Novel Structural Parameters of Ig-Ag Complexes Yield a Quantitative Description of Interaction Specificity and Binding Affinity

A one-step immunoassay based on switching peptides for diagnosis of porcine epidemic diarrhea virus (PEDV) using screened Fv-antibodies

In this study, a one-step immunoassay for porcine epidemic diarrhea virus (PEDV) based on Fv-antibodies and switching peptides was developed, and the assay results of PEDV were obtained by just mixing samples without any further reaction or washing steps. The Fv-antibodies with binding affinity to the spike protein of PEDV were screened from the Fv-antibody library using the receptor-binding domain (RBD) of the spike protein as a screening probe. Screened Fv-antibodies with binding affinities to the RBD antigen were expressed, and the binding constants (KD) were calculated to be 83-142 nM. The one-step immunoassay for the detection of PEDV was configured as a displacement immunoassay using a fluorescence-labeled switching peptide. The one-step immunoassay based on switching peptides was performed using PEDV, and the limit of detection (LOD) values for PEDV detection were estimated to be Ct = 39.7-36.4. Compared with the LOD value for a conventional lateral flow immunoassay (Ct = 33.0), the one-step immunoassay showed a remarkably improved LOD for the detection of PEDV. Finally, the interaction between the screened Fv-antibodies and the PEDV RBD was investigated using docking simulations and compared with the amino acid sequences of the receptors on host cells, such as aminopeptidase N (APN) and angiotensin-converting enzyme-2 (ACE-2).

A computational simulation appraisal of banana lectin as a potential anti-SARS-CoV-2 candidate by targeting the receptor-binding domain

BACKGROUND

The ongoing concern surrounding coronavirus disease 2019 (COVID-19) primarily stems from continuous mutations in the genome of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), leading to the emergence of numerous variants. The receptor-binding domain (RBD) in the S1 subunit of the S protein of the virus plays a crucial role in recognizing the host's angiotensin-converting enzyme 2 (hACE2) receptor and facilitating cell membrane fusion processes, making it a potential target for preventing viral entrance into cells. This research aimed to determine the potential of banana lectin (BanLec) proteins to inhibit SARS-CoV-2 attachment to host cells by interacting with RBD through computational modeling.

MATERIALS AND METHODS

The BanLecs were selected through a sequence analysis process. Subsequently, the genes encoding BanLec proteins were retrieved from the Banana Genome Hub database. The FGENESH online tool was then employed to predict protein sequences, while web-based tools were utilized to assess the physicochemical properties, allergenicity, and toxicity of BanLecs. The RBDs of SARS-CoV-2 were modeled using the SWISS-MODEL in the following step. Molecular docking procedures were conducted with the aid of ClusPro 2.0 and HDOCK web servers. The three-dimensional structures of the docked complexes were visualized using PyMOL. Finally, molecular dynamics simulations were performed to investigate and validate the interactions of the complexes exhibiting the highest interactions, facilitating the simulation of their dynamic properties.

RESULTS

The BanLec proteins were successfully modeled based on the RNA sequences from two species of banana (Musa sp.). Moreover, an amino acid modification in the BanLec protein was made to reduce its mitogenicity. Theoretical allergenicity and toxicity predictions were conducted on the BanLecs, which suggested they were likely non-allergenic and contained no discernible toxic domains. Molecular docking analysis demonstrated that both altered and wild-type BanLecs exhibited strong affinity with the RBD of different SARS-CoV-2 variants. Further analysis of the molecular docking results showed that the BanLec proteins interacted with the active site of RBD, particularly the key amino acids residues responsible for RBD's binding to hACE2. Molecular dynamics simulation indicated a stable interaction between the Omicron RBD and BanLec, maintaining a root-mean-square deviation (RMSD) of approximately 0.2 nm for a duration of up to 100 ns. The individual proteins also had stable structural conformations, and the complex demonstrated a favorable binding-free energy (BFE) value.

CONCLUSIONS

These results confirm that the BanLec protein is a promising candidate for developing a potential therapeutic agent for combating COVID-19. Furthermore, the results suggest the possibility of BanLec as a broad-spectrum antiviral agent and highlight the need for further studies to examine the protein's safety and effectiveness as a potent antiviral agent.

An artificial neural network model to predict structure-based protein-protein free energy of binding from Rosetta-calculated properties

The prediction of the free energy (ΔG) of binding for protein-protein complexes is of general scientific interest as it has a variety of applications in the fields of molecular and chemical biology, materials science, and biotechnology. Despite its centrality in understanding protein association phenomena and protein engineering, the ΔG of binding is a daunting quantity to obtain theoretically. In this work, we devise a novel Artificial Neural Network (ANN) model to predict the ΔG of binding for a given three-dimensional structure of a protein-protein complex with Rosetta-calculated properties. Our model was tested using two data sets, and it presented a root-mean-square error ranging from 1.67 kcal mol^-1 to 2.45 kcal mol^-1, showing a better performance compared to the available state-of-the-art tools. Validation of the model for a variety of protein-protein complexes is showcased.

Binding affinity prediction for antibody-protein antigen complexes: A machine learning analysis based on interface and surface areas

Specific antibodies can bind to protein antigens with high affinity and specificity, and this property makes them one of the best protein-based therapeutics. Accurate prediction of antibody‒protein antigen binding affinity is crucial for designing effective antibodies. The current predictive methods for protein‒protein binding affinity usually fail to predict the binding affinity of an antibody‒protein antigen complex with a comparable level of accuracy. Here, new models specific for antibody‒antigen binding affinity prediction are developed according to the different types of interface and surface areas present in antibody‒antigen complex. The contacts-based descriptors are also employed to construct or train different models specific for antibody‒protein antigen binding affinity prediction. The results of this study show that (i) the area-based descriptors are slightly better than the contacts-based descriptors in terms of the predictive power; (ii) the new models specific for antibody‒protein antigen binding affinity prediction are superior to the previously-used general models for predicting the protein‒protein binding affinities; (iii) the performances of the best area-based and contacts-based models developed in this work are better than the performances of a recently-developed graph-based model (i.e., CSM-AB) specific for antibody‒protein antigen binding affinity prediction. The new models developed in this work would not only help understand the mechanisms underlying antibody‒protein antigen interactions, but would also be of some applicable utility in the design and virtual screening of antibody-based therapeutics.

Antibody Sequence and Structure Analyses Using IMGT®: 30 Years of Immunoinformatics

IMGT^®, the international ImMunoGeneTics information system^®, http://www.imgt.org , the global reference in immunogenetics and immunoinformatics, was created in 1989 by Marie-Paule Lefranc (Université de Montpellier and CNRS) to manage the huge diversity of the antigen receptors, immunoglobulins (IG) or antibodies, and T cell receptors (TR) of the adaptive immune responses. The founding of IMGT^® marked the advent of immunoinformatics, which emerged at the interface between immunogenetics and bioinformatics. IMGT^® standardized analysis of the IG, TR, and major histocompatibility (MH) genes and proteins bridges the gap between sequences and three-dimensional (3D) structures, for all jawed vertebrates from fish to humans. This is achieved through the IMGT Scientific chart rules, based on the IMGT-ONTOLOGY axioms, and primarily CLASSIFICATION (IMGT gene and allele nomenclature) and NUMEROTATION (IMGT unique numbering and IMGT Colliers de Perles). IMGT^® comprises seven databases (IMGT/LIGM-DB for nucleotide sequences, IMGT/GENE-DB for genes and alleles, etc.), 17 tools (IMGT/V-QUEST, IMGT/JunctionAnalysis, IMGT/HighV-QUEST for NGS, etc.), and more than 20,000 Web resources. In this chapter, the focus is on the tools for amino acid sequences per domain (IMGT/DomainGapAlign and IMGT/Collier-de-Perles), and on the databases for receptors (IMGT/2Dstructure-DB and IMGT/3D-structure-DB) described per receptor, chain, and domain and, for 3D, with contact analysis, paratope, and epitope. The IMGT/mAb-DB is the query interface for monoclonal antibodies (mAb), fusion proteins for immune applications (FPIA), composite proteins for clinical applications (CPCA), and related proteins of interest (RPI) with links to IMGT^® 2D and 3D databases and to the World Health Organization (WHO) International Nonproprietary Names (INN) program lists. The chapter includes the human IG allotypes and antibody engineered variants for effector properties used in the description of therapeutical mAb.

Absolute Quantitation of Serum Antibody Reactivity Using the Richards Growth Model for Antigen Microspot Titration

In spite of its pivotal role in the characterization of humoral immunity, there is no accepted method for the absolute quantitation of antigen-specific serum antibodies. We devised a novel method to quantify polyclonal antibody reactivity, which exploits protein microspot assays and employs a novel analytical approach. Microarrays with a density series of disease-specific antigens were treated with different serum dilutions and developed for IgG and IgA binding. By fitting the binding data of both dilution series to a product of two generalized logistic functions, we obtained estimates of antibody reactivity of two immunoglobulin classes simultaneously. These estimates are the antigen concentrations required for reaching the inflection point of thermodynamic activity coefficient of antibodies and the limiting activity coefficient of antigen. By providing universal chemical units, this approach may improve the standardization of serological testing, the quality control of antibodies and the quantitative mapping of the antibody-antigen interaction space.

Direct-MS analysis of antibody-antigen complexes

In recent decades, antibodies (Abs) have attracted the attention of academia and the biopharmaceutical industry due to their therapeutic properties and versatility in binding a vast spectrum of antigens. Different engineering strategies have been developed for optimizing Ab specificity, efficacy, affinity, stability and production, enabling systematic screening and analysis procedures for selecting lead candidates. This quality assessment is critical but usually demands time-consuming and labor-intensive purification procedures. Here, we harnessed the direct-mass spectrometry (direct-MS) approach, in which the analysis is carried out directly from the crude growth media, for the rapid, structural characterization of designed Abs. We demonstrate that properties such as stability, specificity and interactions with antigens can be defined, without the need for prior purification.

Anti-TNF Alpha Antibody Humira with pH-dependent Binding Characteristics: A constant-pH Molecular Dynamics, Gaussian Accelerated Molecular Dynamics, and In Vitro Study

Humira is a monoclonal antibody that binds to TNF alpha, inactivates TNF alpha receptors, and inhibits inflammation. Neonatal Fc receptors can mediate the transcytosis of Humira-TNF alpha complex structures and process them toward degradation pathways, which reduces the therapeutic effect of Humira. Allowing the Humira-TNF alpha complex structures to dissociate to Humira and soluble TNF alpha in the early endosome to enable Humira recycling is crucial. We used the cytoplasmic pH (7.4), the early endosomal pH (6.0), and pKa of histidine side chains (6.0-6.4) to mutate the residues of complementarity-determining regions with histidine. Our engineered Humira (W1-Humira) can bind to TNF alpha in plasma at neutral pH and dissociate from the TNF alpha in the endosome at acidic pH. We used the constant-pH molecular dynamics, Gaussian accelerated molecular dynamics, two-dimensional potential mean force profiles, and in vitro methods to investigate the characteristics of W1-Humira. Our results revealed that the proposed Humira can bind TNF alpha with pH-dependent affinity in vitro. The W1-Humira was weaker than wild-type Humira at neutral pH in vitro, and our prediction results were close to the in vitro results. Furthermore, our approach displayed a high accuracy in antibody pH-dependent binding characteristics prediction, which may facilitate antibody drug design. Advancements in computational methods and computing power may further aid in addressing the challenges in antibody drug design.

A computational method for immune repertoire mining that identifies novel binders from different clonotypes, demonstrated by identifying anti-pertussis toxoid antibodies

Due to their shared genetic history, antibodies from the same clonotype often bind to the same epitope. This knowledge is used in immune repertoire mining, where known binders are used to search bulk sequencing repertoires to identify new binders. However, current computational methods cannot identify epitope convergence between antibodies from different clonotypes, limiting the sequence diversity of antigen-specific antibodies that can be identified. We describe how the antibody binding site, the paratope, can be used to cluster antibodies with common antigen reactivity from different clonotypes. Our method, paratyping, uses the predicted paratope to identify these novel cross clonotype matches. We experimentally validated our predictions on a pertussis toxoid dataset. Our results show that even the simplest abstraction of the antibody binding site, using only the length of the loops involved and predicted binding residues, is sufficient to group antigen-specific antibodies and provide additional information to conventional clonotype analysis.

Abbreviations: BCR: B-cell receptor; CDR: complementarity-determining region; PTx: pertussis toxoid

SARS-CoV-2 neutralizing antibody structures inform therapeutic strategies

The coronavirus disease 2019 (COVID-19) pandemic presents an urgent health crisis. Human neutralizing antibodies that target the host ACE2 receptor-binding domain (RBD) of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) spike protein1-5 show promise therapeutically and are being evaluated clinically6-8. Here, to identify the structural correlates of SARS-CoV-2 neutralization, we solved eight new structures of distinct COVID-19 human neutralizing antibodies5 in complex with the SARS-CoV-2 spike trimer or RBD. Structural comparisons allowed us to classify the antibodies into categories: (1) neutralizing antibodies encoded by the VH3-53 gene segment with short CDRH3 loops that block ACE2 and bind only to 'up' RBDs; (2) ACE2-blocking neutralizing antibodies that bind both up and 'down' RBDs and can contact adjacent RBDs; (3) neutralizing antibodies that bind outside the ACE2 site and recognize both up and down RBDs; and (4) previously described antibodies that do not block ACE2 and bind only to up RBDs9. Class 2 contained four neutralizing antibodies with epitopes that bridged RBDs, including a VH3-53 antibody that used a long CDRH3 with a hydrophobic tip to bridge between adjacent down RBDs, thereby locking the spike into a closed conformation. Epitope and paratope mapping revealed few interactions with host-derived N-glycans and minor contributions of antibody somatic hypermutations to epitope contacts. Affinity measurements and mapping of naturally occurring and in vitro-selected spike mutants in 3D provided insight into the potential for SARS-CoV-2 to escape from antibodies elicited during infection or delivered therapeutically. These classifications and structural analyses provide rules for assigning current and future human RBD-targeting antibodies into classes, evaluating avidity effects and suggesting combinations for clinical use, and provide insight into immune responses against SARS-CoV-2.

Immunoglobulins or Antibodies: IMGT® Bridging Genes, Structures and Functions

IMGT®, the international ImMunoGeneTics® information system founded in 1989 by Marie-Paule Lefranc (Université de Montpellier and CNRS), marked the advent of immunoinformatics, a new science at the interface between immunogenetics and bioinformatics. For the first time, the immunoglobulin (IG) or antibody and T cell receptor (TR) genes were officially recognized as 'genes' as well as were conventional genes. This major breakthrough has allowed the entry, in genomic databases, of the IG and TR variable (V), diversity (D) and joining (J) genes and alleles of Homo sapiens and of other jawed vertebrate species, based on the CLASSIFICATION axiom. The second major breakthrough has been the IMGT unique numbering and the IMGT Collier de Perles for the V and constant (C) domains of the IG and TR and other proteins of the IG superfamily (IgSF), based on the NUMEROTATION axiom. IMGT-ONTOLOGY axioms and concepts bridge genes, sequences, structures and functions, between biological and computational spheres in the IMGT® system (Web resources, databases and tools). They provide the IMGT Scientific chart rules to identify, to describe and to analyse the IG complex molecular data, the huge diversity of repertoires, the genetic (alleles, allotypes, CNV) polymorphisms, the IG dual function (paratope/epitope, effector properties), the antibody humanization and engineering.

Structural classification of neutralizing antibodies against the SARS-CoV-2 spike receptor-binding domain suggests vaccine and therapeutic strategies

The COVID-19 pandemic presents an urgent health crisis. Human neutralizing antibodies (hNAbs) that target the host ACE2 receptor-binding domain (RBD) of the SARS-CoV-2 spike1-5 show therapeutic promise and are being evaluated clincally6-8. To determine structural correlates of SARS-CoV-2 neutralization, we solved 8 new structures of distinct COVID-19 hNAbs5 in complex with SARS-CoV-2 spike trimer or RBD. Structural comparisons allowed classification into categories: (1) VH3-53 hNAbs with short CDRH3s that block ACE2 and bind only to "up" RBDs, (2) ACE2-blocking hNAbs that bind both "up" and "down" RBDs and can contact adjacent RBDs, (3) hNAbs that bind outside the ACE2 site and recognize "up" and "down" RBDs, and (4) Previously-described antibodies that do not block ACE2 and bind only "up" RBDs9. Class 2 comprised four hNAbs whose epitopes bridged RBDs, including a VH3-53 hNAb that used a long CDRH3 with a hydrophobic tip to bridge between adjacent "down" RBDs, thereby locking spike into a closed conformation. Epitope/paratope mapping revealed few interactions with host-derived N-glycans and minor contributions of antibody somatic hypermutations to epitope contacts. Affinity measurements and mapping of naturally-occurring and in vitro-selected spike mutants in 3D provided insight into the potential for SARS-CoV-2 escape from antibodies elicited during infection or delivered therapeutically. These classifications and structural analyses provide rules for assigning current and future human RBD-targeting antibodies into classes, evaluating avidity effects, suggesting combinations for clinical use, and providing insight into immune responses against SARS-CoV-2.

Network Organization of Antibody Interactions in Sequence and Structure Space: the RADARS Model

Adaptive immunity in vertebrates is a complex self-organizing network of molecular interactions. While deep sequencing of the immune-receptor repertoire may reveal clonal relationships, functional interpretation of such data is hampered by the inherent limitations of converting sequence to structure to function. In this paper, a novel model of antibody interaction space and network, termed radial adjustment of system resolution, RAdial ADjustment of System Resolution (RADARS), is proposed. The model is based on the radial growth of interaction affinity of antibodies towards an infinity of directions in structure space, each direction corresponding to particular shapes of antigen epitopes. Levels of interaction affinity appear as free energy shells of the system, where hierarchical B-cell development and differentiation takes place. Equilibrium in this immunological thermodynamic system can be described by a power law distribution of antibody-free energies with an ideal network degree exponent of phi square, representing a scale-free fractal network of antibody interactions. Plasma cells are network hubs, memory B cells are nodes with intermediate degrees, and B1 cells function as nodes with minimal degree. Overall, the RADARS model implies that a finite number of antibody structures can interact with an infinite number of antigens by immunologically controlled adjustment of interaction energy distribution. Understanding quantitative network properties of the system should help the organization of sequence-derived predicted structural data.

IMGT® and 30 Years of Immunoinformatics Insight in Antibody V and C Domain Structure and Function

At the 10th Human Genome Mapping (HGM10) Workshop, in New Haven, for the first time, immunoglobulin (IG) or antibody and T cell receptor (TR) variable (V), diversity (D), joining (J), and constant (C) genes were officially recognized as 'genes', as were the conventional genes. Under these HGM auspices, IMGT®, the international ImMunoGeneTics information system®, was created in June 1989 at Montpellier (University of Montpellier and CNRS). The creation of IMGT® marked the birth of immunoinformatics, a new science, at the interface between immunogenetics and bioinformatics. The accuracy and the consistency between genes and alleles, sequences, and three-dimensional (3D) structures are based on the IMGT Scientific chart rules generated from the IMGT-ONTOLOGY axioms and concepts: IMGT standardized keywords (IDENTIFICATION), IMGT gene and allele nomenclature (CLASSIFICATION), IMGT standardized labels (DESCRIPTION), IMGT unique numbering and IMGT Collier de Perles (NUMEROTATION). These concepts provide IMGT® immunoinformatics insights for antibody V and C domain structure and function, used for the standardized description in IMGT® web resources, databases and tools, immune repertoires analysis, single cell and/or high-throughput sequencing (HTS, NGS), antibody humanization, and antibody engineering in relation with effector properties.

Antibody-mediated biorecognition of myelin oligodendrocyte glycoprotein: computational evidence of demyelination-related epitopes

Antigen-antibody interaction is crucial in autoimmune disease pathogenesis, as multiple sclerosis and neuromyelitis optica. Given that, autoantibodies are essential biomolecules, of which the myelin oligodendrocyte glycoprotein (MOG) can figure as a target. Here we combined Molecular Dynamics (MD), Steered Molecular Dynamics (SMD), and Atomic Force Microscope (AFM) to detail MOG recognition by its specific antibody. The complex model consisted of the MOG external domain interacting with an experimental anti-MOG antibody from the Protein Data Bank (1PKQ). Computational data demonstrated thirteen MOG residues with a robust contribution to the antigen-antibody interaction. Comprising five of the thirteen anchor residues (ASP₁₀₂, HIS₁₀₃, SER₁₀₄, TYR₁₀₅, and GLN₁₀₆), the well-known MOG_92–106 peptide in complex with the anti-MOG was analysed by AFM and SMD. These analyses evidenced similar force values of 780 pN and 765 pN for computational and experimental MOG_92–106 and anti-MOG detachment, respectively. MOG_92–106 was responsible for 75% of the total force measured between MOG external domain and anti-MOG, holding the interaction with the antibody. The antigen-antibody binding was confirmed by Surface Plasmon Resonance (SPR) measurements. Combined approaches presented here can conveniently be adjusted to detail novel molecules in diseases research. This can optimize pre-clinical steps, guiding experiments, reducing costs, and animal model usage.

Use of IMGT® Databases and Tools for Antibody Engineering and Humanization

IMGT^®, the international ImMunoGeneTics information system^® ( http://www.imgt.org ), was created in 1989 by Marie-Paule Lefranc (Université de Montpellier and CNRS) to manage the huge diversity of the antigen receptors, immunoglobulins (IG) or antibodies, and T cell receptors (TR). The founding of IMGT^® marked the advent of immunoinformatics, which emerged at the interface between immunogenetics and bioinformatics. Standardized sequence and structure analysis of antibody using IMGT^® databases and tools allow one to bridge, for the first time, the gap between antibody sequences and three-dimensional (3D) structures. This is achieved through the IMGT Scientific chart rules, based on the IMGT-ONTOLOGY concepts of classification (IMGT gene and allele nomenclature), description (IMGT standardized labels), and numerotation (IMGT unique numbering and IMGT Collier de Perles). IMGT^® is acknowledged as the global reference for immunogenetics and immunoinformatics, and its standards are particularly useful for antibody engineering and humanization. IMGT^® databases for antibody nucleotide sequences and genes include IMGT/LIGM-DB and IMGT/GENE-DB, respectively, and nucleotide sequence analysis is performed by the IMGT/V-QUEST and IMGT/JunctionAnalysis tools and for NGS by IMGT/HighV-QUEST. In this chapter, we focus on IMGT^® databases and tools for amino acid sequences, two-dimensional (2D) and three-dimensional (3D) structures: the IMGT/DomainGapAlign and IMGT Collier de Perles tools and the IMGT/2Dstructure-DB and IMGT/3Dstructure-DB database. IMGT/mAb-DB provides the query interface for monoclonal antibodies (mAb), fusion proteins for immune applications (FPIA), and composite proteins for clinical applications (CPCA) and related proteins of interest (RPI) and links to the proposed and recommended lists of the World Health Organization International Nonproprietary Name (WHO INN) programme, to IMGT/2Dstructure-DB for amino acid sequences, and to IMGT/3Dstructure-DB and its associated tools (IMGT/StructuralQuery, IMGT/DomainSuperimpose) for crystallized antibodies.

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.