest in single-domain antibodies (sdAbs) stems from their unique structural/pronounced, hence therapeutically desirable, features. From the outset-as therapeutic modalities-human antibody heavy chain variable domains (VHs) attracted a particular attention compared with 'naturally-occurring' camelid and shark heavy-chain-only antibody variable domains (VHHs and VNARs, respectively) due to their perceived lack of immunogenicity. However, they have not quite lived up to their initial promise as the VH hits, primarily mined from synthetic VH phage display libraries, have too often been plagued with aggregation tendencies, low solubility and low affinity. Largely unexplored, synthetic camelized human VH display libraries appeared to have remediated the aggregation problem, but the low affinity of the VH hits still persisted, requiring undertaking additional, laborious affinity maturation steps to render VHs therapeutically feasible. A wholesome resolution has recently emerged with the development of non-canonical transgenic rodent antibody discovery platforms that appear to facilely and profusely generate high affinity, high solubility and aggregation-resistant human VHs.

Therapeutic Antibodies Targeting Potassium Ion Channels

Monoclonal antibodies combine specificity and high affinity binding with excellent pharmacokinetic properties and are rapidly being developed for a wide range of drug targets including clinically important potassium ion channels. Nonetheless, while therapeutic antibodies come with great promise, K⁺ channels represent particularly difficult targets for biologics development for a variety of reasons that include their dynamic structures and relatively small extracellular loops, their high degree of sequence conservation (leading to immune tolerance), and their generally low-level expression in vivo. The process is made all the more difficult when large numbers of antibody candidates must be screened for a given target, or when lead candidates fail to cross-react with orthologous channels in animal disease models due to their highly selective binding properties. While the number of antibodies targeting potassium channels in preclinical or clinical development is still modest, significant advances in the areas of protein expression and antibody screening are converging to open the field to an avalanche of new drugs. Here, the opportunities and constraints associated with the discovery of antibodies against K⁺ channels are discussed, with an emphasis on novel technologies that are opening the field to exciting new possibilities for biologics development.

Antibody display technologies: selecting the cream of the crop

Antibody display technologies enable the successful isolation of antigen-specific antibodies with therapeutic potential. The key feature that facilitates the selection of an antibody with prescribed properties is the coupling of the protein variant to its genetic information and is referred to as genotype phenotype coupling. There are several different platform technologies based on prokaryotic organisms as well as strategies employing higher eukaryotes. Among those, phage display is the most established system with more than a dozen of therapeutic antibodies approved for therapy that have been discovered or engineered using this approach. In recent years several other technologies gained a certain level of maturity, most strikingly mammalian display. In this review, we delineate the most important selection systems with respect to antibody generation with an emphasis on recent developments.

Dataset of next-generation sequence reads of nanobody clones in phage display library derived from Indian desert camel (Camelus dromedarius L.)

Next-generation sequences (NGS) dataset of nanobody (Nb) clones in a phage display library (PDL) is of immense value as it serves in many different ways, such as: i). estimating the library size, ii). improving selection and identification of Nbs, iii). informing about frequency of V gene families, diversity and length of CDRs, iv). high resolution analysis of natural and synthetic libraries, etc. [1], [2], [3]. We used a fraction of our previously constructed PDL of Nbs derived from an E. coli lipopolysaccharide-immunized Indian desert camel in order to obtain the dataset of NGS reads of Nbs. The cryo-preserved transformants library was revived to extract the Nb-encoding VHH (inserts)-pHEN4 (vector) DNA pool. The DNA sample was used for amplifying VHH pool by PCR [6]. The VHH amplicons band was gel-purified and subjected to NGS using Illumina MiSeqTM platform. 'Nextra XT micro V2 Index' kit was used for the Nb library DNA sample sequencing, with the adaptors: 'i7' (N706: TAGGCATG) and 'i5' (S517: GCGTAAGA). The raw data comprised of a total read count of 182146 (matched= 179591; unmatched=2555), with average read length of 130.33 bases and a total of 23.74 Mb. Of 179591 matched reads, 142004 were paired reads and 37587 broken paired reads. The raw data of NGS reads was submitted to NCBI Sequence Reads Archive accessible at URL: https://www.ncbi.nlm.nih.gov/Traces/study/?acc=PRJNA516512 (dataset ref. [7]), and after analysis deposited in Mendeley Datasets repository, which is accessible at URL: [https://data.mendeley.com/datasets/4rsz3snvk5/3] (dataset ref. [8]). The sequence reads were analyzed by bioinformatics tools [9], [10], [11], [12]. The assembled consensus contigs revealed Nb orthologs of diverse Ag-specificities, including those isolated by conventional panning and Sanger-sequenced functional Nbs. Contig 1 CDR1-3 matched to those of anti-Trypanosoma evansi RoTat1.2 variant surface glycoprotein (VSG), while Contig 2 CDR1-3 matched to those of anti-LPS Nb clones isolated from the library. Contig 3 was however incomplete and lacked CDR3. Despite lacking the depth, the NGS data is a useful guide for selection of antigen-specific Nbs from the library, as demonstrated by anti-T. evansi VSG Nbs, and provides templates for Nb-based diagnostic reagents and therapeutic agents.

Hybridoma technology: is it still useful?

The isolation of single monoclonal antibodies (mAbs) against a given antigen was only possible with the introduction of the hybridoma technology, which is based on the fusion of specific B lymphocytes with myeloma cells. Since then, several mAbs were described for therapeutic, diagnostic, and research purposes. Despite being an old technique with low complexity, hybridoma-based strategies have limitations that include the low efficiency on B lymphocyte-myeloma cell fusion step, and the need to use experimental animals. In face of that, several methods have been developed to improve mAb generation, ranging from changes in hybridoma technique to the advent of completely new technologies, such as the antibody phage display and the single B cell antibody ones. In this review, we discuss the hybridoma technology along with emerging mAb isolation approaches, taking into account their advantages and limitations. Finally, we explore the usefulness of the hybridoma technology nowadays.

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Hybridoma technology is the most popular technique to obtain monoclonal antibodies.

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Hybridoma technology variants include B cell and stereospecific targeting protocols.

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Phage display and single B cell methods are hybridoma technology alternatives.

Development of Therapeutic Antibodies and Modulating the Characteristics of Therapeutic Antibodies to Maximize the Therapeutic Efficacy

Monoclonal antibodies (mAb) have been used as therapeutic agents for various diseases, and immunoglobulin G (IgG) is mainly used among antibody isotypes due to its structural and functional properties. So far, regardless of the purpose of the therapeutic antibody, wildtype IgG has been mainly used, but recently, the engineered antibodies with various strategies according to the role of the therapeutic antibody have been used to maximize the therapeutic efficacy. In this review paper, first, the overall structural features and functional characteristics of antibody IgG, second, the old and new techniques for antibody discovery, and finally, several antibody engineering strategies for maximizing therapeutic efficacy according to the role of a therapeutic antibody will be introduced.

RNase H-dependent PCR enables highly specific amplification of antibody variable domains from single B-cells

Immunization-based antibody discovery platforms require robust and effective protocols for the amplification, cloning, expression, and screening of antibodies from large numbers of B-cells in order to effectively capture the diversity of an experienced Ig-repertoire. Multiplex PCR using a series of forward and reverse primers designed to recover antibodies from a range of different germline sequences is challenging because primer design requires the recovery of full length antibody sequences, low starting template concentrations, and the need for all the primers to function under the same PCR conditions. Here we demonstrate several advantages to incorporating RNase H2-dependent PCR (rh-PCR) into a high-throughput, antibody-discovery platform. Firstly, rh-PCR eliminated primer dimer synthesis to below detectable levels, thereby eliminating clones with a false positive antibody titer. Secondly, by increasing the specificity of PCR, the rh-PCR primers increased the recovery of cognate antibody variable regions from single B-cells, as well as downstream recombinant antibody titers. Finally, we demonstrate that rh-PCR primers provide a more homogeneous sample pool and greater sequence quality in a Next Generation Sequencing-based approach to obtaining DNA sequence information from large numbers of cloned antibody cognate pairs. Furthermore, the higher specificity of the rh-PCR primers allowed for a better match between native antibody germline sequences and the VL/VH fragments amplified from single B-cells.

Clonal array profiling of scFv-displaying phages for high-throughput discovery of affinity-matured antibody mutants

"Antibody-breeding" approach potentially generates therapeutic/diagnostic antibody mutants with greater performance than native antibodies. Therein, antibody fragments (e.g., single-chain Fv fragments; scFvs) with a variety of mutations are displayed on bacteriophage to generate diverse phage-antibody libraries. Rare clones with improved functions are then selected via panning against immobilized or tagged target antigens. However, this selection process often ended unsuccessful, mainly due to the biased propagation of phage-antibody clones and the competition with a large excess of undesirable clones with weaker affinities. To break radically from such panning-inherent problems, we developed a novel method, clonal array profiling of scFv-displaying phages (CAP), in which colonies of the initial bacterial libraries are examined one-by-one in microwells. Progenies of scFv-displaying phages generated are, if show sufficient affinity to target antigen, captured in the microwell via pre-coated antigen and detected using a luciferase-fused anti-phage scFv. The advantage of CAP was evidenced by its application with a small error-prone-PCR-based library (~ 10⁵ colonies) of anti-cortisol scFvs. Only two operations, each surveying only ~ 3% of the library (9,400 colonies), provided five mutants showing 32–63-fold improved K_a values (> 10¹⁰ M⁻¹), compared with the wild-type scFv (K_a = 3.8 × 10⁸ M⁻¹), none of which could be recovered via conventional panning procedures operated for the entire library.

Quantifying the nativeness of antibody sequences using long short-term memory networks

Antibodies often undergo substantial engineering en route to the generation of a therapeutic candidate with good developability properties. Characterization of antibody libraries has shown that retaining native-like sequence improves the overall quality of the library. Motivated by recent advances in deep learning, we developed a bi-directional long short-term memory (LSTM) network model to make use of the large amount of available antibody sequence information, and use this model to quantify the nativeness of antibody sequences. The model scores sequences for their similarity to naturally occurring antibodies, which can be used as a consideration during design and engineering of libraries. We demonstrate the performance of this approach by training a model on human antibody sequences and show that our method outperforms other approaches at distinguishing human antibodies from those of other species. We show the applicability of this method for the evaluation of synthesized antibody libraries and humanization of mouse antibodies.

Discovering Selected Antibodies From Deep-Sequenced Phage-Display Antibody Library Using ATTILA

Phage display is a powerful technique to select high-affinity antibodies for different purposes, including biopharmaceuticals. Next-generation sequencing (NGS) presented itself as a robust solution, making it possible to assess billions of sequences of the variable domains from selected sublibraries. Handling this process, a central difficulty is to find the selected clones. Here, we present the AutomaTed Tool For Immunoglobulin Analysis (ATTILA), a new tool to analyze and find the enriched variable domains throughout a biopanning experiment. The ATTILA is a workflow that combines publicly available tools and in-house programs and scripts to find the fold-change frequency of deeply sequenced amplicons generated from selected VH and VL domains. We analyzed the same human Fab library NGS data using ATTILA in 5 different experiments, as well as on 2 biopanning experiments regarding performance, accuracy, and output. These analyses proved to be suitable to assess library variability and to list the more enriched variable domains, as ATTILA provides a report with the amino acid sequence of each identified domain, along with its complementarity-determining regions (CDRs), germline classification, and fold change. Finally, the methods employed here demonstrated a suitable manner to combine amplicon generation and NGS data analysis to discover new monoclonal antibodies (mAbs).

Fully Human Antibodies for Malignant Pleural Mesothelioma Targeting

Immunotherapy is the most promising therapeutic approach against malignant pleural mesothelioma (MPM). Despite technological progress, the number of targetable antigens or specific antibodies is limited, thus hindering the full potential of recent therapeutic interventions. All possibilities of finding new targeting molecules must be exploited. The specificity of targeting is guaranteed by the use of monoclonal antibodies, while fully human antibodies are preferred, as they are functional and generate no neutralizing antibodies. The aim of this review is to appraise the latest advances in screening methods dedicated to the identification and harnessing of fully human antibodies. The scope of identifying useful molecules proceeds along two avenues, i.e., through the antigen-first or binding-first approaches. The first relies on screening human antibody libraries or plasma from immunized transgenic mice or humans to isolate binders to specific antigens. The latter takes advantage of specific binding to tumor cells of antibodies present in phage display libraries or in responders' plasma samples without prior knowledge of the antigens. Additionally, next-generation sequencing analysis of B-cell receptor repertoire pre- and post-therapy in memory B-cells from responders allows for the identification of clones expanded and matured upon treatment. Human antibodies identified can be subsequently reformatted to generate a plethora of therapeutics like antibody-drug conjugates, immunotoxins, and advanced cell-therapeutics such as chimeric antigen receptor-transduced T-cells.

Machine Learning-Guided Prediction of Antigen-Reactive In Silico Clonotypes Based on Changes in Clonal Abundance through Bio-Panning

c-Met is a promising target in cancer therapy for its intrinsic oncogenic properties. However, there are currently no c-Met-specific inhibitors available in the clinic. Antibodies blocking the interaction with its only known ligand, hepatocyte growth factor, and/or inducing receptor internalization have been clinically tested. To explore other therapeutic antibody mechanisms like Fc-mediated effector function, bispecific T cell engagement, and chimeric antigen T cell receptors, a diverse panel of antibodies is essential. We prepared a chicken immune scFv library, performed four rounds of bio-panning, obtained 641 clones using a high-throughput clonal retrieval system (TrueRepertoireTM, TR), and found 149 antigen-reactive scFv clones. We also prepared phagemid DNA before the start of bio-panning (round 0) and, after each round of bio-panning (round 1-4), performed next-generation sequencing of these five sets of phagemid DNA, and identified 860,207 HCDR3 clonotypes and 443,292 LCDR3 clonotypes along with their clonal abundance data. We then established a TR data set consisting of antigen reactivity for scFv clones found in TR analysis and the clonal abundance of their HCDR3 and LCDR3 clonotypes in five sets of phagemid DNA. Using the TR data set, a random forest machine learning algorithm was trained to predict the binding properties of in silico HCDR3 and LCDR3 clonotypes. Subsequently, we synthesized 40 HCDR3 and 40 LCDR3 clonotypes predicted to be antigen reactive (AR) and constructed a phage-displayed scFv library called the AR library. In parallel, we also prepared an antigen non-reactive (NR) library using 10 HCDR3 and 10 LCDR3 clonotypes predicted to be NR. After a single round of bio-panning, we screened 96 randomly-selected phage clones from the AR library and found out 14 AR scFv clones consisting of 5 HCDR3 and 11 LCDR3 AR clonotypes. We also screened 96 randomly-selected phage clones from the NR library, but did not identify any AR clones. In summary, machine learning algorithms can provide a method for identifying AR antibodies, which allows for the characterization of diverse antibody libraries inaccessible by traditional methods.

Adaptive immune receptor repertoires, an overview of this exciting field

The adaptive immune response in jawed vertebrates relies on the huge diversity and specificity of the B cell and T cell antigen receptors, the immunoglobulins (IG) or antibodies and the T cell receptors (TR), respectively. The high level of diversity has represented a barrier to a comprehensive analysis of the adaptive immune response before the emergence of high-throughput sequencing (HTS) technologies. The size and complexity of HTS data requires the generation of novel computational and analytical approaches, which are transforming how the adaptive immune responses are deciphered to understand the clonal dynamics and properties of antigen-specific B and T cells in response to different kind of antigens. This exciting and rapidly evolving field is not only impacting human and clinical immunology but also comparative immunology. We are now closer to understanding the evolution of adaptive immune response in jawed vertebrates. This review provides an overview about classical and current strategies developed to assess the IG/TR diversity and their applications in basic and clinical immunology.