The present study focuses on the diversity found from CDR1 until the end of FR3 of the human H chain V domain. By focusing our attention on diversification of well-defined germline genes and alleles, our analysis is minimally confounded by differences in germline gene allelic makeup between individuals or between haplotypes of an individual. In preparation of the present study, we consequently inferred the germline VH repertoires of the lymphocyte donors (19) and analyzed genes of donors with well-established germline gene allele composition. The one exception to this rule is IGHV3-23 and its, in their mature peptide-coding sequences, exactly duplicated sequence IGHV3-23D, sequences that were treated as one entity in this study. Furthermore, as some germline genes are highly similar, confounding outcomes may occur as a consequence of mutational processes rendering sequences derived from one germline gene more similar to the nucleotide sequence of other germline genes. Such highly similar germline genes (>98% nucleotide identity) were, to avoid erroneous interpretation, not investigated in the present study.

Antibodies of different germline gene origins differed substantially in terms of diversified residues, in agreement with recent findings (46). There is thus a solid basis for defined, preferred germline-centric paths of antibody evolution. Certainly, driving forces that promote higher substitution frequencies may relate to affinity maturation, stability enhancement etc. It is likely that part of the observed differences relate to the presence or absence of sequences acting as hot-spots for the mutational machinery (47). However, structural analysis have previously demonstrated that amino acids that are in mutational hot-spots are not more likely to actually undergo substitution during somatic hypermutation, suggesting that such hot-spots "are not a major driving force in determining which residues are mutated" (48). In any event, IGHV2-5, IGHV4-39, IGHV4-59, IGHV5-51, and IGHV6-1 all encode S29, a residue capable of different interactions in different antibodies (**Figures 14A–D**). It is, however, only in sequences derived from IGHV5-51 that this residue is targeted by extensive substitutions. This gene is also the only one among the five that carries a mutational hot-spot motif affecting this codon. If this is a selected, germline-encoded feature preventing extensive, non-functional substitution of products derived from the other four genes, or not, is currently not known. However, if this is not the case, there is a capacity for antibody evolution in antibodies derived from some genes that is not efficiently explored by the human immune system.

Beyond hot-spot involvement in the orchestration of substitution, it is also likely that structural consideration in many cases guide the ability of antibodies of different germline gene origins to tolerate or even prefer substitutions. Certainly, loops belonging to different canonical classes (49, 50), positioning side chains with identical residue numbers in entirely differently orientations and environments, may affect their ability to structurally accept substitutions. Importantly, some germline genes encode unusual residues in some positions, the side chain of which may be suboptimal for its environment. We envisage that such unusual residue may be more commonly mutated even if they reside in FR, often attaining the more common residue following substitution, as illustrated by residues 53 of IGHV3-11 and residue 71 or IGHV1-18. Furthermore, residue 80, a residue in the upper core of the variable domain, with particular importance for the conformation of CDRH2 (38) provide interesting insight into germline-directed paths for antibody evolution. The common diversification of R80 in products with an origin in IGHV1-8 (in contrast to the lack of diversification of R80 in products derived from IGHV3 subgroup genes) (**Figures 6** and **9**) is not associated with the presence of a mutational hotspot in IGHV1-8 affecting codon 80 (Figure S3 in Supplementary Material). Possibly R80 is less important for maintenance of the integrity of the domain or the general architecture of the binding site in antibodies derived from this germline gene. Interestingly, reorientation of R80 has been demonstrated in one antibody (24) with a likely origin in IGHV1-8. This was also associated with a reorientation of CDRH2 as determined by X-ray crystallography (**Figures 14E-H**). We hypothesize that substitution of R80 in products originating from genes like IGHV1-8 may be part of an efficient route to evolve antibody functionality while still being tolerated in terms of structural stability. We hypothesize that some germline genes may even have an inherent need, or, if one so-prefer, capacity, for evolution that is not present in other germline genes. In all, the reason for such high substitution frequency *in vivo* may differ between antibodies of different germline gene origins. Future studies will have to address the difficulties encountered by, or alternatively extended opportunity of, the cells producing antibodies derived from these germline genes to gain an advantage in the race for selection through the affinity maturation processes occurring in germinal centers.

Residues beyond CDR may interact with antigen or contribute indirectly to the architecture of the binding site. There are, however, many definitions of CDRs (8–12) apart from the one used in this study (13), definitions that accommodate different viewpoints of what constitutes an antigen binding site. Indeed, substantial diversity is apparently tolerated, or even selected for, in residues in immediate proximity to CDRs as defined by IMGT (**Figures 1** and **5**). For instance, codon 55 of different germline genes encodes very different side chains. Is the observed difference in mutability solely a result of the presence or absence of a mutational hotspot, or is it also directed by a difference in importance of this residue for establishment of a core antigen-interacting surface (51) in products encoded by the different germline genes? It is furthermore evident that many other residues in FRs, even those belonging to the cores of the antibody fold and to the VH/VL interface, may harbor extensive diversity. We envisage that such diversity, when structurally tolerated may contribute substantially to improved biophysical properties of the encoded antibody or even to affinity maturation, for instance through stabilization of the binding site during affinity maturation (52). In particular, an area with CDR-like potential, CDR4, that resides in a loop adjacent to the

substitution (Figure 3). A diversity of potential polar interactions of the side chain of S29 have been implicated such as those to Oγ of S31 (IGHV2-5; PDB: 3QRG) (A), Oδ2 of D31 (IGHV4-39; PDB: 5C6T) (B), backbone O of S31 (IGHV4-59; PDB: 3HI1) (C), and Oδ1 of N36 (IGHV5-51; PDB: 4BUH) (D). H chain CDR1, CDR2, and CDR3, are colored in brown, green, and blue, respectively. Side chain atoms are colored in red (oxygen), blue (nitrogen), green (carbon of residue 29) and cyan (carbon of residue 31 or 36). Position of R80 (carbon of side chain shown in cyan) and residue 58 (residues of the side chain as spheres). Structures PDB: 3×3G (with P58) (E) and PDB: 3U1S (with H58) (F) both derived from IGHV1-8, and PDB: 3FZU (with S58) (G) and PDB: 2R56 (with G58) (H), both derived from the IGHV3 subgroup, are shown. H chain CDR1, CDR2, and CDR3, are colored in brown, green, and blue, respectively.

classical CDRs in the folded structure have been defined and exploited (11, 39, 48, 53). It has been suggested to be able to accommodate extensive diversity (39). We have now identified that some, but not all, germline genes introduce diversity in this loop. Such diversity may contribute to functional evolution of antibodies of such germline gene origins. In all, the preferred paths of evolution of antibody V domains extend substantially beyond conventional CDRs in ways directed by an antibody's germline gene origin.

Antibody variable domains, indeed, diversify not only by substitution but also through insertion and deletion of residues into the variable domain sequence (31, 32). In this study, we observed germline gene-inherited patterns that differently target genes with such insertions and deletions, not only in conventional CDRs but also in CDR4 (**Figure 12**; Figure S5 in Supplementary Material). In the past, we hypothesized that the presence of repetitive codons might be one feature that targets such modifications to a particular part of a gene (54). It is also conceivable that parts of VH domains of different germline gene origins are able to structurally harbor such diversity to different extents, a factor that certainly needs further assessment. Nevertheless, it is conceivable that any immune response that relies on introduction of sequence insertion and/or deletion *in vivo* will only recruit members derived from those germline genes that introduce such diversification with ease in critical parts of the sequence. Important immune responses that require such modification have been reported (5, 55) and other responses relying on such evolution, such as those targeting occluded sites, will likely be described in the future. By understanding the ability of particular germline genes to diversify by insertion and deletion, it will be possible to develop our understanding of selection of germline genes made by the immune system in the generation of these particularly difficult immune responses.

Overall, the diverse pattern of diversification even beyond conventional CDR likely complicates computational efforts to assess the involvement of selection during antibody development. We employed one such analysis technology on highly expressed IgG H chain V domain sequences encoded in BM but found no evidence of a positive selection force in the mutational pattern targeting CDR. Our findings are in line with past studies demonstrating a failure to identify evidence of positive selection in CDR, while evidence of negative selection of modifications in FR is detected (43, 44). In agreement with a recent study (46), and given the diversity of paths through which antibodies of different origins evolve, we suggest that any approach to assess selection ought to take germline gene-specific mutational patterns as found in selected and non-selected repertoires into account and not rely entirely on an analysis of mutations based on current global CDR definitions. Processes to facilitate analysis of selection in a germline gene origin-centric fashion have been initiated elsewhere (46). We foresee that such a development will be required if computational approaches are to accurately address the impact of selection on antibody repertoire development. In some situations, such as IgE responses, this aspect is a matter of substantial biological controversy (56, 57) and certainly need further investigations, the outcomes of which will impact our understanding of fundamental biological processes associated with disease.

In summary, we identified germline gene-unique patterns of evolution that occur during hypermutation of antibodies of diverse IGHV germline gene origins. Our findings extend the findings of a recent study, published during preparation of the present manuscript, that identified gene-specific substitution profiles of antibodies of different germline gene origins (46). Collectively, we have demonstrated a diversity of paths taken by antibodies of different germline gene origins to evolve by somatic hypermutation, including not only base substitution but also processes of codon insertion and deletion. Our study forms the basis for improved understanding of molecular evolution as it proceeds in immune responses *in vivo* and establishes a foundation for future germline gene origin-centered analysis approaches.

### ETHICS STATEMENT

This study was carried out in accordance with the recommendations of Regionala etikprövningsnämnden (Lund). All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the Regionala etikprövningsnämnden (Lund).

## AUTHOR CONTRIBUTIONS

UK: bioinformatic pipeline development and bioinformatic analysis, manuscript preparation, and approved the final manuscript. HP: conceived the study, manuscript preparation, and approved the final manuscript. FL: initial bioinformatic pipeline development, manuscript preparation, and approved the final manuscript. LG: patient management, manuscript preparation, and approved the final manuscript. MO: conceived the study, bioinformatic analysis, main responsibility for manuscript preparation, and approved the final manuscript.

### FUNDING

This study was supported by grants from the Swedish Research Council (grant number 2016-01720), and Lund University's Avtal om Läkarutbildning och Forskning (ALF). We acknowledge support from Science for Life Laboratory, the Knut and Alice Wallenberg Foundation, the National Genomics Infrastructure funded by the Swedish Research Council, and Uppsala Multidisciplinary Center for Advanced Computational Science for assistance with NGS and access to the UPPMAX computational infrastructure.

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at http://www.frontiersin.org/article/10.3389/fimmu.2017.01433/ full#supplementary-material.

### REFERENCES

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2017 Kirik, Persson, Levander, Greiff and Ohlin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# How B-Cell Receptor Repertoire Sequencing Can Be enriched with Structural Antibody Data

*Aleksandr Kovaltsuk1 , Konrad Krawczyk1 , Jacob D. Galson2 , Dominic F. Kelly3 , Charlotte M. Deane1 \*† and Johannes Trück2 \*†*

*1Department of Statistics, University of Oxford, Oxford, United Kingdom, 2Division of Immunology and the Children's Research Center, University Children's Hospital, University of Zürich, Zürich, Switzerland, 3Oxford Vaccine Group, Department of Paediatrics, University of Oxford and the NIHR Oxford Biomedical Research Center, Oxford, United Kingdom*

#### *Edited by:*

*Gregory C. Ippolito, University of Texas at Austin, United States*

#### *Reviewed by:*

*Christopher Vollmers, University of California, Santa Cruz, United States Jeffrey J. Gray, Johns Hopkins University, United States Jeliazko R. Jeliazkov, Johns Hopkins University, United States*

#### *\*Correspondence:*

*Charlotte M. Deane<EMAIL_ADDRESS>Johannes Trück<EMAIL_ADDRESS>
> *† Joint senior authors.*

#### *Specialty section:*

*This article was submitted to B Cell Biology, a section of the journal Frontiers in Immunology*

*Received: 11 October 2017 Accepted: 27 November 2017 Published: 08 December 2017*

#### *Citation:*

*Kovaltsuk A, Krawczyk K, Galson JD, Kelly DF, Deane CM and Trück J (2017) How B-Cell Receptor Repertoire Sequencing Can Be Enriched with Structural Antibody Data. Front. Immunol. 8:1753. doi: 10.3389/fimmu.2017.01753*

Next-generation sequencing of immunoglobulin gene repertoires (Ig-seq) allows the investigation of large-scale antibody dynamics at a sequence level. However, structural information, a crucial descriptor of antibody binding capability, is not collected in Ig-seq protocols. Developing systematic relationships between the antibody sequence information gathered from Ig-seq and low-throughput techniques such as X-ray crystallography could radically improve our understanding of antibodies. The mapping of Ig-seq datasets to known antibody structures can indicate structurally, and perhaps functionally, uncharted areas. Furthermore, contrasting naïve and antigenically challenged datasets using structural antibody descriptors should provide insights into antibody maturation. As the number of antibody structures steadily increases and more and more Ig-seq datasets become available, the opportunities that arise from combining the two types of information increase as well. Here, we review how these data types enrich one another and show potential for advancing our knowledge of the immune system and improving antibody engineering.

Keywords: Ig-seq, antibody modeling, B cell, Antibodies, Developability, computational modeling, Next-generation sequencing

### INTRODUCTION

Antibodies are proteins produced by the B cells of jawed vertebrates. Their primary function is to recognize structural sequence motifs (epitopes) within molecules (antigens) usually related to pathogens, which may lead to direct neutralization of those pathogens or their toxins. Further functions of antibodies are activation of the complement system or tagging of antigens for elimination by other immune pathways. Antibodies have the capacity for binding an extraordinary variety of epitopes as a result of their sequence diversity, which is estimated at 1013 unique molecules in the human antibody repertoire (1). An antibody is a large complex molecule (~150 kDa). It can be divided into two parts, the crystallizable fragment (Fc) and the antigen binding fragment (Fab). The Fab fragment is further split into constant and variable regions. There are five possible main Fc portions in humans, and which one is used on a particular antibody is governed by the process of class switching (2). The variable region (Fv) is composed of two domains called the heavy (VH) and light (VL) chains. Within each B cell, the antibody Fv domains are built by somatic recombination between V(D)J segments (3, 4). Upon antigen recognition, somatic hypermutation introduces further diversification into the naïve Fv domains (5). Within each of the VL and VH chains lie three hypervariable loops, the complementarity determining regions (CDRs), which are the most

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diverse parts of the antibody (**Figure 1**). These loops form the majority of chemical interactions with antigens, thus defining the antigen-binding region, the paratope (6). The CDR3 of the heavy chain (CDR-H3) is the most diverse of the CDRs as it is being formed at the join between the V, D, and J gene segments and subject to high levels of hypermutation. As a result of this diversity, CDR-H3 plays a key role in antigen recognition and binding (7). The non-CDR sections of the variable domain are called the framework. Framework positions next to CDRs along with CDR sequence govern the structural shape of the loops (8).

The properties of antibodies, in particular designable antigen recognition specificity and binding affinity, have made them useful as diagnostics and research agents as well as the most successful class of biopharmaceuticals (10). Although small molecules constitute the largest proportion of potential therapeutics in clinical trials, the antibody market is steadily growing, with new antibody approvals at a rate of about four per year. As of 2016, five out of the 10 best-selling drugs worldwide were recombinant monoclonal antibodies (11).

Successful exploitation of antibodies relies on our ability to interrogate their diversity and function. Application of nextgeneration sequencing of immunoglobulin gene repertoire (Ig-seq) to antibody profiling is able to produce comprehensive snapshots of the repertoire diversity (12). However, most Ig-seq techniques are currently unable to perform sequencing of paired heavy–light antibody sequences or to obtain an immunoglobulin gene repertoire solely from antibody-secreting B cells (13–15). Advances in liquid chromatography tandem-mass spectroscopy (LC-MS/MS) now allow high-throughput analysis of serum antibodies at the amino-acid sequence level (16, 17). Previously transcriptomics and Ig-seq datasets have been used to deconvolute MS spectra of serum antibodies into constituent full-length entities (18). Such combined Ig-seq and LC-MS/MS techniques have provided new insights in vaccination and autoimmunity studies (19, 20). Recent advances in computational tools that integrate *de novo* antibody sequencing, error correction data, and sequence homology databases now permit an accurate assembly of full-length antibodies based on the remit of LC-MS/MS spectra alone (21).

The biggest advantage of Ig-seq and LC-MS/MS techniques is their high-throughput nature. This means that the methods provide a broad-brush description and quantification of antibodies in the repertoire. However, this will often include inaccurate data caused by PCR or sequencing errors. The limitation of Ig-seq and LC-MS/MS methods is that they provide sequence information only, whereas it is the shape/structure of an antibody that determines its exact biological function. For instance, antibody CDRs with low-sequence identities can adopt structurally close shapes,

numbering (9)]: VH is colored blue; VL is green; CDRs are labeled and depicted in different colors; and disulfide bonds are in yellow.

and hence present conformationally similar, though perhaps chemically different, binding sites (22). Knowledge of antibody structure is vital for inferring chemistry of antigen recognition as well as allowing binding site comparison between antibodies. Current experimental determination of antibody structures is achieved by X-ray crystallography or NMR spectroscopy. However, collecting such detailed experimental information limits the rate of analysis to the level of individual or a small number of antibodies (23).

To help tackle the rising costs and time required for engineering and characterization of antibodies, a number of computational tools have been developed that can facilitate experimental efforts. Computational methods are used to profile the physico-chemical properties of antibodies, predict antibody–antigen contacts, and redesign antibody–antigen complexes (24, 25). The tools can be broadly divided into those that require only the sequence of an antibody as input and those that require the structure of the antibody. The inclusion of structural information where available has been shown to improve prediction of most properties over sequence-based methods (26). These improved predictions are only possible if a native structure or an accurate model of the antibody is available.

Since the structure of an antibody is key to its function and high-throughput crystallographic determination of the structures of every antibody is currently not feasible, computational modeling techniques may aid to reduce attrition in the biopharmaceutical industry and to accelerate drug discovery (27). The development of systematic relationships between the antibody information gathered from Ig-seq and techniques such as X-ray crystallography, NMR spectroscopy, and tandem LC-MS/MS could radically improve our understanding of antibody biology. As the number of antibody structures steadily increases and more Ig-seq datasets become available, the opportunities that arise from combining them increase as well. As of October 9, 2017, more than 2,860 antibody structures were available in the Protein Data Bank (PDB) (28) as identified by the Structural Antibody Database (29). The publically available volume of sequences produced from Ig-seq experiments is now in the hundreds of millions (30). In this manuscript, we consider the information obtained from high-throughput sequencing experiments and antibody structures. We review how these datasets can enrich one another and with the help of computational techniques, advance our knowledge of antibody diversity, maturation, and selection and pave the way for improved antibody engineering.

### IMMUNOGLOBULIN GENE REPERTOIRE SEQUENCING TECHNOLOGIES

Ig-seq offers high-throughput characterization of immunoglobulin gene sequences at great depth and typically includes several B-cell samples in a single-sequencing run. By controlling the number of samples that are combined and the number of B cells contained therein, it is possible to obtain a large fraction of an immunoglobulin repertoire from a sample. The potential applications of Ig-seq include vaccine and drug development as well as immunodiagnostics (12, 31, 32). Such applications rely on our ability to efficiently identify the population of antibodies responding to an antigen challenge. Ig-seq has already been successfully applied to isolate antigen-specific antibodies from immunized animals in conjunction with common laboratory screening platforms such as phage display (33) or hybridoma (34) or even when the screening step was omitted (35). Furthermore, amino-acid sequence convergences in the CDR-H3 have been observed in the response to a variety of antigens, and may serve as an additional way to isolate antigen-specific antibodies through identifying sequences common among several individuals exposed to the same antigen (30, 36–39).

Heavy and light chains are products of two independent mRNA transcripts that co-assemble into full-length immunoglobulin molecules in the endoplasmic reticulum of the B cell. However, cognate pairing is lost after B-cell bulk lysis prior to Ig-seq and most Ig-seq studies therefore only consider heavy chains (12). However, for human and mouse native pairing is crucial for antibody folding, stability, expression, and antigen binding (40–42). Furthermore, information on the heavy/light chain dimer is required to create an accurate three-dimensional (3D) model of the Fv region and of its antigen-binding pocket which is essential for rational antibody engineering (43). Such models can map antibody sequences to structural space (44), identify the paratope and its physico-chemical properties (45), interrogate the mode of interaction with antigens (46), and predict antibody developability properties (47, 48). Predicting or experimentally obtaining the native VH/VL pairing of the antibody is therefore crucial for our understanding of antibody biology and our ability to engineer these molecules (49).

Several approaches have been devised to circumvent the loss of native pairing in current Ig-seq experiments. Reddy et al. (35) assigned VH/VL pairs based on relative variable chain frequencies in VH and VL chain Ig-seq datasets. This methodology required an accompanying VL Ig-seq dataset and does not always produce antibodies with good pharmacodynamics properties, indicating that it is not always accurate (35). Researchers have also used protein expression platforms, such as recombinant cell lines or phage display, to assign VL to VH chains in a combinatorial fashion followed by experimental screening to identify productive VH/VL combinations (20, 50). Dekosky et al. (15, 51) published the first high-throughput paired VH/VL gene sequencing approach by using single-cell linkage PCR to physically join the VH and VL chains prior to Illumina sequencing. The limitation of this approach is that the current Illumina read length cannot cover the entire paired sequence, so the analysis is restricted to only CDR-H3, CDR-L3, and neighboring framework 4 and proximal positions of framework 3 of respective chains. Once sufficient paired datasets are available, these can potentially act as a reference for guiding computational pairing when VH-only Ig-seq is performed (52). Paired Ig-seq techniques currently yield smaller dataset sizes than unpaired sequencing––for instance, there were 200k sequences for the paired dataset from Dekosky et al. (15) as opposed to 40-m unpaired VH sequences in a recent study (53). The unprecedented speed and depth of Ig-seq techniques both paired and unpaired is unfortunately accompanied by high-sequencing error rates as discussed below.

The four main high-throughput sequencing platforms used to interrogate the immunoglobulin gene repertoire are Illumina, Roche 454, PacBio, and IonTorrent (39, 54–57). Earlier studies often used the Roche 454 technology as it offered greater read lengths than the Illumina methodology. In recent years, Illumina sequencing platforms are usually preferred as they have increasing read length, higher read depth, lower error rates, and lower costs per base (57, 58). Employment of unique molecular identifiers (UIDs) now permits sequencing of the entire antibody chain together with a fragment of a constant domain which holds antibody isotype information (59, 60). Unfortunately, any high-throughput Ig-seq technique suffers from significant error rates (61). Sequencing error can be introduced into Ig-seq datasets from incorrect base calling and sequencing primer artifacts, and has distinct features depending on the sequencing platform used. Error and biases can also originate from the process of preparing sequencing material including reverse transcriptase and polymerase error, amplification of nonproductive V(D)J variable domains during DNA sequencing and multiplex PCR amplification biases (62, 63). Such error may result in the overestimation of the actual number of unique clones in an Ig-seq dataset (62).

Several computational and experimental approaches have been developed to identify and remove or correct erroneous reads (58, 63), though no single-error correction strategy is currently widely used in Ig-seq repertoire analysis (30, 58). In particular, the recent application of UID to Ig-seq can help to correct errors in sequenced transcripts by generating a consensus of reads originating from the same mRNA molecule. As many studies are confined to CDR-H3 analysis, erroneous reads may also be corrected for by using a consensus CDR-H3 sequence for analysis following CDR-H3 clustering (39, 51, 64).

### ANTIBODY STRUCTURAL PROPERTIES

The structure of an antibody is crucial in order to understand its function. Antibody–antigen recognition relies on the 3D conformation of the antibody binding site, the paratope, in relation to the cognate epitope on the antigen. In their 3D form, antibodies adopt a Y-shape conformation which can exist in monomer (IgG, IgD, and IgE), dimer (IgA) or pentamer (IgM) forms in humans (65). Several disulfide bonds help to maintain the immunoglobulin fold (**Figure 1**). One set of disulfide bonds hold the heavy constant domains together in the hinge region and another set connects the light and heavy chains (66). Intravariable domain cysteine pairs play a crucial part in shaping the antibody Fv region and artificial disruption of these bonds leads to impaired stability, folding and antigen recognition (67). These cysteines therefore have a crucial role in delineating the structural features of an antibody.

Equivalent residue positions across immunoglobulin sequences and structures can be identified by applying an antibody numbering scheme. Several numbering schemes have been developed to confer consistency and standardization on antibody sequence annotation (9, 22, 68–71). The most commonly used scheme in Ig-seq analysis is the IMGT scheme (12, 39). This numbering was built considering both structural and sequence information (9). The IMGT scheme supports symmetrical amino-acid insertions inside CDRs which ensures that structurally equivalent resides will be annotated the same regardless of CDR length. In contrast, Chothia numbering is often used by structural biologists for its simple CDR loop indel management and inherently structural focus (69, 71).

One of the principal differences between numbering schemes is how they define CDRs. Wu and Kabat (68) were the first to discover and define CDRs as portions of Fv chains that display high-sequence entropy, but as with numbering schemes, there is not a single widely adopted CDR definition and different schemes are used for legacy reasons or for specific features (such as insertion management in IMGT). The different numbering schemes define antibody CDR positions very consistently with the exception of CDR-H1 and CDR-H2 (70). Structural analysis of CDR loops has suggested that all CDRs, except for CDR-H3, adopt a restricted number of conformations, termed canonical classes (22, 72). The canonical classes link sequence patterns to a defined structure (22, 44). This enables the prediction of canonical class structure from sequence. Over the last 30 years, there have been several attempts to cluster CDR sequences/ structures (22, 44, 69, 70, 72, 73). On the sequence level, the presence of certain cluster defining key residues indicates the shape the loop can adopt (22, 69, 73). Hence, some changes to the canonical CDRs can be tolerated with no explicit change to loop conformations. The different clustering methods tend to recapitulate previously found groups and find new canonical forms as a result of new data. Most algorithms incorporate CDR loops into clusters with the same number of residues (note that the number of residues varies with different CDR definitions). More recently, Nowak et al. (44) created a novel method of defining length-independent canonical classes based on findings that loops of mismatching lengths can be structurally related. This method allowed fast and accurate structural assignment of a far wider spectrum of canonical CDRs from Ig-seq datasets into fewer canonical clusters (44).

Complementarity determining region-3 of the heavy chain shows a high degree of sequence, length, and structure variation. Due to this diversity, it has so far proved impossible to classify CDR-H3 loops into canonical classes in the manner of the other CDRs. It has been proposed that CDR-H3 can be categorized into "bulged" or "extended" conformations based on the presence of asparagine at position 116 (IMGT numbering) (74, 75). However, increasing knowledge of CDR-H3 structural diversity has shown that the CDR-H3 bulged/extended configuration is difficult to predict solely from sequence (76). The relationship between sequence and structure in CDR-H3 can be important in Ig-seq as current approaches of clonotype assignment are based on CDR-H3 similarity. In this review, we define clonotypes by the presence of identical V, J genes, matching CDR-H3 lengths and CDR-H3 sequence identities greater than 85% (77). However, structural data show that CDR-H3 sequences within distinct clonotypes (sequence-dissimilar) can adopt similar 3D conformations, while those in the same clonotype (similar sequences) can adopt different 3D conformations (**Figure 2**). This suggests that the sequence alone is not a reliable indicator of similarity/difference between structures and therefore cannot

Figure 2 | Two aligned pairs of VH chains extracted from SAbDab, the antibody structural database (29). Complementarity determining region-3 of the heavy chain (CDR-H3) sequences in pair (A) belong to different CDR-H3 clonotypes but adopt very similar structural configurations with a root mean square deviation (RMSD) of ~1 Å. Pair (B) includes germline precursor (4JDV) and matured (3U7W) anti-gp120 antibodies (78, 79). Although CDR-H3 sequences of pair (B) are members of the same clonotype, the RMSD shows that their CDR-H3 shapes are structurally distinct (RMSD > 2 Å). CDR-H3 loops and their amino-acid sequences are in purple and cyan colors, mismatched amino acid are in bold. The RMSD of the backbone atom positions of proteins provides a pairwise measurement of the threedimensional dissimilarity between two sets of coordinates where solved or predicted structures are available. Sub-Angstrom RMSD indicates structurally identical shapes, while an RMSD value greater than 2 Å for a short segment indicates structurally distinct configurations (80).

reliably indicate similar/different binding sites, functional properties and clonotype assignment.

The discrepancy between traditional clonotype assignments and native structure only illustrates how 3D information could be used to draw much more meaningful comparisons between antibodies in an Ig-seq dataset. Such comparisons should not be confined to CDR-H3 alone, but can be extended to the canonical CDRs and the entire Fv region, allowing for much more accurate grouping of functionally related antibodies.

### COMPUTATIONAL TOOLS LEVERAGING ANTIBODY STRUCTURE INFORMATION

The increasing number of potential applications of antibodies as therapeutics has led to the development of computational tools which aim to streamline discovery pipelines. Some groups have already demonstrated the viability of *in silico* antibody engineering methodologies in conjunction with experimental workflows (81–84). Computational methods can be broadly divided into those that require a sequence as input and those that require a structure. Methods that require a structure as input accept experimental as well as computational models of the antibody. The large number of experimentally determined antibody structures has enabled researchers to rapidly and accurately model antibodies by leveraging homology methods (8, 85). Below we review current antibody modeling approaches and their applications.

### Computational Antibody Modeling

The standard antibody modeling workflow includes four steps (**Figure 3**) (8, 86, 87). The first step is homology modeling of the VH and VL frameworks. The framework template can either be selected by sequence identity to the full-length chain (87) or to individual framework regions (8). Due to framework structure and sequence invariance, current computational tools can model framework structures very accurately (sub-Angstrom precision) (80). The second step is determining the VH/VL orientation, which can be achieved by copying the orientation angle from structures with high Fv sequence identity using VH/VL orientation methods such as AbAngle (88), analytical estimation of the angle using energy functions (89), tailored protein–protein docking (49) or structure-trained machine learning (90). Once the VH/VL orientation is set, it constrains the geometry of the binding site, allowing for the third step, which is modeling of non-H3 CDRs. At this stage, either the canonical classes are used (91) or template-based modeling such as FREAD (92) or ABGEN (93). In the final step, CDR-H3 is modeled using either homology or *ab initio* techniques (94). The resultant antibody model is refined for feasibility of dihedral angles from Ramachandran distribution, side chain orientations and side-chain clashes (89).

Homology modeling approaches can be fast at generating models if a template structure is available. Models can be created using online services: PIGSpro (86), Kotai Antibody Builder (95), and ABodyBuilder (8). Homology modeling is highly dependent on the availability of a similar template structure in

current databases, which can be a problem for CDR-H3 where templates for longer loop length are often unavailable (94). This lack of templates is primarily due to the huge diversity of CDR-H3 shapes (96). An alternative to homology methods in such cases is *ab initio* modeling which does not rely on knowledge of already solved structures. These modeling methods create a large number of potential conformations, often referred to as decoys (97), which makes them computationally expensive compared with homology methods. *Ab initio* approaches include RosettaAntibody (98) and PLOP (99). RosettaAntibody is accessible online *via* the ROSIE (100) website, where a quick antibody modeling option is available which omits the step of intensive searching for low-energy CDR-H3 conformations. Hybrid loop modeling methodologies leverage the advantages of both modeling paradigms. For instance, Accelrys creates an initial loop model with a knowledge-based approach followed by *ab initio* loop refinement (101). More recently, a novel CDR-H3 modeling tool, Sphinx, was developed (102), inspired by the length-independent canonical CDR clustering of Nowak et al. (44). Sphinx outperformed all modeling tools on CDR-H3 structure prediction in an *ex post facto* comparison to the antibody modeling assessment (80). Despite development of different approaches, no single tool currently exists that is able to reliably model native CDR-H3 configurations. Accurate predictions of the CDR-H3 specifically and other CDRs in general are crucial to structurally characterize the antibody–antigen complex.

Performance of antibody modeling tools has been assessed in two blind studies, AMA-I and AMA-II (80, 103), where several computational tools were benchmarked against a small number of X-ray solved but unpublished antibody crystal structures. Models of frameworks and canonical CDRs are usually accurate within 1–1.5 Å root mean square deviation (RMSD), respectively (see **Figure 2** for description of RMSD), which is very close to native structure. However, CDR-H3 prediction remains the biggest hurdle for computational antibody modeling as average accuracies for this step ranged between 2 and 3 Å RMSD, indicating a decidedly different structure to the native fold. Predictions of this quality are usually not suitable for rational design applications (80, 104).

AMA-II suggested that antibody modeling tools on average produce models of approximately similar accuracies with higher RMSD for longer loop lengths. However, the time required is radically different between homology and *ab initio* approaches (80). Homology modeling can produce a model on average in under a minute [ABodyBuilder (8)], whereas *ab initio* approaches may require up to tens of CPU hours per model [RosettaAntibody takes 482 CPU hours on average per model (100)]. To be able to use a fast homology method a suitable template is needed. Such templates are becoming more frequently available as the number of solved antibody structures increases (29). In order to model millions of sequences in a typical Ig-seq dataset, speed is crucial. Modeling at such high throughput can currently only be achieved by tools such as ABodyBuilder, which is able to generate a model within ~30 s (8). However, further increasing the rate and accuracy of antibody modeling, and developing new ways of speeding up CDR-H3 prediction, are needed if we are to structurally characterize complete Ig-seq datasets.

The accuracy and speed of some computational tools mean that thousands of sequences from Ig-seq datasets can be modeled. Such structurally annotated Ig-seq datasets allow more relevant comparisons of CDRs, binding sites and thus a more accurate grouping of molecules (**Figure 2**). The improved capacity to compare and group antibodies allows us to better visualize the antibody structure space and to investigate structural convergences of paratopes, which can be important for vaccine development (36, 37). In addition, modeled Ig-seq data can be used as input for several computational tools which annotate structure-derived antibody properties, such as therapeutic viability of the molecule (105).

### Computational Prediction of Developability

Developing an antibody of high specificity and affinity against a target is only the initial step in engineering a therapeutic molecule. The resulting antibody can carry an array of risks, collectively described as developability, which includes low-expression yields, high-aggregation propensity, and off-target effects (106, 107). In the process of identifying therapeutic candidates, structurally mapped Ig-seq data can be computationally further refined for entities that pass developability criteria (45).

High-aggregation propensity is one of the most undesirable features of antibody therapeutics. Since aggregation is related to the hydrophobicity of the molecule, knowledge of structure is crucial as it allows the calculation of solvent accessible surface area. Structure-based aggregation propensity prediction tools operate by either locating surface-exposed aggregation hot spots and/or leveraging physico-chemical properties of the structure (105, 108). AGGRESCAN3D, a tool inspired by identification of hot spots in the beta amyloid peptide, distinguishes between buried, conformation engaged, and solvent-exposed aggregation prone hydrophobic patches (48). The drawback of this method was that it was not initially designed for antibodies. The Developability Index (DI) was designed for antibodies and is a structure based computational tool that quantitatively assess antibody's propensity to aggregate (105). The DI function considers the net charge of the full-length antibody and hydrophobicity of solvent-exposed sidechains of CDRs.

Such computational tools can be employed early in drug development pipelines to either isolate therapeutically viable drug candidates from the entirety of Ig-seq-derived antibody repertoire (47). Application of such structurally oriented tools requires large-scale modeling of Ig-seq datasets. Nevertheless, to date, there have not been many attempts to combine Ig-seq with structural and computational methods systematically.

### COMBINING Ig-seq, STRUCTURAL, AND COMPUTATIONAL APPROACHES

Current approaches to delineate immune repertoires usually employ Ig-seq methodology only, remaining firmly within the remit of information that can be derived from sequences (31, 109, 110). The only study which has attempted to combine paired Ig-seq and structural information to characterize antibody 3D space was that of Dekosky et al. (45). Using high-throughput RosettaAntibody modeling, more than 2,000 models in naïve and antigen-experienced Ig-seq datasets were analyzed. These models helped to obtain a set of structural descriptors such as net charge, surface hydrophobicity of solvent accessible surface area for computationally determined paratopes. However, the choice of methodologies for this study imposed several limitations. Paired VH/VL data did not contain information about the full-length Fv region. Hence, all paired reads had to be completed using respective V germline gene sequences. Moreover, RosettaAntibody modeling speed only permitted the prediction of structure of 1% of the total Ig-seq dataset (2,000 sequences) in 570k CPU hours. Finally, the paired reads with CDR-H3 sequences longer than 16 amino acids were not included in the structural analysis as the modeling accuracy of such loops is currently low. This emphasizes the challenges of modeling longer CDR-H3 configurations (94, 96). Hence, novel fast and reliable CDR-H3 *ab initio* prediction as well as technologically optimized paired VH/VL gene Ig-seq are urgently needed for improved Ig-seq data modeling and interrogation.

RosettaAntibody (98) is a well-established antibody modeling tool and is able to structurally model sequence data; however, its run times make it difficult to structurally characterize the millions of sequences that are gathered during a typical Ig-seq experiment. For this reason, streamlined approaches are being developed to tackle the structural annotation of Ig-seq datasets. For instance, Nowak et al. (44) performed the structural clustering analysis of

*Many of these tools have online presence and links to these are available on our website http://antibodystructure.org.*

CDR-L3 of two large Ig-seq datasets: 200k paired Ig-seq sample from Dekosky et al. (15) and 9-m in-house UCB Pharma Ltd sequences as well as a database of 71k antibody sequences [DIGIT (111)]. Every CDR-L3 sequence was submitted to HMMER (112) to assign it to a length-independent cluster. This is the first instance of structurally mapping the entirety of an Ig-seq dataset. The method can be extrapolated to any non-H3 CDR to provide structural annotation of sampling of loop shapes as well as to identify yet uncharacterized loop configurations.

Structural characterization of large sequence sets can be extended to the entire Fv region. The modeling method, ABodyBuilder, was used to predict structures of 6,000 paired antibody sequences from public repositories (8). The average modeling time per 1,000 antibody sequences was 567 CPU hours compared with 285,000 CPU hours using RossettaAntibody (45). ABodyBuilder produces model accuracies that are in line with the AMA-II values (80). Using tools such as ABodyBuilder, one can perform large-scale structural modeling of Ig-seq data. Such structural characterization of Ig-seq similarity/difference would allow more accurate inter-molecule comparisons and assessment of developability. The structural software outlined in this manuscript together with other tools that are often employed in computational/structural annotation of antibody sequences is summarized in **Table 1**.

### CONCLUSION

The ability to engineer better antibody-based therapeutics relies on our knowledge of the exact sequence and the 3D shape of individual molecules within the antibody repertoire. Nextgeneration sequencing methodologies that can yield millions of immunoglobulin gene sequences in a single sequencing run have already given insights into the steady-state and antigen-stimulated B-cell receptor repertoire (12, 32). On the other hand, low-throughput techniques such as X-ray crystallography can provide detailed information about individual antibody structures. Computational methodologies can offer a bridge between the two fields by allowing structural annotation of Ig-seq experiments (8, 44, 45). Availability of antibody structures and maturity of modeling techniques means it is now possible to perform large-scale structural characterizations of Ig-seq samples. This enriched structural content can be used to perform more precise characterization of antibodies allowing inter-antibody comparisons and grouping of structurally similar sequences (that may not be possible on the sequence level) as well as annotation of developability information. Large-scale Ig-seq datasets can also direct computational tools for targeted interrogation of antibody structural space. Statistical knowledge of the distribution of the antibody structures and sequences can offer crystallographers an idea of the common but currently unknown antibody variants. The Ig-seq and structural communities will benefit from cross-fertilization of ideas and methodologies. Together they will advance our knowledge of the antibodies in health and disease and pave the way for more advanced antibody-based therapeutics.

### AUTHOR CONTRIBUTIONS

All authors contributed to the development of writing of the manuscript.

### FUNDING

This work was supported by funding from Biotechnology and Biological Sciences Research Council (BBSRC) [BB/M011224/1] and UCB Pharma Ltd awarded to AK. DK receives salary support from the NIHR Oxford Biomedical Research Centre. JT is funded by the Swiss National Science Foundation through an

### REFERENCES

Ambizione-SCORE grant and has received further funding from the Olga Mayenfisch Foundation Zurich and the Bangerter-Rhyner Foundation Basel.

in the B cell receptor repertoire. *Front Immunol* (2015) 6:531. doi:10.3389/ fimmu.2015.00531

prediction. *Bioinformatics* (2017) 33:1346–53. doi:10.1093/bioinformatics/ btw823

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2017 Kovaltsuk, Krawczyk, Galson, Kelly, Deane and Trück. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Pacific Biosciences sequencing and IMGt/highV-QUest Analysis of Full-Length single Chain Fragment Variable from an *In Vivo* selected Phage-display Combinatorial Library

*Audrey Hemadou1 , Véronique Giudicelli2 , Melissa Laird Smith3†, Marie-Paule Lefranc2 \*, Patrice Duroux <sup>2</sup> , Sofia Kossida2 , Cheryl Heiner3 , N. Lance Hepler3 , John Kuijpers3 , Alexis Groppi <sup>4</sup> , Jonas Korlach3 , Philippe Mondon5 , Florence Ottones1 , Marie-Josée Jacobin-Valat1 , Jeanny Laroche-Traineau1‡ and Gisèle Clofent-Sanchez <sup>1</sup> \*‡*

*1CRMSB, UMR 5536, CNRS, Bordeaux, France, 2 IMGT*®*, The International ImMunoGeneTics Information System*®*, Laboratoire d'ImmunoGénétique Moléculaire, LIGM, Institut de Génétique Humaine, IGH, UMR 9002, CNRS, Montpellier University, Montpellier, France, 3Pacific Biosciences, Menlo Park, CA, United States, 4Université de Bordeaux, Centre de Bioinformatique de Bordeaux (CBiB), Bordeaux, France, 5 LFB Biotechnologies, Lille, France*

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, fulllength 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

*Edited by:*

*Prabakaran Ponraj, Intrexon, United States*

#### *Reviewed by:*

*Lei Xu, National Cancer Institute (NIH), United States Kwan-Ki Hwang, Duke University, United States*

#### *\*Correspondence:*

*Marie-Paule Lefranc<EMAIL_ADDRESS>Gisèle Clofent-Sanchez gisele.clofent-sanchez@ rmsb.u-bordeaux2.fr*

#### *†Present address:*

*Melissa Laird Smith, Department of Genetics and Genomic Sciences, Icahn School of Medicine Mount Sinai, New York, United States*

> *‡ Equivalent position of authors.*

#### *Specialty section:*

*This article was submitted to B Cell Biology, a section of the journal Frontiers in Immunology*

*Received: 30 October 2017 Accepted: 30 November 2017 Published: 20 December 2017*

#### *Citation:*

*Hemadou A, Giudicelli V, Smith ML, Lefranc M-P, Duroux P, Kossida S, Heiner C, Hepler NL, Kuijpers J, Groppi A, Korlach J, Mondon P, Ottones F, Jacobin-Valat M-J, Laroche-Traineau J and Clofent-Sanchez G (2017) Pacific Biosciences Sequencing and IMGT/ HighV-QUEST Analysis of Full-Length Single Chain Fragment Variable from an In Vivo Selected Phage-Display Combinatorial Library. Front. Immunol. 8:1796. doi: 10.3389/fimmu.2017.01796*

**93**

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.

Keywords: human antibody, IMGT/HighV-QUEST, immunoinformatics, immunoglobulin, Pacific Biosciences sequencing, phage combinatorial library, single chain fragment variable, next-generation sequencing

### INTRODUCTION

Immunoglobulin (IG) or antibody fragments displayed as single chain Fragment variable (scFv) on filamentous phages (scFvphages) are classically selected from scFv-phage combinatorial libraries to obtain human antibodies specific for a given target (1–3). This selection from scFv-phage display libraries is widely used for the discovery of novel specificities for therapeutic antibodies in cancer, cardiovascular, autoimmune, infectious or neurodegenerative pathologies, with many of them at various stages of clinical or research development (4–10). Classical *in vitro* phage display approaches involve multiple rounds of selection (or panning) for the enrichment of scFv-phages that demonstrate the desired specificity against a target followed, at the last selection round, by functional screening and characterization of selected candidates using appropriate assays. At this very last step, analysis of the selected scFv *via* Sanger sequencing is commonly used to identify sequences of interest, taking advantage of the genotype–phenotype linkage inherent to the display system. A critical limitation of using biological assays followed by Sanger sequencing is that only a minute fraction of the selected library is actually sampled, a few hundred at best, whereas the selected library may usually contain up to 105 to 106 variants. This limitation is further enhanced when scFv-phage selection is performed *in vivo* (biopanning) in different pathological models in which scFv-phages can encounter a very large panel of unknown biomarkers (11–13). Currently available next-generation sequencing (NGS) platforms allow the simultaneous sequencing of millions of reads. However, a main challenge for the NGS sequencing of scFv from combinatorial libraries remains the scFv length >800 bp, which is too long for most NGS platforms. Up to now, NGS methods have only provided reads encompassing one variable (V) domain (400 bp), therefore losing a critical piece of information found in scFv sequences, that of the association of two specific V domains [variable heavy (VH) and variable light (VL) for the IG] by the peptide linker. Although a few approaches have been proposed, retrieving information regarding V domain association has still not been solved (14–16).

The analysis of antibody scFv sequences is a difficult exercise because not only are scFv composed of two V domains, but these two V domains are different from each other and each can potentially be extremely diverse. Indeed, the huge diversity of IG or antibodies results from complex *in vivo* mechanisms that occur during the synthesis of the VH and VL domains, which include the molecular rearrangements at the DNA level of the variable (V), diversity (D) (only for VH), and joining (J) genes with nucleotide deletions and insertions (N-diversity) at the V-(D)-J junctions in the bone marrow pre-B and immature B cells (17, 18). In spleen and lymph nodes, somatic hypermutations accumulate in the mature B cell VH and VL, creating a huge diversity of the B cell membrane IG for the recognition of foreign antigens. Following a specific antibody-antigen interaction the B cell proliferates and generates clones engaged in *in vivo* selection and affinity maturation. The specificity of the V domains is conferred by the complementarity determining regions (CDR) and more particularly the CDR3 (19–21). The same features are observed in *in vitro* combinatorial libraries, which mimic the natural *in vivo* diversity, selection and affinity maturation (1–3).

In order to manage, analyze and compare the extraordinary diversity of the immune repertoires, IMGT®, the international ImMunoGeneTics information system®<sup>1</sup> (22), was created in 1989 in Montpellier by Marie-Paule Lefranc (Montpellier University, CNRS), which marked the birth of immunoinformatics (18), a new science at the interface between immunogenetics and bioinformatics. IMGT® has developed online tools that provide a detailed and accurate analysis of the V domains and which, in the case of nucleotide sequences, include IMGT/V-QUEST (23–25) for the analysis of the rearranged V-(D)-J sequences of the IG or antibodies and T cell receptors (TR), and IMGT/JunctionAnalysis (26, 27) for the analysis of the V-(D)-J junctions and of the included CDR3. The algorithms and IMGT reference directories of these tools have been implemented in IMGT/HighV-QUEST (28–31), the first and only web portal for NGS sequence analysis of IG and TR, begun in 2010. IMGT/HighV-QUEST analyses up to 500,000 NGS reads per batch and includes a statistical module for IMGT clonotype identification and comparison (analyses are performed on the results of up to one million reads, from one or several batches) (30). IMGT/StatClonotype (32, 33), a stand-alone tool and R package, allows for the comparison of IMGT clonotype diversity and expression between two NGS data sets, using the IMGT/HighV-QUEST statistical results output. In order to overcome the analysis challenge of the NGS scFv, the IMGT/V-QUEST functionality "Analysis of single chain Fragment variable (scFv)" which includes the search and characterization of two V domains in a single sequence [IMGT/V-QUEST Documentation2 (34)] has recently been integrated in IMGT/HighV-QUEST (IMGT/ HighV-QUEST Documentation3 ).

**Abbreviations:** CCS, circular consensus sequencing; IG, immunoglobulin; IMGT, IMGT®, the International ImMunoGeneTics Information System®; NGS, next-generation sequencing; scFv, single chain Fragment variable; SMRT, singlemolecule real-time; TR, T cell receptor.

<sup>1</sup>http://www.imgt.org.

<sup>2</sup>http://www.imgt.org/IMGT\_vquest/share/textes/imgtvquest.html. 3http://www.imgt.org/HighV-QUEST/doc.action.

The main challenge addressed in this study was to obtain high quality NGS reads of the scFv long enough to encompass the two VH and VL domains and to analyze and characterize the association of the two V domains in the NGS scFv reads using the scFv functionality in IMGT/HighV-QUEST. Full-length scFv reads are expected to have a length of around 800 bp with a whole insert of around 1,000 bp (including the 5′ and 3′ sequences of the vector and primer sequences at both ends). We used the Pacific Biosciences (PacBio) third-generation NGS technology, which provides long sequencing reads and the highest consensus accuracy available today (35–38). For this project, the high accuracy is a result of the generation of circular consensus sequencing (CCS) reads, by which the long sequencing reads allow for multiple passes of the same insert and thus removal of random sequencing errors upon consensus construction. Practically, one single molecule, realtime (SMRT) cell has 150,000 zero-mode waveguides (ZMWs), of which 50,000–75,000 are loaded with single molecules during sequencing, resulting in the production of ~50,000–75,000 unique consensus reads per run and per SMRT cell. PacBio NGS sequencing was performed from amplified DNA of scFv-phages isolated from the third round of an *in vivo* biopanning in an animal model of atherosclerosis (12, 13). The PacBio scFv CCS (version 2) reads were first analyzed using IMGT/HighV-QUEST with the scFv functionality. In a second step, the sequence quality was evaluated by tracking the NGS reads of a scFv-phage clone P3, identified by biological assays and Sanger sequencing.

### MATERIALS AND METHODS

## *In Vivo* Selection of scFv-Phages Specific to Atheroma Plaque

A fully human-recombinant scFv antibody library (scFv cloned in the pMG72 phagemid vector, containing the ampicillinresistant gene for the selection and maintenance of the phagemid) with a diversity of 3.4 × 109 clones (full description in patent WO2007137616) was expressed by phage display and selected *in vivo*, as previously described (13). The scFv-phages were obtained from the scFv-phagemid combinatorial library by expression of the scFv on the phage surface, following the addition of a helper filamentous phage to recombinant phagemid infected bacteria in the exponential phase (39). Three rounds of biopanning were performed in atheromatous injured rabbits (12). All animal experiments were performed in conformity with the Guide for the Care and Use of Laboratory Animals (NIH Publication No. 85–23, revised 1996) and were accredited by the local ethical committee (Animal Care and Use Committee of Bordeaux, France under the No. 50120192). Briefly, the procedure was the following (**Figure 1**): 2.4 × 1012 colony-forming units (cfu) of scFv-phages were injected into an atheromatous rabbit. After 1 h in circulation, the animal was sacrificed, the aorta was retrieved and scFv-phages binding to the aorta were eluted in different fractions. The eluted scFv-phages were reamplified in XL1-Blue bacteria and following scFv expression at the phage surface as above (39), the amplified

FIGURE 1 | Human single chain fragment variable-phagemid combinatorial library construction (A) and *in vivo* phage display selection (B).

scFv-phages were reinjected in another atheromatous animal. Rounds 2 and 3 were conducted following the same procedure. The number of reinjected colony-forming units were 4.8 × 1011 in round 2 and 3.9 × 1011 in round 3. The total number of eluted scFv-phages from the third round was 1.5 × 107 cfu (total for seven fractions corresponding to different areas of the aorta).

In this study, the analyzed fraction is the one recovered after the third round of selection from the endothelial cells of the damaged abdominal aorta vessel wall (named AAR3 for abdominal aorta round 3) (**Figure 2**). The scFv-phages were amplified in XL1-Blue bacteria and plated on 145 mm Petri dishes for storage of the whole AAR3 fraction before sequencing, and on 80 mm Petri dishes for limiting dilution (quantification of recombinant bacteria and picking of individual clones) (**Figure 2**). The recombinant bacteria plated on 145 mm Petri dishes were scratched and stored at −80°C in 50% v/v glycerol. Around 3.5 × 105 clones issuing from the whole AAR3 fraction were counted by limiting dilution of recombinant bacteria on 80 mm Petri dishes. Ninety-six recombinant bacteria clones were individually picked and grown in selective medium on a 96-well MASTERBLOCK® polypropylene storage plate (Greiner Bio-One, France) and stored for *in vitro* bioreactivity assays (*in vitro* screening of scFv-phages on atheromatous proteins) and for Sanger sequencing.

Each of the 96 individual recombinant bacteria clones from the MASTERBLOCK® was repicked on an agarose 96-Well plate (Beckman Coulter, France). After incubation at 37°C overnight, plasmid DNA extraction, PCR amplification and sequencing were then performed by Beckman Coulter (France). One of these sequenced clones, P3 (767 bp), was used in this study for tracking the PacBio reads that were identical or related to it.

## PacBio RS II Sequencing of the Whole AAR3 Fraction

### Generating High Quality PCR Products

Single-molecule real-time sequencing requires high-quality, doubled-stranded DNA as input. To ensure this, plagemid DNA of recombinant bacteria from the whole AAR3 fraction was extracted directly from the frozen extract just before PCR amplification, using the QIAprep spin miniprep kit (Qiagen, France) according to manufacturer's instructions. To generate clean, undamaged and non-chimeric amplicons, the highest fidelity polymerase was used. All PCR reactions were performed in volumes of 50 µL using 25 µL of the KAPA HiFi™ HotStart ready Mix (Kapa Biosystems, France) and 20 ng of DNA template. Each primer was used at a final concentration of 0.3 µM. PCR reactions were performed with the forward primer (Primer 1, 23-mer FWD) 5′-TGCAAATTCTATTTCAAGGAGAC-3′ and the reverse primer (Primer 2, 20-mer REV) 5′-TCACGTG CAAAAGCAGCGGC-3′. These primers were designed based on the phagemid vector; for primer 1 from position −96 to −74 upstream of the BamHI site and for primer 2 from positions 95 to 114 downstream of the NotI site (**Figure 3A**).

~400 bp and variable light (VL) between ~320 and ~350 bp]. The linker is 53 bp including the EcoRI and XbaI sites. The PCR products are expected to be ~1,000 bp on average, including the 5′ and 3′ region and the primers. (B) Agarose gel electrophoresis of PCR products. The DNA was amplified from the AAR3 fraction and PCR products were analyzed on 1.2% (w/v) agarose gel. The band at ~1,000 bp corresponds to the expected size for scFv amplicons. S1, S2, S3, and S4 correspond to the samples 1, 2, 3, and 4, respectively. The Bioanalyzer trace of the four samples shows the purity of amplicons with a high-quality single peak. (C) Pacific Biosciences RS II CCS2 read length distribution using P6-C4 chemistry for 1 SMRT cell (similar results were obtained for the 15 SMRT cells). Data are based on a 1-kb size-selected scFv library using a 6 h movie.

The PCR cycling protocol was chosen according to the manufacturer's instructions and consisted of 95°C for 3 min; 15 cycles of 98°C 20 s, 65°C 15 s, 72°C 30 s; one 72°C 1 min followed by one cycle at 4°C. The reduction of the number of PCR cycles minimizes the PCR bias, however when further decreases in the number of PCR cycles were attempted, it led to several contaminating, offtarget bands on an agarose gel.

The required quantity in PacBio guidelines for insert sizes superior to 750 bp is 500 ng of DNA. Four PCR reactions were performed on the same AAR3 fraction for reproducibility purposes. Agarose gel electrophoresis was used to confirm amplification, correct fragment size, and to check for non-specific product contamination. A sizing marker was included to confirm size specificity (GeneRuler 1 kb Plus DNA Ladder, Thermo Fisher Scientific, France). Amplicons were cleaned using 1× ratio of AMPure PB Beads (Pacific Biosciences). DNA purity and quantification (sample volume, yields and size distributions) were evaluated and measured using the Agilent 2100 Bioanalyzer DNA12000 kit (Agilent technologies).

#### SMRTbell Template Preparation

SMRTbell templates (PacBio, CA, USA) were constructed following the standard Amplicon Sequencing Protocol.4 The full procedure is explained in Figure S1 in Supplementary Material.

Briefly, PCR products were treated to repair DNA damage and then hairpin adapters were added *via* blunt end ligation to produce SMRTbell templates using the SMRTbell template prep kit 1.0. Exonucleases III and VII were used to remove failed ligation products and SMRTbell templates were purified with AMPure PB Beads. The ratio of sequencing primer and polymerase was determined by a PacBio calculator to correlate with SMRTbell concentrations and the 1,100-bp insert size. The sequencing primer was annealed to the single-stranded loop region of the SMRTbell template, and primer-annealed templates were then bound to DNA polymerase P6 using the DNA/polymerase binding kit P6v2. The DNA–polymerase complexes were loaded on

<sup>4</sup>http://www.pacb.com/wp-content/uploads/Procedure-Checklist-Amplicon-Template-Preparation-and-Sequencing.pdf.

15 SMRT cells using MagBeads onto the PacBio RS II system and sequenced using the C4 chemistry and 6-h movies.

Initial loading titrations were performed to identify the optimal loading concentration, identifying 0.03–0.035 nM as the best loading conditions for scFv PCR products. Each SMRT cell generates ~50,000 reads on average (considering that 50,000–75,000 ZMW can be optimally loaded with a single molecule of DNA). A total of 15 SMRT cells were loaded for the four different PCR products of the same AAR3 fraction (three SMRT for sample 1 and four SMRT for samples 2, 3, and 4) to cover, per PCR sample, the diversity of the AAR3 fraction (Figure S2 in Supplementary Material). The use of 15 SMRT cells was chosen to provide a thorough and sound proof-of-concept and read comparison from four different PCR amplicons generated from the same fraction (AAR3).

### PacBio CCS Read Generation

The PacBio RSII instrument produces sequencing reads with an average read length of ~15 kb, which would theoretically pass over a 1,100 bp molecule more than 10 times, producing CCS2 sequences with an accuracy ~99.9%. Longer read lengths can be achieved by increasing the instrument run time, so these data were collected using 15 SMRT cells and 6 h movies, to maximize read length and, thus, number of passes. Following sequencing, the raw data were processed using the CCS2 pipeline (CCS code used available at https://github.com/PacificBiosciences/unanimity). All CCS2 reads that were 99.9% accurate or greater were exported for further analysis. The raw NGS data of the 85-related P3 sequences can be found in the NCBI Sequence Read Archive with the accession number SRP124616.

## IMGT/HighV-QUEST Analysis of the PacBio CCS2 Reads

### Characterizing IMGT/HighV-QUEST scFv Reads from AAR3

The FASTQ files of the PacBio CCS2 reads were imported and converted to FASTA sequences for submission to IMGT/HighV-QUEST5 (29, 30), which implements IMGT/V-QUEST program version 3.4.2 (August 4, 2016) and IMGT/V-QUEST reference directory release 201631-4. The analysis was performed with the advanced functionality "Analysis of single chain Fragment variable (scFv)" (IMGT/HighV-QUEST Documentation, see text footnote 3).

Data filtering was applied with the following criteria to be fulfilled for each of the two V domains: (i) >85% of identity of the V-REGION of the V domain with the V-REGION of the closest germline IMGT gene and allele and (ii) in-frame V-(D)-J junction. Filtered sequences were then analyzed to identify the closest V, D (for VH) and J IMGT genes and alleles, to characterize the amino acid (AA) junction, to evaluate the mutations and to give a complete description of the scFv with IMGT labels (IMGT Index > scFv6 ).

### Tracking and Analysis of Identical and Related PacBio Reads of the AAR3 scFv-Phage Clone P3

In order to evaluate the PacBio scFv read sequencing quality, reads identical or closely related to the sequence of a scFv-phage clone P3 (previously isolated from the same fraction AAR3 and Sanger sequenced) were tracked among the total reads generated in the 15 SMRT cells by two approaches. First, reads potentially related to P3 were searched for based on the expected VH-VL characteristics determined by IMGT/HighV-QUEST (same V and J genes and alleles, and same AA junctions). Second, a Blast search was performed to check whether P3-related reads could have been missed by the IMGT/HighV-QUEST filtering. The fasta headers of the sequences were modified to include the sequence set identifier and a blast database was built (formatdb 2.2.26) from the accumulated reads from the 15 SMRT cells. The database search was performed using the blastn program (2.2.26), with the combined following criteria: longest alignments on the P3 Sanger (767 bp) sequence, highest identity percentage, and maximum number of 20 mismatches or indels. Among the extracted reads only those fulfilling the P3 characteristics (in terms of V and J genes, allele names and AA junctions) were retained.

### RESULTS

### Generation and Analysis of PCR Products from the Whole AAR3 Fraction

The unbiased characterization of scFv from phage-display combinatorial libraries, in conjunction with sequencing on the PacBio RS II system, requires high-quality PCR products with undamaged, clean and non-chimeric amplicons. Creating PCR protocols to generate products that are truly representative of the starting cell population is a major challenge.

To achieve this aim: (1) the complete frozen AAR3 fraction was directly used, without amplification, as the source of recombinant bacteria; (2) efforts were made to limit the number of amplification cycles (the four samples subject to 15 cycles) in order to reduce quantitative distortions as well as error rates due to PCR artifact (data not shown); and (3) different high-fidelity Taq polymerases were tested so as to fit with our amplification system. The required quantity (500 ng) and the correct size of amplicons (~1,000 bp) were obtained with the KAPA HiFi™ HotStart polymerase (**Figure 3A**). The quality and quantity of amplicons was confirmed using an Agilent 2100 Bioanalyzer. After confirmation of the purity (**Figure 3B**), PCR samples were sequenced on the PacBio platform.

### PacBio Sequencing and CCS2 Analysis

Sequencing was done on the PacBio RS II system using P6-C4 sequencing chemistry and SMRTbell libraries generated from DNA amplified from the AAR3 fraction (corresponding to 3.5 × 105 scFv-phagemids). For each of the four PCR samples, 3 or 4 SMRT cells were run to ensure adequate sampling of the scFv as described in Section "Materials and Methods" (Figure S2 in Supplementary Material). A total of 15 SMRT cells were used.

Circular consensus sequencing (CCS2) analysis of the 15 SMRT cells produced 450,558 reads. These reads were filtered to remove any double-loaded wells or other artifactual/lower

<sup>5</sup>http://www.imgt.org/HighV-QUEST/login.action. 6http://www.imgt.org/IMGTindex/scFv.php.

accuracy reads. The data obtained from filtered, post-CCS2 analysis represent the reads achieving 99.9% accuracy or above, derived only from wells loaded with a single molecule. Any reads that did not reach consensus coverage of QV30 (99.9% accurate) or above were filtered out. Those settings and filters are built into the CCS2 pipeline that is available *via* the PacBio web-based analysis software (SMRT Link). Thus, these 450,558 reads passed initial filtering with an average pass number of 24 and a quality score of minimum 99.94% accuracy (**Figure 3C**). Another contributing factor to data quality and throughput was the use of the longest movie lengths possible at the time (6 h) to ensure the longest read lengths for analysis.

### IMGT/HighV-QUEST Analysis of the scFv PacBio Reads

The 450,558 FASTQ PacBio CCS2 reads were converted in FASTA sequences and analyzed using IMGT/HighV-QUEST, as described in Section "Materials and Methods." After analysis, a total of 391,655 scFv "candidates" (i.e., sequences with two V regions, IMGT label V-REGION) (86.93% of the submitted PacBio reads) were identified (**Table 1**). The scFv sequences were then filtered according to the criteria described in Section "Materials and Methods" [>85% of identity of the V-REGION of the V domains with the V-REGION of the closest germline IMGT genes and alleles and in-frame V-(D)-J junction, determined for both V domains]. The threshold of 85% of identity is the standard filter for classical IG repertoire analysis in IMGT/HighV-QUEST (29). Following this filtering, 348,659 full-length scFv representing 89.02% of the filtered sequences were identified (**Table 1**). These scFv reads include 346,934 VH-VL or VL-VH expected scFv sequences (Table S1 in Supplementary Material). The other 1,725 scFv reads comprise 171 VH-VH (5–22 found per SMRT cell) and 1,554 VL-VL (68–158 found per SMRT cell). These combinations most probably occurred during the construction of the original scFv-phage combinatorial library. Thus these results provide a useful and detailed overview of the content of the scFv combinatorial library.

The IMGT/HighV-QUEST analysis of the scFv reads included identification of the closest V, D (if VH), and J IMGT genes and alleles, characterization of the junction, evaluation of the mutations, and complete description of both V domains with IMGT labels (see text footnotes 2 and 5). These results demonstrate that both domains of the scFv reads sequenced by PacBio could be fully characterized with the functionality for scFv.

### Tracking and Analysis of PacBio Reads Identical or Related to the P3 Clone across 15 SMRT Cells

Single chain fragment variable-phages issuing from the *in vivo* AAR3 selected fraction have been screened by a high-throughput flow cytometry method against atherosclerotic rabbit proteins. Some of the selected clones were then processed into scFv-Fc format in HEK293 cells and validated by immunohistochemistry on atheromatous aorta sections of two animal models of atherosclerosis (ApoE<sup>−</sup>/<sup>−</sup> mouse and New Zealand White rabbit submitted to hypercholesterolemic diet) and on human endarteriectomy TABLE 1 | IMGT/HighV-QUEST analysis of the scFv Pacific Biosciences (PacBio) reads.

biopsies. A scFv-phage clone, named P3, selected for its high accuracy in biological assays in the two animal models and human biopsies was Sanger sequenced and patented (EP 17306337.1). The whole scFv P3 sequence (Figure S3 in Supplementary Material) was then tracked among the 346,934 VH-VL and VL-VH reads (Table S1 in Supplementary Material) from the 15 SMRT cells, by the two approaches described in Section "Materials and Methods," in order to evaluate the PacBio scFv read sequencing quality.

### Analysis of PacBio Reads Identical to the P3 Clone

Sixty PacBio reads with 100% identity to the region aligned with the P3 Sanger sequence (767 bp) were obtained by both approaches. Interestingly, the P3 scFv was identified in the top 100 of the most abundant VH-VL associations in all the 15 data sets. These PacBio reads have a length of 977 bp, including the 5′ and 3′ regions and the primers. Fifty-three of the 60 reads have a 100% identity on their full length of 977 bp. As these PacBio reads were obtained from the 15 SMRT cells (**Table 2**), we can confidently say that no sequencing error was observed in the 51,781 bp of these 53 reads.

Seven reads showed 100% identity on a 939 bp length (**Table 2**). Interestingly their mutations are all localized at the ends of the primers and/or in the immediate vicinity of the 3′ primer (**Table 3**). A most probable explanation is that they occur during the sequencing polymerization (the priming step could be excluded as different mutations were observed in different SMRT cells and no degenerate bases were used in the primers). Ignoring the mutations in or next to the primers, no sequencing error was observed in 6,573 nucleotides of the 7 reads (100% identity on 939 bp). Combining these results with those of the 53 reads (100% identity on the full length of 977 bp), no sequencing error was detected on 58,354 nucleotides for the 60 scFv reads.

### Analysis of PacBio CCS Reads Related to the P3 Clone

In addition to the 60 PacBio reads with a 100% identity (53 reads on 977 bp and seven reads on 939 bp) (**Table 2**), 25 "related" P3

TABLE 2 | Pacific Biosciences (PacBio) reads 100% identical to the aligned P3 Sanger sequence and 100% identical between them on 977 bp (53 reads) or 939 bp (7 reads).

TABLE 3 | Mutations observed at the 5′ and 3′ end of the seven Pacific Biosciences (PacBio) reads with 100% identity on 939 bp (positions 3–941).

*Positions of the primers are 1–23 and 958–977.*

*a Mutations are described according to the IMGT Scientific chart rules (http://www.imgt.org/IMGTScientificChart/Nomenclature/IMGTmutation.html) (40). bThe 1 nt-deletion (a942* > *del) found in reads from the four samples most probably originates from the library.*

reads were identified (Figure S4 in Supplementary Material). These 25 P3-related reads showed 15 different mutation types in the insert and, for each type, a limited number of reads (1–6) (**Table 4**). Altogether, the 15 mutation types are described by 28 different individual mutations. This heterogeneity is in sharp contrast with the 60 reads with a 100% identical insert.

The observation of related reads was expected as the scFv are from a phagemid combinatorial library in which point mutations were initially introduced to mimic the IG somatic hypermutations. The analysis of the 25 reads was therefore performed in an attempt to distinguish among them related clones with potential biological interest (as reflecting the diversity of the original library) from reads with artifactual differences.

For 15 of these PacBio CCS reads, the origin of the mutations (mostly substitutions) was clearly from the scFv phagemid combinatorial library (and therefore of potential interest given their relatedness to P3). This was supported by the fact that identical mutated reads were obtained from different PCR and from different SMRT cells (category A in **Table 4**). These included 7 reads (highlighted in pink) with four identical individual mutations, 2 reads (highlighted in green) with three identical individual mutations different from those described above and one shared by both groups [c838 > g (VL)], 6 reads (highlighted in blue) with two identical individual mutations (and still different from those of the previous mutation types). All the substitution mutations are localized in the VL, in agreement with mutations of the VL and VH domains being targeted differently before the assembly into the scFv and confirming that the differences observed are intrinsic to the VL domain, and not due to PCR or sequencing errors. The IMGT/V-QUEST alignment of the 15 PacBio NGS sequences with the initial P3 Sanger sequence is provided as an additional PDF file in supplementary data (Figure S5 in Supplementary Material).

For the other 10 PacBio reads, present in one copy (category B in **Table 4**), the origin of the differences could not be determined with certainty: and any of the possible explanations: putative sequencing errors, PCR amplification errors or diversity of the combinatorial library could not be formally excluded at this stage. The 1 nt-deletion observed in VH and VL for reads 85 and 40, respectively, the large nt deletion at the 3′ end in read 78, the nt deletion at both 5′ and 3′ ends for read 81 and the one or two nt insertions in VH or VL for reads 84 and 9 could be considered as sequencing errors. The six reads with different single substitution and the one with 3 mutations could be attributed to PCR amplification errors or diversity of the combinatorial library. Moreover, and even if these differences are due to diversity of the combinatorial library, their single copy number suggests that they are from unselected (or poorly selected) scFv-phages from the library background and could be ignored in the library screening.

### DISCUSSION

Antibody libraries are important resources to derive antibodies to be used for a wide range of diagnostic and therapeutic applications. *In vivo* or *ex vivo* phage-display selections have emerged as interesting ways to identify accurate antibodies in the context of the pathologic microenvironment (11). Although advancements in automation of biological assays have greatly improved screening strategies, high-throughput campaigns are still severely limited in the number of antibody fragments that can be interrogated, providing only a tiny fraction of the enriched phage library. Access to the genetic information encoded in antibody repertoires by NGS should allow more in depth analysis of the diversity of the selected library. However, the currently available NGS platforms that were capable of providing several million reads per run, generated only short reads of up to 300–700 bp (41). Therefore, only synthetic combinatorial scFv libraries, in which diversity was confined to CDR3 regions of the heavy and light chains, have benefited from NGS potential in the extensive *in silico* analysis of complex collections of selected antibodies (16).

While Mi-Seq, 454, or Ion-Torrent technologies will completely sequence heavy and light variable domains, they are currently insufficient to cover the full-length scFv, which are comprised of both VH and VL domains, connected by a peptide linker. These technologies often necessitate consensus building of multiple reads originating from the scFv fragment to obtain whole sequence information (42–44). Therefore, NGS sequencing of scFv fragments longer than 800 bp is still hampered by technical limitations in the length of reads. These limitations could be circumvented by the third generation PacBio sequencing platform. Capitalizing on PacBio SMRT DNA sequencing for high-resolution and high-throughput HLA typing (36, 37), we develop here a PacBio SMRT/CCS2 approach, combined with IMGT/HighV-QUEST analysis of full-length scFv reads to provide a straightforward protocol for characterization of the complete VH and VL domains of scFv from fully human combinatorial libraries.

Pacific Biosciences SMRT sequencing generated 450,558 reads of about 1,000 bp across 15 SMRT cells, following DNA amplification of the scFv insert from 3.5 × 105 *in vivo* selected scFv-phages. IMGT/HighV-QUEST with its scFv functionality allowed for further filtration and characterization of the 348,659 PacBio CCS reads as containing full-length scFv, which represent 89.02% of the overall filtered sequences from the 15 SMRT cells run. Among them, 346,934 identified expected VH-VL or VL-VH full-length scFv reads.

In order to evaluate the PacBio scFv read sequencing quality, a selected scFv-phage clone P3, previously identified from the AAR3 fraction by biological screenings and sequenced by the Sanger methodology, was tracked within the 346,934 VH-VL and VL-VH scFv reads characterized by IMGT/HighV-QUEST. Sixty PacBio CCS2 reads were identified from the 15 SMRT cells that demonstrated 100% identity on a length of 939 bp (including the complete scFv of 767 bp and the 5′ and 3′ regions) and for 53 of them on the full length of 977 bp (including the primers at both ends). Thus no sequencing error was observed on a total of 51,728 bp for these 53 reads, obtained from the 15 SMRT cells (and 58,354 bp if the seven reads with 100% on 939 bp are included), validating the capacity of the PacBio SMRT-CCS2 method to produce reads with an accuracy of >99.9% for complete sequences of inserts approaching 1,000 bp. It is of interest to note that these PacBio reads were obtained from the 15 SMRT cells.

In addition to the 60 PacBio reads with a 100% identity, 25 "related" P3 reads were identified and for 15 of them, mutation

GenBank/ENA/

Mutation descriptiona

TABLE 4 | Mutation heterogeneity observed in 25 P3-related PacBio reads, in contrast with the 60 P3 identical PacBio reads with 100% identity on the complete scFv.

> Mutation type

Number

Read

Pacific Biosciences

PCR

SMRT cell

analysis (category A in **Table 4**) clearly showed that they are related to P3. This was supported by the fact that identical mutated reads in the VL (in agreement with the diversity generated during the library construction) were obtained from different PCR samples and from different SMRT cells. P3 (60 reads) with its 15 related reads are among the top 100 most represented associated VH-VL domains in all the 15 data sets.

This study provides the first proof-of-concept that similar sequences could be tracked in phage-display selected scFv samples and their frequency determined by the number of reads. The favorite candidates chosen for their high frequency of enrichment could be rescued from these *in silico* data for implementation of downstream biological assays. This could be easily done by custom gene chemical synthesis, which offers the utmost flexibility and efficiency with high production yields (45).

Thus the large number of sequenced reads delivered, following the enrichment process, could be ideally suited for a more extensive evaluation of antibody candidates by biological assays. In that way, bringing full sequence data from NGS will accelerate search for identification of both the antibodies and their targeted biomarkers. We thus aimed to combine the sensitivity of the sequencing approach with the functional information provided by the immune assays. This *in silico* approach could be applied to any other pathology and phage-display screening methodology. Resolving the issue of complete scFv sequencing has the potential to profoundly impact the selection process of antibodies with desired properties after phage display biopanning with a special focus of *in vivo* selections. It is expected that this will contribute to therapeutic antibody discovery, selection and development.

### Limitations

Scientists working on IGs are very concerned by the problems of sequencing errors versus mutations and of short read assembly. This study demonstrates that the PacBio SMRT-CCS2 method is able to produce reads with an accuracy of >99.9% for complete sequences of scFv inserts without the need of *in silico* VH and VL assembly as usually necessary using MiSeq or Ion Torrent technologies (43, 44). To assess the read quality of PacBio sequencing, we performed a pilot study using different PCR samples and 3 or 4 SMRT for each PCR, starting from the same AAR3 fraction. Using the biologically validated P3 clone as a reference, we demonstrated that P3 clone is among the top 100 *in vivo* selected clones with a representativity of 0.025%. Moreover, among the 85 P3 related sequences, 60 were 100% identical and 15 clearly originated from the scFv phagemid combinatorial library. From the IMGT/V-QUEST alignment of P3 pink, blue, and green groups of reads with the initial P3 Sanger sequence, we can confidently say that 88% of reads were free of sequencing errors. However, a doubt remains for 10 of them, present in just one copy. The nt-deletions or nt insertions observed in VH and VL for six of the 10 reads could be definitively considered as sequencing errors. For the single substitutions observed in seven reads, it is obviously impossible to determine their origin. To circumvent this limitation, other technologies could be considered, such as inverse PCR method based on VH CDR3 or VL CDR3 sequences (44) to synthesize these clones from the AAR3 fraction. Nonetheless, what is of upmost importance in our study is to identify over-represented clones (from the top 100 candidates) and to proceed to the rescue of highly enriched scFv and not isolated clones. Indeed, during phage-display selections, the reads of greatest interest will have the greatest depth of coverage, having expanded in the pool and thus receiving greater proportional read depth.

### ETHICS STATEMENT

All animal experiments were performed in conformity with the Guide for the Care and Use of Laboratory Animals (NIH Publication No. 85–23, revised 1996) and were accredited by the local ethical committee (Animal Care and Use Committee of Bordeaux, France under the No. 50120192).

### AUTHOR CONTRIBUTIONS

AH, FO, M-JJ-V, JL-T, MS, SK, M-PL, and GC-S conceived and designed the experiments. PM provided the library. MS sequenced the data. NH processed the raw data. VG designed the analysis algorithms and PD implemented the tool for NGS. CH, JKuijpers, AG, and JKorlach supervised the sequencing procedure. GC-S and M-PL supervised the project. AH, MS, GC-S and M-PL wrote the article. All the authors have read and approved the final manuscript.

### ACKNOWLEDGMENTS

We would like to thank Franck Salin for his helpful advice on DNA amplification and analysis using the Agilent 2100 Bioanalyzer. We are really grateful to Alexandre Fontayne for his help in the P3 patenting process. We thank Roberto Lleras for helpful information. We are grateful to Gérard Lefranc for helpful comments, Arthur Lavoie and Karthik Kalyan for IMGT/HighV-QUEST and to all IMGT team members for their constant motivation. IMGT® is Academic Institutional Member of the International Medical Informatics Association (IMIA) and of the Global Alliance for the Genomics and Health (GA4GH).

### FUNDING

IMGT® is currently supported by the Centre National de la Recherche Scientifique (CNRS); the Ministère de l′Enseignement Supérieur et de la Recherche (MESR); the Montpellier University, France; the Agence Nationale de la Recherche (ANR) Labex MabImprove [ANR-10-LABX-5301]; BioCampus Montpellier; Région Languedoc-Roussillon [Grand Plateau Technique pour la Recherche (GPTR)]. This work was granted access to the HPC@ LR and to the High Performance Computing (HPC) resources of the Centre Informatique National de l′Enseignement Supérieur (CINES) and to Très Grand Centre de Calcul (TGCC) of the Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA) under the allocation [036029] (2010–2017) made by GENCI (Grand Equipement National de Calcul Intensif). This study was achieved within the context of the Laboratory of Excellence TRAIL ANR-10-LABX-57, referenced ANR-10- LABX-0057 and named TRAIL. A public grant from the French National Agency within the context of the Investments for the Future Program (ANR-13-BSV5-0018 SVSE5 Program), named ATHERANOS supported this work.

### REFERENCES

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at http://www.frontiersin.org/articles/10.3389/fimmu.2017.01796/ full#supplementary-material.

of immunoglobulins and T cell receptors. *PLoS One* (2015) 10(11):e0142353. doi:10.1371/journal.pone.0142353

**Conflict of Interest Statement:** CH, NH, JKuijpers, and JKorlach are full-time employees at Pacific Biosciences, a company developing single-molecule sequencing technologies. MS was a full-time employee at Pacific Biosciences at the time of this work. PM is full-time employee at LFB, a pharmaceutical group specializing in biological medicinal products. The other authors declare that they have no conflict of interest.

*Copyright © 2017 Hemadou, Giudicelli, Smith, Lefranc, Duroux, Kossida, Heiner, Hepler, Kuijpers, Groppi, Korlach, Mondon, Ottones, Jacobin-Valat, Laroche-Traineau and Clofent-Sanchez. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

### *Arjun K. Mishra1,2 and Roy A. Mariuzza1,2\**

*1W. M. Keck Laboratory for Structural Biology, Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Rockville, MD, United States, 2Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, College Park, MD, United States*

Affinity maturation is the process whereby the immune system generates antibodies of higher affinities during a response to antigen. It is unique in being the only evolutionary mechanism known to operate on a molecule in an organism's own body. Deciphering the structural mechanisms through which somatic mutations in antibody genes increase affinity is critical to understanding the evolution of immune repertoires. Next-generation sequencing (NGS) has allowed the reconstruction of antibody clonal lineages in response to viral pathogens, such as HIV-1, which was not possible in earlier studies of affinity maturation. Crystal structures of antibodies from these lineages bound to their target antigens have revealed, at the atomic level, how antibodies evolve to penetrate the glycan shield of envelope glycoproteins, and how viruses in turn evolve to escape neutralization. Collectively, structural studies of affinity maturation have shown that increased antibody affinity can arise from any one or any combination of multiple diverse mechanisms, including improved shape complementarity at the interface with antigen, increased buried surface area upon complex formation, additional interfacial polar or hydrophobic interactions, and preorganization or rigidification of the antigenbinding site.

Keywords: antibody, affinity maturation, somatic hypermutation, HIV-1, structural biology, next-generation sequencing

### INTRODUCTION

The ability of the humoral immune system to generate high-affinity binders for virtually any antigen is predicated on its capacity to produce large repertoires of antibodies encompassing a vast array of specificities and to then select members of this repertoire with high affinity for a particular immunogen (1–3). The extensive sequence diversity of antibody molecules derives from several sources: (i) combinatorial diversification, whereby two sets of light (L) chain gene segments, VL and JL, and three sets of heavy (H) chain gene segments, VH, D, and JH, rearrange to produce functional variable (V) regions; (ii) imprecise joining of these gene segments; and (iii) somatic hypermutation, by which point mutations, as well as insertions and deletions (indels), are introduced throughout the sequences encoding L and H chains (4). B cells expressing antibodies with improved affinity are better equipped to compete for antigen and thus receive signals that result in preferential expansion and further antibody sequence diversification *via* additional rounds of somatic hypermutation (1–3). Through this rapid evolutionary process of mutation and selection, antibody affinity typically improves 10- to 5,000-fold during the course of an immune response, bolstering host defense.

Recent advances in next-generation sequencing (NGS) have revolutionized the analysis of antibody repertoires by dramatically increasing sample depth compared to previous low-throughput

#### *Edited by:*

*Prabakaran Ponraj, Intrexon, United States*

### *Reviewed by:*

*Robyn Stanfield, The Scripps Research Institute, United States Stanley Nithianantham, University of California, Davis, United States*

> *\*Correspondence: Roy A. Mariuzza<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to B Cell Biology, a section of the journal Frontiers in Immunology*

*Received: 16 November 2017 Accepted: 15 January 2018 Published: 01 February 2018*

#### *Citation:*

*Mishra AK and Mariuzza RA (2018) Insights into the Structural Basis of Antibody Affinity Maturation from Next-Generation Sequencing. Front. Immunol. 9:117. doi: 10.3389/fimmu.2018.00117*

methods (5). In addition, new methods have been developed for single-cell sequencing, which allow large-scale determination of paired L and H chains. These advances, in conjunction with computational tools for reconstituting antibody clonal lineages, can provide a genetic record for the evolutionary processes of recombination and somatic hypermutation in immune responses to specific microbial pathogens. Crystal structures of affinitymatured and germ line antibodies from such lineages in complex with their target antigens have produced new insights into the molecular basis of affinity maturation.

Here, we first review our understanding of the basic biophysical principles underlying affinity maturation before the arrival of NGS. We then summarize what studies of bulk B cell populations by NGS have taught us about the general features of antibody repertoire selection. Finally, we discuss structural studies of reconstructed antibody clonal lineages with special emphasis on the immune response to HIV-1, which has so far benefited most from the application of NGS and single-cell analysis to better understanding affinity maturation (6) (**Table 1**).

### STUDIES OF AFFINITY MATURATION PRIOR TO NGS

Before the advent of NGS, a number of structural studies were carried out comparing affinity-matured antibodies and their putative germ line precursors bound to the same antigen. In studies involving small molecules (haptens) such as phenyloxazolone

and nitrophenyl phosphonate, rather than proteins, it was found that somatic mutations in complementarity-determining region (CDR) residues directly or indirectly implicated in binding hapten permit the formation of additional hydrogen bonds, electrostatic interactions, and van der Waals contacts (23–26). A particularly revealing case involved the matured 48G7 antibody, which binds nitrophenyl phosphonate ~3,000-fold more tightly than its germ line counterpart 48G7g (26). Large changes in the conformation of the antigen-binding site (paratope) of 48G7g were observed upon hapten engagement by this germ line antibody, whereas the free and hapten-bound forms of affinity-matured 48G7 showed few structural differences. Thus, affinity maturation in this case appeared to be driven largely by a mechanism of preorganizing the paratope into a conformation favorable for binding its hapten ligand (26). Such conformational preorganization was accompanied by a decrease in the flexibility of the paratope during the maturation process, which may increase specificity for the target antigen while reducing the possibility of cross-reactivity with other antigens, including self-antigens (27–29). The antibody maturation process appears to simultaneously select for both higher binding affinity and increased thermodynamic stability. In a study of matured antibody 93F3, which recognizes a small hapten, somatic mutations in the paratope that increased affinity were found to reduce the melting temperature of 93F3 compared to its germ line precursor. However, the destabilizing effects of these mutations were compensated by additional somatic mutations in the VL/VH interface, distal to the paratope (29).

The first structural study of the maturation of an antibody response to a protein antigen, instead of a hapten, involved a set of closely related antibodies specific for hen egg white lysozyme (HEL) (30). These antibodies represented different stages of affinity maturation, whereby the number of somatic mutations correlated with increasing affinity. Surprisingly, improved affinity could not be attributed to the formation of additional hydrogen bonds or salt bridges or to an increase in total buried surface area. Instead, affinity maturation resulted mainly from burial of increasing amounts of hydrophobic surface at the expense of polar surface, accompanied by improved shape complementarity at the VH–HEL interface. The increase in hydrophobic interactions resulted from highly correlated structural rearrangements in antibody residues at the periphery of the interface with antigen, adjacent to the central energetic hot spot (30). Indeed, the periphery may offer more suitable sites for optimization because these regions are typically more flexible and tolerant to mutations than central sites (12), in agreement with the finding that somatic hypermutation spreads structural diversity generated by V(D)J recombination from central to peripheral regions of the antibody binding site (31).

Collectively, these structural studies showed that increased antibody affinity for small haptens or model protein antigens such as HEL can arise from any one or any combination of several variables, including additional interfacial hydrogen bonds or van der Waals contacts, conformational preorganization of the paratope, improved shape complementarity at the interface with antigen, or increased burial of total or hydrophobic surface area. These same basic strategies, as well as others, govern affinity maturation of antibody responses to biological antigens such as the envelope glycoproteins of HIV-1 and other viral pathogens, as discussed below. We first summarize what NGS of bulk B cell populations has taught us about antibody repertoire selection. We then discuss recent insights into affinity maturation gained from structural studies of antibody clonal lineages that were reconstructed using NGS (**Table 1**).

### NGS ANALYSIS OF ANTIBODY REPERTOIRES IN BULK B CELL POPULATIONS

Next-generation sequencing of paired antibody L and H chains combined with computational modeling of antibody structures has been used to profile human antibody repertoire selection and maturation at the population level (5). In the most exhaustive study to date, a comparison of ~55,000 VL/VH pairs from naïve B cells with ~120,000 VL/VH pairs from antigen-experienced B cells, all isolated from human peripheral blood, showed that VL and VH genes pair in a purely combinatorial fashion with no detectable biases, but that certain VL/VH gene pairs are significantly depleted or enriched in the antigen-experienced repertoire compared to the naïve repertoire (32). Repertoire-wide computational structure prediction was carried out to characterize the physiochemical properties of the antibody paratopes. Whereas no appreciable differences in paratope hydrophobicity or solvent-accessible surface area were evident in antigen-experienced versus naïve antibodies, antigen-experienced VLCDR3 and VHCDR3 amino acid sequences displayed slightly increased positive charge compared to naïve sequences (32). Overall, however, the evolutionary processes of somatic hypermutation and affinity selection that occur in periphery blood did not leave a distinctive physiochemical imprint on the antigen-experienced antibody repertoire, even at the level of CDR3s, which are a major focus for somatic hypermutation. By contrast, bone marrow B cells expressing antibodies with positively charged CDR3 loops undergo preferential elimination at discrete developmental checkpoints before entering the periphery, possibly as a mechanism for reducing the risk of self-reactivity (33).

In a study to determine whether NGS could be used to detect antigen-specific sequences in bulk B cell populations, an analysis of identical CDR3 sequences that were shared by individuals previously vaccinated against *Haemophilus influenzae* type b identified a number of sequences known to be specific for this bacterium (34). Conserved CDR3 sequences were also observed in patients recovering from acute dengue infection (35), indicating convergent antibody evolution in different individuals exposed to the same antigens. In another study, NGS and single-cell sorting of peripheral blood plasmablasts were used to profile the acute antibody response to influenza A vaccination (36). Antibodies able to neutralize the virus were selected bioinformatically from clonal families. These vaccine-induced antibodies contained on average >30 somatic mutations overall. Notably, some antibodies exhibited higher affinities for hemagglutinins (HAs) from prior years' influenza strains than for the HA of the immunizing strain, suggesting recall of memory B cells expressing antibodies that had previously undergone affinity maturation (36).

### STUDIES OF AFFINITY MATURATION AFTER NGS

Next-generation sequencing coupled with bioinformatics analysis has allowed, for the first time, the reconstruction of antibody clonal lineages and inference of germ line progenitor sequences, neither of which was possible in earlier studies of affinity maturation (37). However, an important caveat is that germ line sequences are predicted sequences that may differ from the true unmutated ancestor sequences. Whereas mutations in VL and VH gene segments can be identified with high confidence, the original VLJL and (especially) VHDJH junctional sequences of germ line antibodies are uncertain. In particular, it is impossible to know if insertions or deletions in these sequences took place during V(D) J recombination or were introduced during B cell affinity maturation. As a consequence, statistical methods must be used to infer the most likely unmutated common ancestor for an aligned set of sequences that are taken to be clonally related (37).

As measured by surface plasmon resonance (SPR), a putative germ line precursor of the anti-HIV-1 antibody 2F5, which recognizes the gp41 subunit of the HIV-1 envelope glycoprotein (Env), bound recombinant gp41 with ~500-fold lower affinity than the matured antibody (*K*<sup>D</sup> = 0.7 µM versus 1.2 nM) (38). Micromolar *K*Ds were also reported for germ line precursors of broadly neutralizing antibodies (bNAbs) CH01 and CH04, which recognize the V2/V3 quaternary epitope of the gp120 subunit of HIV-1 Env (39). Although seemingly low, such affinities are nevertheless sufficient to trigger affinity maturation of unmutated B cells *in vivo* (40, 41).

By contrast, the putative germ line ancestors of several bNAbs specific for the CD4 binding site of HIV-1 Env (b12, NIH45-46, and 3BNC60) failed to show detectable binding to recombinant Envs, raising the question of how B cell maturation leading to the eventual production of these bNAbs was initiated (42, 43). Moreover, this failure was observed even though the amino acid sequences of the V(D)J junctions of the affinity-matured antibodies were left unchanged in the reconstructed germ line versions. One possibility is that maturation of anti-CD4 binding site bNAb precursors was triggered by non-HIV antigens and that the resultant antibody intermediates serendipitously cross-reacted with Env. More likely, however, interactions between proteins in solution (3D affinity), as measured by SPR or related techniques, differ from those at contacts between two cells or between a cell and a virus (2D affinity) (44). For example, whereas SPR was unable to detect any binding between CD4 and MHC class II (45), the affinity of CD4 for MHC class II on B cells could be measured in 2D using CD4-functionalized supported lipid bilayers (46). Therefore, under physiological conditions, germ line precursors of anti-CD4 binding site bNAbs, expressed on the surface of B cells, might be engaged by membrane-anchored Env on the virion surface or on the surface of infected cells with sufficient affinity to trigger B cell maturation. In support of this idea, the germ line precursors of several anti-influenza HA bNAbs were found to bind to HA only when presented on membranes in the form of cell surface IgMs; as soluble IgGs, these precursors had no detectable affinity for HA (47). In the following sections, we have selected representative examples of affinity maturation in order to illustrate the multiple structural strategies that the antibodies employ to increase potency and breadth of pathogen neutralization.

### Preorganization, Rigidification, and Reorientation

Antibody CH58 was isolated from a participant in the RV144 HIV vaccine efficacy trial. Like most bNAbs elicited in response to HIV-1 infection, CH58 is highly mutated (7). The structure of affinity-matured CH58 in complex with an Env V2 peptide showed that this bNAb targets V2 residue Lys169, which is a site of vaccine-induced immune pressure. Structures have also been determined for the putative germ line precursor of CH58 in unliganded form and bound to the V2 peptide (8). A comparison of these structures revealed that affinity maturation of CH58 is driven by the formation of two new salt bridges linking VLCDR1Asn31→Asp and VHCDR1Ser28→Arg to Lys171 and Asp180 of V2, respectively (**Figure 1**) (8). In addition, VLCDR3 in the unbound germ line precursor adopts a different conformation in the CH58–V2 complex, implying flexibility. By contrast, the conformation of VLCDR3 in the CH58–V2 complex is nearly identical to that in unbound matured CH58. Such preorganization of the CH58 paratope into a configuration more suitable for binding V2, accompanied by rigidification of VLCDR3 to lower the entropic cost of complex formation, further contributes to the 2,000-fold affinity increase during maturation. Paratope preorganization and rigidification have also been described for the CH65 lineage of anti-influenza virus HA antibodies (21), underscoring the general utility of these mechanisms for improving affinity (26–29).

A related study used hydrogen/deuterium exchange in combination with mass spectrometry (HDX/MS) to investigate how affinity maturation alters the dynamics of bNAbs against HIV-1 Env (12). Importantly, the high variability and constantly evolving nature of HIV-1 Env distinguish this conformationally dynamic glycoprotein from the static model antigens used in studies of affinity maturation prior to NGS (23–26, 30). HDX/MS directly measures local protein dynamics by monitoring backbone amide deuterium uptake when the protein is diluted into a solution of D2O. Dynamic regions of a protein take up deuterium more rapidly than stable regions. HDX/MS was used to compare the local dynamics of the predicted germ line and affinity-matured forms of two bNAbs (VRC03 and VRC-PG04) specific for the CD4 binding site of HIV-1 Env (12). In both cases, the paratopes of the matured bNAbs were less dynamic overall that those of their germ line counterparts, in agreement with previous evidence that paratopes become more rigid during the maturation process (28, 30, 48). Surprisingly, however, the largest decreases in dynamics occurred at the periphery of the paratopes, at sites adjacent to Env glycans, rather than at primary Env-contacting sites. A similar pattern was observed for a bNAb (CAP256-VRC26) specific for

Figure 1 | Affinity maturation through formation of additional interactions with antigen. (A) Close-up view of the interface between the germ line precursor of antibody CH58 and the V2 peptide of HIV-1 envelope glycoprotein in the vicinity of V2 Lys171 (Protein Data Bank accession code 4RIS) (8). VL is green; V2 is magenta. (B) In affinity-matured CH58, the somatic mutation VLAsn31→Asp allows formation of a stabilizing salt bridge (dotted black line) to V2 Lys171. VL is teal. (C) Close-up of the interface between the germ line precursor of CH58 and the V2 peptide in the vicinity of VHSer28 (4HPO) (7). VH is green; V2 is magenta. (D) In affinity-matured CH58, the mutation VHSer28→Arg results in formation of a bidentate salt bridge with V2 Asp180. VH is teal. This residue was not visible in (C) due to disorder in the C-terminus of the V2 peptide.

the V1/V2 quaternary epitope of HIV-1 Env. This stabilization of the paratope periphery may serve to minimize potential clashes with nearby Env glycans, while maintaining critical binding interactions mediated by the center of the paratope. It is probably not coincidental that a similar focus of affinity maturation on sites peripheral rather than central to the interface with antigen was also observed for anti-HEL antibodies (30).

Antibodies of the CH103 lineage block the CD4 binding site of HIV-1 *via* interactions dominated by VHCDR3 (13). Affinity maturation of CH103 is associated with mutations in both contacting and non-contacting residues, including framework (FR) residues distant from the interface with Env. Structural analysis of the putative germ line precursor of CH103 and of two intermediates in the maturation pathway revealed a shift in the relative orientation of the VL and VH domains during evolution of the CH103 lineage, corresponding to a root mean squared deviation in α-carbon positions of 2.1 Å (**Figure 2A**) (14). This shift is mediated by several mutations at the VL/VH interface, including a leucine-to-valine substitution at FR position VL46 that not only contributes to reorienting the VL domain but also to reconfiguring VHCDR3 (**Figure 2B**). Although not as important as the VLLeu46→Val mutation, the neighboring VLTyr49→Phe and VHPhe100→Tyr mutations, which are also located in the VL/ VH interface, may further contribute to VL/VH reorientation. Most likely, VL/VH reorientation occurred in response to insertions in the V5 loop of Env during infection, which had allowed the virus to escape neutralization by progenitors of CH103. Displacement of VL away from V5 allowed accommodation of insertions in V5 without steric hindrance (14). In addition, the conformation of VHCDR3 in the germ line precursor of CH103 is incompatible with gp120 binding, at least as observed in the CH103–gp120 complex (13), thereby necessitating rearrangement of this loop during the maturation process (14).

In sharp contrast to bNAbs against HIV-1, which are highly mutated, the potent human anti-Middle East respiratory syndrome coronavirus antibody m336 is almost germ line, with only one somatic mutation in the H chain (49). The structure of m336 in complex with the MERS-CoV receptor-binding domain showed that the IGV1-69-derived H chain contributes >85% of the binding surface. The subnanomolar affinity of m336 despite the near absence of somatic mutations results from direct interactions with germ line-encoded VHCDR2 and VH framework region 3 (VHFR3) residues and with recombination-generated residues in the VHDJH junction (49). Like m336, the anti-influenza HA bNAb CR6261 uses the IGV1-69 VH gene segment (47). Virus neutralization depended solely on the H chain, and only seven somatic mutations in VHCDR1 and VHFR3 were required for maximum affinity.

To systematically dissect the contribution of mutations in FR residues to affinity maturation, deep mutational scanning was applied to the anti-vascular endothelial cell growth factor antibody G6.31 (50). A number of FR mutations at positions distal to the CDRs were found to improve the affinity and/or thermostability of G6.31. In particular, the FR mutation VLPhe83→Ala, which is ~25 Å away from the antigen-binding site, increased both affinity and stability by altering the orientation of the constant domains (CL and CH1) relative to VL and VH, as well as the orientation of VL to VH (50). As measured by HDX/MS, the VLPhe83→Ala mutation modulated the interdomain conformational dynamics of antibody G6.31. Furthermore, analysis of > 5,000 human VL sequences showed that somatic mutations occur frequently at position 83, strongly suggesting its biological relevance in repertoire selection.

Figure 2 | Reorientation of VL and VH domains in response to viral escape mutations. (A) Superposition of the germ line precursor of CH103 (VL and VH domains are green and cyan, respectively) (4QHL) (14) onto matured CH103 (VL and VH domains are red and magenta, respectively) in complex with envelope glycoprotein gp120 (yellow) (4JAN) (13). During affinity maturation, a shift occurred in the orientation of VL with respect to VH. The shift is an adaptation to insertions in the gp120 V5 loop during infection. Movement of VL away from gp120 enables accommodation of the V5 insertion. (B) Close-up view of the VL/VH interface in the vicinity of the VLLeu46→Val mutation showing changes in interdomain contacts and rearrangement of VHCDR3.

5 **110**

### Affinity Maturation of Glycan-Binding Antibodies

Besides high sequence variability, another feature of HIV-1 Env that distinguishes it from model antigens such as HEL used in previous studies of affinity maturation is glycosylation. Indeed, extensive N-glycosylation masks much of the Env protein surface from antibody recognition. Nevertheless, a number of potent bNAbs have been discovered that penetrate this glycan shield and engage both carbohydrate and protein antigenic determinants (51, 52). In the most thoroughly studied example to date, the PGT121 family of bNAbs was shown to bind to N-glycans located in a high-mannose patch centered on the highly conserved Asn332 glycan and to protein elements at the base of the V3 loop (15, 16).

As revealed by NGS and X-ray crystallography, the putative germ line precursor of the PGT121 family splits into two evolutionary branches that differ considerably in how they interact with Env glycans (**Figures 3A,B**). One branch, exemplified by PGT124, contacts only the Asn332 glycan (16), whereas the other branch, exemplified by PGT122, also contacts the Asn137, Asn156, and Asn301 glycans (15). PGT124 and PGT122 employ a common set of CDR residues to contact the Asn332 glycan and V3 loop, nearly all of which are also found in the germ line precursor. This conservation suggests that a critical event in triggering the antibody response is simultaneous recognition of both carbohydrate and protein determinants.

Antibodies PGT124 and PGT122 share similar binding site architectures with long VHCDR3 loops that pack against the entire length of the Asn332 glycan, thereby penetrating the glycan shield to reach the Env protein surface below (**Figure 3C**) (16). In both antibodies, as well as in the germ line precursor, a closed face on one side of the paratope engages the Asn332 glycan. A second, open face differs between PGT124 and PGT122. For PGT124, the open face enables avoidance of neighboring glycans through a shift of VHCDR3 away from its equivalent position in PGT122 (**Figure 3C**). For PGT122, by contrast, mutations during affinity maturation result in productive interactions between VHCDR2 and the Asn137 glycan. In agreement with the structural data, deletion of the Asn332 glycan abolished PGT124 neutralization of nearly all HIV-1 isolates, whereas deletion of neighboring glycans had little or no effect (16). Conversely, PGT122 is less reliant on the Asn332 glycan than PGT124 for virus neutralization because of its ability to utilize alternative glycans at Asn137 and Asn301 to achieve high-affinity binding.

composed of V1 and V3, as well as all four glycans at N137, N156, N301, and N332. (C) VHCDR3 of PGT122 (yellow) contacts both the N137 and N332 glycans.

VHCDR3 of PGT124 (wheat) contacts only the N332 glycan due to a shift in position relative to VHCDR3 of PGT122.

Recently, NGS of HIV-1 longitudinal cohorts has been performed to investigate coevolution of HIV-1 Env and antibodies targeting this glycoprotein and to identify viral variants that initiate maturation of bNAbs (18, 53, 54). These studies have shown that viral escape generates a pool of diverse epitope variants and that somatic hypermutation, acting in parallel, creates antibodies with differential ability to neutralize these variants. Thus, antibodies from the CAP256 and PCT64 lineages, which were isolated independently from two different HIV-1 infected African patients, both target the V2 apex epitope of the Env trimer (18, 53, 54). The crystal structure of bNAb PCT64-35B, isolated at 35 months postinfection, showed that the somatically mutated 25-residue VHCDR3 loop adopts a β-hairpin conformation that projects above the other CDRs (18). The extended conformation and anionic character of this VHCDR3, which contains at least one sulfotyrosine, probably enable this loop to penetrate between the glycans that shield the V2 apex epitope to contact the positively charged V1/V2 protein surface of the Env trimer. Moreover, in the PCT64 donor, Env glycoform heterogeneity may have played a role in activating B cell precursors of the PCT64 lineage by allowing germ line antibody binding to early Env trimer forms lacking complex or hybrid glycans at key positions (18).

According to the polyreactivity hypothesis, B cells initially produce germ line antibodies with conformationally flexible combining sites that are able to recognize diverse antigens with low affinity (12, 28, 48). In this way, the immune system can respond to an enormous variety of potential antigens, whose numbers dwarf theoretical estimates of the clonal diversity of germ line antibodies. As described earlier, studies of immune responses to haptens, model proteins, and HIV-1 Env have shown that affinity maturation generates antibodies with higher affinity and specificity than germ line antibodies, at least in part through rigidification of the paratope. However, a recent study of glycan-specific antibodies has suggested an alternative pathway for antibody evolution that is distinct from the polyreactive germ line pathway (55).