By-passing in vitro screening--next generation sequencing technologies applied to antibody display and in silico candidate selection

Phage Display as a Medium for Target Therapy Based Drug Discovery, Review and Update

Phage libraries are now amongst the most prominent approaches for the identification of high-affinity antibodies/peptides from billions of displayed phages in a specific library through the biopanning process. Due to its ability to discover potential therapeutic candidates that bind specifically to targets, phage display has gained considerable attention in targeted therapy. Using this approach, peptides with high-affinity and specificity can be identified for potential therapeutic or diagnostic use. Furthermore, phage libraries can be used to rapidly screen and identify novel antibodies to develop immunotherapeutics. The Food and Drug Administration (FDA) has approved several phage display-derived peptides and antibodies for the treatment of different diseases. In the current review, we provided a comprehensive insight into the role of phage display-derived peptides and antibodies in the treatment of different diseases including cancers, infectious diseases and neurological disorders. We also explored the applications of phage display in targeted drug delivery, gene therapy, and CAR T-cell.

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.

Machine learning to predict continuous protein properties from binary cell sorting data and map unseen sequence space

Proteins are a diverse class of biomolecules responsible for wide-ranging cellular functions, from catalyzing reactions to recognizing pathogens. The ability to evolve proteins rapidly and inexpensively toward improved properties is a common objective for protein engineers. Powerful high-throughput methods like fluorescent activated cell sorting and next-generation sequencing have dramatically improved directed evolution experiments. However, it is unclear how to best leverage these data to characterize protein fitness landscapes more completely and identify lead candidates. In this work, we develop a simple yet powerful framework to improve protein optimization by predicting continuous protein properties from simple directed evolution experiments using interpretable, linear machine learning models. Importantly, we find that these models, which use data from simple but imprecise experimental estimates of protein fitness, have predictive capabilities that approach more precise but expensive data. Evaluated across five diverse protein engineering tasks, continuous properties are consistently predicted from readily available deep sequencing data, demonstrating that protein fitness space can be reasonably well modeled by linear relationships among sequence mutations. To prospectively test the utility of this approach, we generated a library of stapled peptides and applied the framework to predict affinity and specificity from simple cell sorting data. We then coupled integer linear programming, a method to optimize protein fitness from linear weights, with mutation scores from machine learning to identify variants in unseen sequence space that have improved and co-optimal properties. This approach represents a versatile tool for improved analysis and identification of protein variants across many domains of protein engineering.

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.

Genotyped functional screening of soluble Fab clones enables in-depth analysis of mutation effects

Monoclonal antibodies (mAbs) and their fragments are widely used in therapeutics, diagnostics and basic research. Although display methods such as phage display offer high-throughput, affinities of individual antibodies need to be accurately measured in soluble format. We have developed a screening platform capable of providing genotyped functional data from a total of 9216 soluble, individual antigen binding fragment (Fab) clones by employing next-generation sequencing (NGS) with hierarchical indexing. Full-length, paired variable domain sequences (VL-VH) are linked to functional screening data, enabling in-depth analysis of mutation effects. The platform was applied to four phage display-selected scFv/Fab screening projects and one site-saturation VH affinity maturation project. Genotyped functional screening simultaneously enabled the identification of affinity improving mutations in the VH domain of Fab 49A3 recognizing Dengue virus non-structural protein 1 (NS1) serotype 2 and informed on VH residue positions which cannot be changed from wild-type without decreasing the affinity. Genotype-based identification revealed to us the extent of intraclonal signal variance inherent to single point screening data, a phenomenon often overlooked in the field. Moreover, genotyped screening eliminated the redundant selection of identical genotypes for further study and provided a new analysis tool to evaluate the success of phage display selections and remaining clonal diversity in the screened repertoires.

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.

Combination of Experimental and Bioinformatic Approaches for Identification of Immunologically Relevant Protein-Peptide Interactions

Protein-peptide interactions are an essential player in cellular processes and, thus, of great interest as potential therapeutic agents. However, identifying the protein's interacting surface has been shown to be a challenging task. Here, we present a methodology for protein-peptide interaction identification, implementing phage panning, next-generation sequencing and bioinformatic analysis. One of the uses of this methodology is identification of allergen epitopes, especially suitable for globular inhaled and venom allergens, where their binding capability is determined by the allergen's conformation, meaning their interaction cannot be properly studied when denatured. A Ph.D. commercial system based on the M13 phage vector was used for the panning process. Utilization of various bioinformatic tools, such as PuLSE, SAROTUP, MEME, Hammock and Pepitope, allowed us to evaluate a large amount of obtained data. Using the described methodology, we identified three peptide clusters representing potential epitopes on the major wasp venom allergen Ves v 5.

One-Pot Droplet RT-OE-PCR for the Generation of Natively Paired Antibody Immune Libraries

Classical yeast surface display (YSD) antibody immune libraries are generated by a separate amplification of heavy- and light-chain antibody variable regions (VH and VL, respectively) and subsequent random recombination during the molecular cloning procedure. However, each B cell receptor comprises a unique VH-VL combination, which has been selected and affinity matured in vivo for optimal stability and antigen binding. Thus, the native variable chain pairing is important for the functioning and biophysical properties of the respective antibody. Herein, we present a method for the amplification of cognate VH-VL sequences, compatible with both next-generation sequencing (NGS) and YSD library cloning. We employ a single B cell encapsulation in water-in-oil droplets, followed by a one-pot reverse transcription overlap extension PCR (RT-OE-PCR), resulting in a paired VH-VL repertoire from more than a million B cells in a single day.

Identification of New Antibodies Targeting Tumor Cell Surface Antigens by Phage Display

The majority of therapeutic antibodies, bispecific antibodies, and chimeric antigen receptor (CAR) T cells in cancer therapy are based on an antibody or antibody fragment that specifically binds a target present on the surface of a tumor cell. Suitable antigens that can be used for immunotherapy are ideally tumor-specific or tumor-associated and stably expressed on the tumor cell. The identification of new target structures to further optimize immunotherapies could be realized by comparing healthy and tumor cells using "omics" methods to select promising proteins. However, differences in post-translational modifications and structural alterations that can be present on the tumor cell surface are difficult to identify or even not accessible by these techniques. In this chapter, we describe an alternative approach to potentially identify antibodies targeting novel tumor-associated antigens (TAA) or epitopes by using cellular screening and phage display of antibody libraries. Isolated antibody fragments can be further converted into chimeric IgG or other antibody formats to investigate the anti-tumor effector functions and finally identify and characterize the respective antigen.

Generation of synthetic antibody fragments with optimal complementarity determining region lengths for Notch-1 recognition

Synthetic antibodies have been engineered against a wide variety of antigens with desirable biophysical, biochemical, and pharmacological properties. Here, we describe the generation and characterization of synthetic antigen-binding fragments (Fabs) against Notch-1. Three single-framework synthetic Fab libraries, named S, F, and modified-F, were screened against the recombinant human Notch-1 extracellular domain using phage display. These libraries were built on a modified trastuzumab framework, containing two or four diversified complementarity-determining regions (CDRs) and different CDR diversity designs. In total, 12 Notch-1 Fabs were generated with 10 different CDRH3 lengths. These Fabs possessed a high affinity for Notch-1 (sub-nM to mid-nM K_Dapp values) and exhibited different binding profiles (mono-, bi-or tri-specific) toward Notch/Jagged receptors. Importantly, we showed that screening focused diversity libraries, implementing next-generation sequencing approaches, and fine-tuning the CDR length diversity provided improved binding solutions for Notch-1 recognition. These findings have implications for antibody library design and antibody phage display.

Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space

Therapeutic antibody development requires selection and engineering of molecules with high affinity and other drug-like biophysical properties. Co-optimization of multiple antibody properties remains a difficult and time-consuming process that impedes drug development. Here we evaluate the use of machine learning to simplify antibody co-optimization for a clinical-stage antibody (emibetuzumab) that displays high levels of both on-target (antigen) and off-target (non-specific) binding. We mutate sites in the antibody complementarity-determining regions, sort the antibody libraries for high and low levels of affinity and non-specific binding, and deep sequence the enriched libraries. Interestingly, machine learning models trained on datasets with binary labels enable predictions of continuous metrics that are strongly correlated with antibody affinity and non-specific binding. These models illustrate strong tradeoffs between these two properties, as increases in affinity along the co-optimal (Pareto) frontier require progressive reductions in specificity. Notably, models trained with deep learning features enable prediction of novel antibody mutations that co-optimize affinity and specificity beyond what is possible for the original antibody library. These findings demonstrate the power of machine learning models to greatly expand the exploration of novel antibody sequence space and accelerate the development of highly potent, drug-like antibodies.

Identification of New Antibodies Targeting Malignant Plasma Cells for Immunotherapy by Next-Generation Sequencing-Assisted Phage Display

To identify new antibodies for the treatment of plasma cell disorders including multiple myeloma (MM), a single-chain Fragment variable (scFv) antibody library was generated by immunizing mice with patient-derived malignant plasma cells. To enrich antibodies binding myeloma antigens, phage display with cellular panning was performed. After depleting the immune library with leukocytes of healthy donors, selection of antibodies was done with L-363 plasma cell line in two consecutive panning rounds. Monitoring the antibodies’ enrichment throughout the panning by next-generation sequencing (NGS) identified several promising candidates. Initially, 41 unique scFv antibodies evolving from different B cell clones were selected. Nine of these antibodies strongly binding to myeloma cells and weakly binding to peripheral blood mononuclear cells (PBMC) were characterized. Using stably transfected Chinese hamster ovary cells expressing individual myeloma-associated antigens revealed that two antibodies bind CD38 and intercellular adhesion molecule-1 (ICAM-1), respectively, and 7 antibodies target yet unknown antigens. To evaluate the therapeutic potential of our new antibodies, in a first proof-of-concept study the CD38 binding scFv phage antibody was converted into a chimeric IgG1. Further analyses revealed that #5-CD38-IgG1 shared an overlapping epitope with daratumumab and isatuximab and had potent anti-myeloma activity comparable to the two clinically approved CD38 antibodies. These results indicate that by phage display and deep sequencing, new antibodies with therapeutic potential for MM immunotherapy can be identified.

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.

A Simple Whole-Plasmid PCR Method to Construct High-Diversity Synthetic Phage Display Libraries

Phage display technology utilises peptide and antibody libraries with very high diversities to select ligands with specific binding properties. The production of such libraries can be labour intensive and technically challenging and whilst there are commercial sources of libraries, the exploitation of the resulting binders is constrained by ownership of the libraries. Here, a peptide library of ~ 1 × 10⁹ variants for display on gene VIII was produced alongside three VHH antibody libraries with similar diversity, where 12mer, 16mer or 21mer CDR3s were introduced into the highly stable cAbBCII10 scaffold displayed on gene III. The cloning strategy used a simple whole-plasmid PCR method and type IIS restriction enzyme assembly that facilitate the seamless insertion of diversity into any suitable phage coat protein or antibody scaffold. This method reproducibly produced 1 × 10⁹ variants from just 10 transformations and the four libraries had relatively low bias with 82 to 86% of all sequences present as single copies. The functionality of both peptide and antibody libraries were demonstrated by selection of ligands with specific binding properties by biopanning. The peptide library was used to epitope map a monoclonal antibody. The VHH libraries were pooled and used to select an antibody to recombinant human collagen type 1.

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.

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.

CellectSeq: In silico discovery of antibodies targeting integral membrane proteins combining in situ selections and next-generation sequencing

Synthetic antibody (Ab) technologies are efficient and cost-effective platforms for the generation of monoclonal Abs against human antigens. Yet, they typically depend on purified proteins, which exclude integral membrane proteins that require the lipid bilayers to support their native structure and function. Here, we present an Ab discovery strategy, termed CellectSeq, for targeting integral membrane proteins on native cells in complex environment. As proof of concept, we targeted three transmembrane proteins linked to cancer, tetraspanin CD151, carbonic anhydrase 9, and integrin-α11. First, we performed in situ cell-based selections to enrich phage-displayed synthetic Ab pools for antigen-specific binders. Then, we designed next-generation sequencing procedures to explore Ab diversities and abundances. Finally, we developed motif-based scoring and sequencing error-filtering algorithms for the comprehensive interrogation of next-generation sequencing pools to identify Abs with high diversities and specificities, even at extremely low abundances, which are very difficult to identify using manual sampling or sequence abundances.

Combinatorial mutagenesis with alternative CDR-L1 and -H2 loop lengths contributes to affinity maturation of antibodies

Loop length variation in the complementary determining regions (CDRs) 1 and 2 encoded in germline variable antibody genes provides structural diversity in naïve antibody libraries. In synthetic single framework libraries the parental CDR-1 and CDR-2 length is typically unchanged and alternative lengths are provided only at CDR-3 sites. Based on an analysis of the germline repertoire and structure-solved anti-hapten and anti-peptide antibodies, we introduced combinatorial diversity with alternative loop lengths into the CDR-L1, CDR-L3 and CDR-H2 loops of anti-digoxigenin and anti-microcystin-LR single chain Fv fragments (scFvs) sharing human IGKV3-20/IGHV3-23 frameworks. The libraries were phage display selected for folding and affinity, and analysed by single clone screening and deep sequencing. Among microcystin-LR binders the most frequently encountered alternative loop lengths were one amino acid shorter (6 aa) and four amino acids longer (11 aa) CDR-L1 loops leading up to 17- and 28-fold improved affinity, respectively. Among digoxigenin binders, 2 amino acids longer (10 aa) CDR-H2 loops were strongly enriched, but affinity improved anti-digoxigenin scFvs were also encountered with 7 aa CDR-H2 and 11 aa CDR-L1 loops. Despite the fact that CDR-L3 loop length variants were not specifically enriched in selections, one clone with 22-fold improved digoxigenin binding affinity was identified containing a 2 residues longer (10 aa) CDR-L3 loop. Based on our results the IGKV3-20/IGHV3-23 scaffold tolerates loop length variation, particularly in CDR-L1 and CDR-H2 loops, without compromising antibody stability, laying the foundation for developing novel synthetic antibody libraries with loop length combinations not existing in the natural human Ig gene repertoire.

Immune Literacy: Reading, Writing, and Editing Adaptive Immunity

Advances in reading, writing, and editing DNA are providing unprecedented insights into the complexity of immunological systems. This combination of systems and synthetic biology methods is enabling the quantitative and precise understanding of molecular recognition in adaptive immunity, thus providing a framework for reprogramming immune responses for translational medicine. In this review, we will highlight state-of-the-art methods such as immune repertoire sequencing, immunoinformatics, and immunogenomic engineering and their application toward adaptive immunity. We showcase novel and interdisciplinary approaches that have the promise of transforming the design and breadth of molecular and cellular immunotherapies.

Protective anti-prion antibodies in human immunoglobulin repertoires

Prion immunotherapy may hold great potential, but antibodies against certain PrP epitopes can be neurotoxic. Here, we identified > 6,000 PrP-binding antibodies in a synthetic human Fab phage display library, 49 of which we characterized in detail. Antibodies directed against the flexible tail of PrP conferred neuroprotection against infectious prions. We then mined published repertoires of circulating B cells from healthy humans and found antibodies similar to the protective phage-derived antibodies. When expressed recombinantly, these antibodies exhibited anti-PrP reactivity. Furthermore, we surveyed 48,718 samples from 37,894 hospital patients for the presence of anti-PrP IgGs and found 21 high-titer individuals. The clinical files of these individuals did not reveal any enrichment of specific pathologies, suggesting that anti-PrP autoimmunity is innocuous. The existence of anti-prion antibodies in unbiased human immunological repertoires suggests that they might clear nascent prions early in life. Combined with the reported lack of such antibodies in carriers of disease-associated PRNP mutations, this suggests a link to the low incidence of spontaneous prion diseases in human populations.

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).

How repertoire data are changing antibody science

Antibodies are vital proteins of the immune system that recognize potentially harmful molecules and initiate their removal. Mammals can efficiently create vast numbers of antibodies with different sequences capable of binding to any antigen with high affinity and specificity. Because they can be developed to bind to many disease agents, antibodies can be used as therapeutics. In an organism, after antigen exposure, antibodies specific to that antigen are enriched through clonal selection, expansion, and somatic hypermutation. The antibodies present in an organism therefore report on its immune status, describe its innate ability to deal with harmful substances, and reveal how it has previously responded. Next-generation sequencing technologies are being increasingly used to query the antibody, or B-cell receptor (BCR), sequence repertoire, and the amount of BCR data in public repositories is growing. The Observed Antibody Space database, for example, currently contains over a billion sequences from 68 different studies. Repertoires are available that represent both the naive state (i.e. antigen-inexperienced) and that after immunization. This wealth of data has created opportunities to learn more about our immune system. In this review, we discuss the many ways in which BCR repertoire data have been or could be exploited. We highlight its utility for providing insights into how the naive immune repertoire is generated and how it responds to antigens. We also consider how structural information can be used to enhance these data and may lead to more accurate depictions of the sequence space and to applications in the discovery of new therapeutics.

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.

Development and Application of Computational Methods in Phage Display Technology

BACKGROUND

Phage display is a powerful and versatile technology for the identification of peptide ligands binding to multiple targets, which has been successfully employed in various fields, such as diagnostics and therapeutics, drug-delivery and material science. The integration of next generation sequencing technology with phage display makes this methodology more productive. With the widespread use of this technique and the fast accumulation of phage display data, databases for these data and computational methods have become an indispensable part in this community. This review aims to summarize and discuss recent progress in the development and application of computational methods in the field of phage display.

METHODS

We undertook a comprehensive search of bioinformatics resources and computational methods for phage display data via Google Scholar and PubMed. The methods and tools were further divided into different categories according to their uses.

RESULTS

We described seven special or relevant databases for phage display data, which provided an evidence-based source for phage display researchers to clean their biopanning results. These databases can identify and report possible target-unrelated peptides (TUPs), thereby excluding false-positive data from peptides obtained from phage display screening experiments. More than 20 computational methods for analyzing biopanning data were also reviewed. These methods were classified into computational methods for reporting TUPs, for predicting epitopes and for analyzing next generation phage display data.

CONCLUSION

The current bioinformatics archives, methods and tools reviewed here have benefitted the biopanning community. To develop better or new computational tools, some promising directions are also discussed.

High-Throughput Generation of In Silico Derived Synthetic Antibodies via One-step Enzymatic DNA Assembly of Fragments

Phage-display technology offers robust methods for isolating antibody (Ab) molecules with specificity for different target antigens. Recent advancements couple Ab selections with in silico strategies, such as predictive computational models or next-generation sequencing metadata analysis of Ab selections. These advancements result in enhanced Ab clonal diversities with potential for enlarged epitope coverage of the target antigen. A current limitation however, is that de novo Ab sequences must undergo DNA gene synthesis, and subsequent expression as Ab proteins for downstream validations. Due to the high costs and time for commercially generating large sets of DNA genes, we report a high-throughput platform for the synthesis of in silico derived Ab clones. As a proof of concept we demonstrate the simultaneous synthesis of 96 unique Abs with varied lengths and complementary determining region compositions. Each of the 96 Ab clones undergoes a one-step enzymatic assembly of distinct DNA fragments that combine into a circularized Fab expression plasmid. This strategy allows for the rapid and efficient synthesis of 96 DNA constructs in a 3 day window, and exhibits high percentage fidelity-greater than 93%. Accordingly, the synthesis of Ab DNA constructs as Fab expression plasmids allow for rapid execution of downstream Ab protein validations, with potential for implementation into high-throughput Ab protein characterization pipelines. Altogether, the platform presented here proves rapid and also cost-effective, which is important for labs with limited resources, since it utilizes standard laboratory equipment and molecular reagents.

Multifaceted antibodies development against synthetic α-dystroglycan mucin glycopeptide as promising tools for dystroglycanopathies diagnostic

Dystroglycanopathies are diseases characterized by progressive muscular degeneration and impairment of patient's quality of life. They are associated with altered glycosylation of the dystrophin-glycoprotein (DGC) complex components, such as α-dystroglycan (α-DG), fundamental in the structural and functional stability of the muscle fiber. The diagnosis of dystroglycanopathies is currently based on the observation of clinical manifestations, muscle biopsies and enzymatic measures, and the available monoclonal antibodies are not specific for the dystrophic hypoglycosylated muscle condition. Thus, modified α-DG mucins have been considered potential targets for the development of new diagnostic strategies toward these diseases. In this context, this work describes the synthesis of the hypoglycosylated α-DG mimetic glycopeptide NHAc-Gly-Pro-Thr-Val-Thr[αMan]-Ile-Arg-Gly-BSA (1) as a potential tool for the development of novel antibodies applicable to dystroglycanopathies diagnosis. Glycopeptide 1 was used for the development of polyclonal antibodies and recombinant monoclonal antibodies by Phage Display technology. Accordingly, polyclonal antibodies were reactive to glycopeptide 1, which enables the application of anti-glycopeptide 1 antibodies in immune reactive assays targeting hypoglycosylated α-DG. Regarding monoclonal antibodies, for the first time variable heavy (VH) and variable light (VL) immunoglobulin domains were selected by Phage Display, identified by NGS and described by in silico analysis. The best-characterized VH and VL domains were cloned, expressed in E. coli Shuffle T7 cells, and used to construct a single chain fragment variable that recognized the Glycopeptide 1 (GpαDG1 scFv). Molecular modelling of glycopeptide 1 and GpαDG1 scFv suggested that their interaction occurs through hydrogen bonds and hydrophobic contacts involving amino acids from scFv (I51, Y33, S229, Y235, and P233) and R8 and α-mannose from Glycopeptide 1.

Next-generation sequencing-guided identification and reconstruction of antibody CDR combinations from phage selection outputs

Next-generation sequencing (NGS) technologies have been employed in several phage display platforms for analyzing natural and synthetic antibody sequences and for identifying and reconstructing single-chain variable fragments (scFv) and antigen-binding fragments (Fab) not found by conventional ELISA screens. In this work, we developed an NGS-assisted antibody discovery platform by integrating phage-displayed, single-framework, synthetic Fab libraries. Due to limitations in attainable read and amplicon lengths, NGS analysis of Fab libraries and selection outputs is usually restricted to either V_H or V_L. Since this information alone is not sufficient for high-throughput reconstruction of Fabs, we developed a rapid and simple method for linking and sequencing all diversified CDRs in phage Fab pools. Our method resulted in a reliable and straightforward platform for converting NGS information into Fab clones. We used our NGS-assisted Fab reconstruction method to recover low-frequency rare clones from phage selection outputs. While previous studies chose rare clones for rescue based on their relative frequencies in sequencing outputs, we chose rare clones for reconstruction from less-frequent CDRH3 lengths. In some cases, reconstructed rare clones (frequency ∼0.1%) showed higher affinity and better specificity than high-frequency top clones identified by Sanger sequencing, highlighting the significance of NGS-based approaches in synthetic antibody discovery.

Tools and systems for evolutionary engineering of biomolecules and microorganisms

Evolutionary approaches have been providing solutions to various bioengineering challenges in an efficient manner. In addition to traditional adaptive laboratory evolution and directed evolution, recent advances in synthetic biology and fluidic systems have opened a new era of evolutionary engineering. Synthetic genetic circuits have been created to control mutagenesis and enable screening of various phenotypes, particularly metabolite production. Fluidic systems can be used for high-throughput screening and multiplexed continuous cultivation of microorganisms. Moreover, continuous directed evolution has been achieved by combining all the steps of evolutionary engineering. Overall, modern tools and systems for evolutionary engineering can be used to establish the artificial equivalent to natural evolution for various research applications.

Arming Filamentous Bacteriophage, a Nature-Made Nanoparticle, for New Vaccine and Immunotherapeutic Strategies

The pharmaceutical use of bacteriophages as safe and inexpensive therapeutic tools is collecting renewed interest. The use of lytic phages to fight antibiotic-resistant bacterial strains is pursued in academic and industrial projects and is the object of several clinical trials. On the other hand, filamentous bacteriophages used for the phage display technology can also have diagnostic and therapeutic applications. Filamentous bacteriophages are nature-made nanoparticles useful for their size, the capability to enter blood vessels, and the capacity of high-density antigen expression. In the last decades, our laboratory focused its efforts in the study of antigen delivery strategies based on the filamentous bacteriophage 'fd', able to trigger all arms of the immune response, with particular emphasis on the ability of the MHC class I restricted antigenic determinants displayed on phages to induce strong and protective cytotoxic responses. We showed that fd bacteriophages, engineered to target mouse dendritic cells (DCs), activate innate and adaptive responses without the need of exogenous adjuvants, and more recently, we described the display of immunologically active lipids. In this review, we will provide an overview of the reported applications of the bacteriophage carriers and describe the advantages of exploiting this technology for delivery strategies.

Deep Mining of Complex Antibody Phage Pools Generated by Cell Panning Enables Discovery of Rare Antibodies Binding New Targets and Epitopes

Phage display technology is a common approach for discovery of therapeutic antibodies. Drug candidates are typically isolated in two steps: First, a pool of antibodies is enriched through consecutive rounds of selection on a target antigen, and then individual clones are characterized in a screening procedure. When whole cells are used as targets, as in phenotypic discovery, the output phage pool typically contains thousands of antibodies, binding, in theory, hundreds of different cell surface receptors. Clonal expansion throughout the phage display enrichment process is affected by multiple factors resulting in extremely complex output phage pools where a few antibodies are highly abundant and the majority is very rare. This is a huge challenge in the screening where only a fraction of the antibodies can be tested using a conventional binding analysis, identifying mainly the most abundant clones typically binding only one or a few targets. As the expected number of antibodies and specificities in the pool is much higher, complementing methods, to reach deeper into the pool, are required, called deep mining methods. In this study, four deep mining methods were evaluated: 1) isolation of rare sub-pools of specific antibodies through selection on recombinant proteins predicted to be expressed on the target cells, 2) isolation of a sub-pool enriched for antibodies of unknown specificities through depletion of the primary phage pool on recombinant proteins corresponding to receptors known to generate many binders, 3) isolation of a sub-pool enriched for antibodies through selection on cells blocked with antibodies dominating the primary phage pool, and 4) next-generation sequencing-based analysis of isolated antibody pools followed by antibody gene synthesis and production of rare but enriched clones. We demonstrate that antibodies binding new targets and epitopes, not discovered through screening alone, can be discovered using described deep mining methods. Overall, we demonstrate the complexity of phage pools generated through selection on cells and show that a combination of conventional screening and deep mining methods are needed to fully utilize such pools. Deep mining will be important in future phenotypic antibody drug discovery efforts to increase the diversity of identified antibodies and targets.

SAROTUP: a suite of tools for finding potential target-unrelated peptides from phage display data

SAROTUP (Scanner And Reporter Of Target-Unrelated Peptides) 3.1 is a significant upgrade to the widely used SAROTUP web server for the rapid identification of target-unrelated peptides (TUPs) in phage display data. At present, SAROTUP has gathered a suite of tools for finding potential TUPs and other purposes. Besides the TUPScan, the motif-based tool, and three tools based on the BDB database, i.e., MimoScan, MimoSearch, and MimoBlast, three predictors based on support vector machine, i.e., PhD7Faster, SABinder and PSBinder, are integrated into SAROTUP. The current version of SAROTUP contains 27 TUP motifs and 823 TUP sequences. We also developed the standalone SAROTUP application with graphical user interface (GUI) and command line versions for processing deep sequencing phage display data and distributed it as an open source package, which can perform perfectly locally on almost all systems that support C++ with little or no modification. The web interfaces of SAROTUP have also been redesigned to be more self-evident and user-friendly. The latest version of SAROTUP is freely available at http://i.uestc.edu.cn/sarotup3.

High-throughput retrieval of physical DNA for NGS-identifiable clones in phage display library

In antibody discovery, in-depth analysis of an antibody library and high-throughput retrieval of clones in the library are crucial to identifying and exploiting rare clones with different properties. However, existing methods have technical limitations, such as low process throughput from the laborious cloning process and waste of the phenotypic screening capacity from unnecessary repetitive tests on the dominant clones. To overcome the limitations, we developed a new high-throughput platform for the identification and retrieval of clones in the library, TrueRepertoire™. This new platform provides highly accurate sequences of the clones with linkage information between heavy and light chains of the antibody fragment. Additionally, the physical DNA of clones can be retrieved in high throughput based on the sequence information. We validated the high accuracy of the sequences and demonstrated that there is no platform-specific bias. Moreover, the applicability of TrueRepertoire™ was demonstrated by a phage-displayed single-chain variable fragment library targeting human hepatocyte growth factor protein.

Nucleic Acid-Barcoding Technologies: Converting DNA Sequencing into a Broad-Spectrum Molecular Counter

The emergence of high-throughput DNA sequencing technologies sparked a revolution in the field of genomics that has rippled into many branches of the life and physical sciences. The remarkable sensitivity, specificity, throughput, and multiplexing capacity that are inherent to parallel DNA sequencing have since motivated its use as a broad-spectrum molecular counter. A key aspect of extrapolating DNA sequencing to non-traditional applications is the need to append nucleic-acid barcodes to entities of interest. In this review, we describe the chemical and biochemical approaches that have enabled nucleic-acid barcoding of proteinaceous and non-proteinaceous materials and provide examples of downstream technologies that have been made possible by DNA-encoded molecules. As commercially available high-throughput sequencers were first released less than 15 years ago, we believe related applications will continue to mature and close by proposing new frontiers to support this assertion.

Round optimization for improved discovery of native bispecific antibodies

The assembly of bispecific antibodies (bsAb) that retain the structure of a standard IgG can be challenging as the correct pairing of the different heavy and light chains has to be ensured while unwanted side products kept to a minimum. The use of antibodies sharing a common chain facilitates assembly of such bsAb formats but requires additional efforts during the initial discovery phase. We have developed a native bsAb format called κλ body based on antibodies that, while being specific for different antigens, share the same heavy chain. Such antibodies can readily be isolated from antibody libraries incorporating a single VH combined with light chain diversity. However, in order to improve the discovery process of such fixed VH antibodies, we developed a method to optimize populations of light chains by recovering and shuffling CDRL3 sequences that have been enriched for antigen binding by phage display selection. This approach allowed for the isolation of a more diverse and potent panel of antibodies blocking the interaction between PD-1 and PD-L1 when compared to our standard in vitro selection approach, thus providing better building blocks for subsequent bsAb generation.

Therapeutic Antibody Discovery in Infectious Diseases Using Single-Cell Analysis

Since the discovery of mouse hybridoma technology by Kohler and Milstein in 1975, significant progress has been made in monoclonal antibody production. Advances in B cell immortalization and phage display technologies have generated a myriad of valuable monoclonal antibodies for diagnosis and treatment. Technological breakthroughs in various fields of 'omics have shed crucial insights into cellular heterogeneity of a biological system in which the functional individuality of a single cell must be considered. Based on this important concept, remarkable discoveries in single-cell analysis have made in identifying and isolating functional B cells that produce beneficial therapeutic monoclonal antibodies. In this review, we will discuss three traditional methods of antibody discovery. Recent technological platforms for single-cell antibody discovery will be reviewed. We will discuss the application of the single-cell analysis in finding therapeutic antibodies for human immunodeficiency virus and emerging Zika arbovirus.

Primer Design and Inverse PCR on Yeast Display Antibody Selection Outputs

The display of antibodies on the surface of Saccharomyces cerevisiae cells enables the high-throughput and precise selection of specific binders for the target antigen. The recent implementation of next-generation sequencing (NGS) to antibody display screening provides a complete picture of the entire selected polyclonal population. As such, NGS overcomes the limitations of random clones screening, but it comes with two main limitations: (1) depending upon the platform, the sequencing is usually restricted to the variable heavy chain domain complementary determining region 3 (HCDR3), or VH gene, and does not provide additional information on the rest of the antibody gene, including the VL; and (2) the sequence-identified clones are not physically available for validation. Here, we describe a rapid and effective protocol based on an inverse-PCR method to recover specific antibody clones based on their HCDR3 sequence from a yeast display selection output.

Phage display peptide libraries: deviations from randomness and correctives

Peptide-expressing phage display libraries are widely used for the interrogation of antibodies. Affinity selected peptides are then analyzed to discover epitope mimetics, or are subjected to computational algorithms for epitope prediction. A critical assumption for these applications is the random representation of amino acids in the initial naïve peptide library. In a previous study, we implemented next generation sequencing to evaluate a naïve library and discovered severe deviations from randomness in UAG codon over-representation as well as in high G phosphoramidite abundance causing amino acid distribution biases. In this study, we demonstrate that the UAG over-representation can be attributed to the burden imposed on the phage upon the assembly of the recombinant Protein 8 subunits. This was corrected by constructing the libraries using supE44-containing bacteria which suppress the UAG driven abortive termination. We also demonstrate that the overabundance of G stems from variant synthesis-efficiency and can be corrected using compensating oligonucleotide-mixtures calibrated by mass spectroscopy. Construction of libraries implementing these correctives results in markedly improved libraries that display random distribution of amino acids, thus ensuring that enriched peptides obtained in biopanning represent a genuine selection event, a fundamental assumption for phage display applications.

Many Routes to an Antibody Heavy-Chain CDR3: Necessary, Yet Insufficient, for Specific Binding

Because of its great potential for diversity, the immunoglobulin heavy-chain complementarity-determining region 3 (HCDR3) is taken as an antibody molecule’s most important component in conferring binding activity and specificity. For this reason, HCDR3s have been used as unique identifiers to investigate adaptive immune responses in vivo and to characterize in vitro selection outputs where display systems were employed. Here, we show that many different HCDR3s can be identified within a target-specific antibody population after in vitro selection. For each identified HCDR3, a number of different antibodies bearing differences elsewhere can be found. In such selected populations, all antibodies with the same HCDR3 recognize the target, albeit at different affinities. In contrast, within unselected populations, the majority of antibodies with the same HCDR3 sequence do not bind the target. In one HCDR3 examined in depth, all target-specific antibodies were derived from the same VDJ rearrangement, while non-binding antibodies with the same HCDR3 were derived from many different V and D gene rearrangements. Careful examination of previously published in vivo datasets reveals that HCDR3s shared between, and within, different individuals can also originate from rearrangements of different V and D genes, with up to 26 different rearrangements yielding the same identical HCDR3 sequence. On the basis of these observations, we conclude that the same HCDR3 can be generated by many different rearrangements, but that specific target binding is an outcome of unique rearrangements and VL pairing: the HCDR3 is necessary, albeit insufficient, for specific antibody binding.

Characterization of In Vivo Selected Bacteriophage for the Development of Novel Tumor-Targeting Agents with Specific Pharmacokinetics and Imaging Applications

Bacteriophage (phage) display technology is a powerful strategy for the identification of peptide-based tumor targeting agents for drug discovery. Phage selections performed in vitro often result in many phage clones/peptides with similar properties and often similar sequence. However, these phage and corresponding peptides are selected, validated, and characterized outside the complicated milieu of a living animal. Thus, there is no guarantee that peptides from in vitro selections will successfully meet the requirements of an in vivo targeting compound. In comparison, in vivo phage display selections have the distinct advantage of identifying phage clones with robust pharmacokinetics and tumor/tissue targeting ability. This capacity has allowed for the identification of peptides with specific in vivo localization and/or clearance profiles. However, in vivo phage display selections also have the potential to result in an array of phage clones with various and unknown targets and little to no sequence similarity. Given these shortcomings, we have developed methods to select phage peptide display libraries in living mice to identify phage (and corresponding synthesized peptides) with specific clearance and/or tumor-targeting propensity. Additionally, we describe the use of labeled phage clones for the efficient screening of selected phage/peptides to aid in the identification and characterization of a phage clone with an optimal and specific pharmacokinetic profile.

Computational Strategies for Dissecting the High-Dimensional Complexity of Adaptive Immune Repertoires

The adaptive immune system recognizes antigens via an immense array of antigen-binding antibodies and T-cell receptors, the immune repertoire. The interrogation of immune repertoires is of high relevance for understanding the adaptive immune response in disease and infection (e.g., autoimmunity, cancer, HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the quantitative and molecular-level profiling of immune repertoires, thereby revealing the high-dimensional complexity of the immune receptor sequence landscape. Several methods for the computational and statistical analysis of large-scale AIRR-seq data have been developed to resolve immune repertoire complexity and to understand the dynamics of adaptive immunity. Here, we review the current research on (i) diversity, (ii) clustering and network, (iii) phylogenetic, and (iv) machine learning methods applied to dissect, quantify, and compare the architecture, evolution, and specificity of immune repertoires. We summarize outstanding questions in computational immunology and propose future directions for systems immunology toward coupling AIRR-seq with the computational discovery of immunotherapeutics, vaccines, and immunodiagnostics.

Next-Generation Sequencing of Antibody Display Repertoires

In vitro selection technology has transformed the development of therapeutic monoclonal antibodies. Using methods such as phage, ribosome, and yeast display, high affinity binders can be selected from diverse repertoires. Here, we review strategies for the next-generation sequencing (NGS) of phage- and other antibody-display libraries, as well as NGS platforms and analysis tools. Moreover, we discuss recent examples relating to the use of NGS to assess library diversity, clonal enrichment, and affinity maturation.

Compositional Bias in Naïve and Chemically-modified Phage-Displayed Libraries uncovered by Paired-end Deep Sequencing

Understanding the composition of a genetically-encoded (GE) library is instrumental to the success of ligand discovery. In this manuscript, we investigate the bias in GE-libraries of linear, macrocyclic and chemically post-translationally modified (cPTM) tetrapeptides displayed on the M13KE platform, which are produced via trinucleotide cassette synthesis (19 codons) and NNK-randomized codon. Differential enrichment of synthetic DNA {S}, ligated vector {L} (extension and ligation of synthetic DNA into the vector), naïve libraries {N} (transformation of the ligated vector into the bacteria followed by expression of the library for 4.5 hours to yield a "naïve" library), and libraries chemically modified by aldehyde ligation and cysteine macrocyclization {M} characterized by paired-end deep sequencing, detected a significant drop in diversity in {L} → {N}, but only a minor compositional difference in {S} → {L} and {N} → {M}. Libraries expressed at the N-terminus of phage protein pIII censored positively charged amino acids Arg and Lys; libraries expressed between pIII domains N1 and N2 overcame Arg/Lys-censorship but introduced new bias towards Gly and Ser. Interrogation of biases arising from cPTM by aldehyde ligation and cysteine macrocyclization unveiled censorship of sequences with Ser/Phe. Analogous analysis can be used to explore library diversity in new display platforms and optimize cPTM of these libraries.

Screening Phage-Display Antibody Libraries Using Protein Arrays

Phage-display technology constitutes a powerful tool for the generation of specific antibodies against a predefined antigen. The main advantages of phage-display technology in comparison to conventional hybridoma-based techniques are: (1) rapid generation time and (2) antibody selection against an unlimited number of molecules (biological or not). However, the main bottleneck with phage-display technology is the validation strategies employed to confirm the greatest number of antibody fragments. The development of new high-throughput (HT) techniques has helped overcome this great limitation. Here, we describe a new method based on an array technology that allows the deposition of hundreds to thousands of phages by micro-contact on a unique nitrocellulose surface. This setup comes in combination with bioinformatic approaches that enables simultaneous affinity screening in a HT format of antibody-displaying phages.

Bypassing bacterial infection in phage display by sequencing DNA released from phage particles

Phage display relies on a bacterial infection step in which the phage particles are replicated to perform multiple affinity selection rounds and to enable the identification of isolated clones by DNA sequencing. While this process is efficient for wild-type phage, the bacterial infection rate of phage with mutant or chemically modified coat proteins can be low. For example, a phage mutant with a disulfide-free p3 coat protein, used for the selection of bicyclic peptides, has a more than 100-fold reduced infection rate compared to the wild-type. A potential strategy for bypassing the bacterial infection step is to directly sequence DNA extracted from phage particles after a single round of phage panning using high-throughput sequencing. In this work, we have quantified the fraction of phage clones that can be identified by directly sequencing DNA from phage particles. The results show that the DNA of essentially all of the phage particles can be 'decoded', and that the sequence coverage for mutants equals that of amplified DNA extracted from cells infected with wild-type phage. This procedure is particularly attractive for selections with phage that have a compromised infection capacity, and it may allow phage display to be performed with particles that are not infective at all.

Pacific Biosciences Sequencing and IMGT/HighV-QUEST Analysis of Full-Length Single Chain Fragment Variable from an In Vivo Selected Phage-Display Combinatorial Library

Phage-display selection of immunoglobulin (IG) or antibody single chain Fragment variable (scFv) from combinatorial libraries is widely used for identifying new antibodies for novel targets. Next-generation sequencing (NGS) has recently emerged as a new method for the high throughput characterization of IG and T cell receptor (TR) immune repertoires both in vivo and in vitro. However, challenges remain for the NGS sequencing of scFv from combinatorial libraries owing to the scFv length (>800 bp) and the presence of two variable domains [variable heavy (VH) and variable light (VL) for IG] associated by a peptide linker in a single chain. Here, we show that single-molecule real-time (SMRT) sequencing with the Pacific Biosciences RS II platform allows for the generation of full-length scFv reads obtained from an in vivo selection of scFv-phages in an animal model of atherosclerosis. We first amplified the DNA of the phagemid inserts from scFv-phages eluted from an aortic section at the third round of the in vivo selection. From this amplified DNA, 450,558 reads were obtained from 15 SMRT cells. Highly accurate circular consensus sequences from these reads were generated, filtered by quality and then analyzed by IMGT/HighV-QUEST with the functionality for scFv. Full-length scFv were identified and characterized in 348,659 reads. Full-length scFv sequencing is an absolute requirement for analyzing the associated VH and VL domains enriched during the in vivo panning rounds. In order to further validate the ability of SMRT sequencing to provide high quality, full-length scFv sequences, we tracked the reads of an scFv-phage clone P3 previously identified by biological assays and Sanger sequencing. Sixty P3 reads showed 100% identity with the full-length scFv of 767 bp, 53 of them covering the whole insert of 977 bp, which encompassed the primer sequences. The remaining seven reads were identical over a shortened length of 939 bp that excludes the vicinity of primers at both ends. Interestingly these reads were obtained from each of the 15 SMRT cells. Thus, the SMRT sequencing method and the IMGT/HighV-QUEST functionality for scFv provides a straightforward protocol for characterization of full-length scFv from combinatorial phage libraries.

The state-of-play and future of antibody therapeutics

It has been over four decades since the development of monoclonal antibodies (mAbs) using a hybridoma cell line was first reported. Since then more than thirty therapeutic antibodies have been marketed, mostly as oncology, autoimmune and inflammatory therapeutics. While antibodies are very efficient, their cost-effectiveness has always been discussed owing to their high costs, accumulating to more than one billion dollars from preclinical development through to market approval. Because of this, therapeutic antibodies are inaccessible to some patients in both developed and developing countries. The growing interest in biosimilar antibodies as affordable versions of therapeutic antibodies may provide alternative treatment options as well potentially decreasing costs. As certain markets begin to capitalize on this opportunity, regulatory authorities continue to refine the requirements for demonstrating quality, efficacy and safety of biosimilar compared to originator products. In addition to biosimilars, innovations in antibody engineering are providing the opportunity to design biobetter antibodies with improved properties to maximize efficacy. Enhancing effector function, antibody drug conjugates (ADC) or targeting multiple disease pathways via multi-specific antibodies are being explored. The manufacturing process of antibodies is also moving forward with advancements relating to host cell production and purification processes. Studies into the physical and chemical degradation pathways of antibodies are contributing to the design of more stable proteins guided by computational tools. Moreover, the delivery and pharmacokinetics of antibody-based therapeutics are improving as optimized formulations are pursued through the implementation of recent innovations in the field.