Automated classification of antibody complementarity determining region 3 of the heavy chain (H3) loops into canonical forms and its application to protein structure prediction

More than 370-Fold Increase in Antibody Affinity to Estradiol-17β by Exploring Substitutions in the VH-CDR3

Antibodies that specifically target biomarkers are essential in clinical diagnosis. Genetic engineering has assisted in designing novel antibodies that offer greater antigen-binding affinities, thus providing more sensitive immunoassays. We have succeeded in generating a single-chain Fv fragment (scFv) targeted estradiol-17β (E₂) with more than 370-fold improved affinity, based on a strategy focusing the complementarity-determining region 3 in the V_H domain (V_H-CDR3). Systematic exploration of amino acid substitutions therein, using a clonal array profiling, revealed a cluster of four substitutions, containing H99P and a serial substitution E100eN-I100fA-L100gQ that lead to a 90-fold increase in E₂-binding affinity. This substitution quartet in the V_H-CDR3, combined with the substitution cluster I29V/L36M/S77G in the V_L domain, resulted in a scFv fragment with a further increase in the affinity (K_a, 3.2 × 10¹⁰ M^-1). This enabled a highly sensitive enzyme-linked immunosorbent assay capable of detecting up to 0.78 pg/assay. The current study has, thus, focused on the significance of reevaluating the potential of mutagenesis targeting the V_H-CDR3, and encouraging the production and use of engineered antibodies that enable enhanced sensitivities as next-generation diagnostic tools.

Public Immunity: Evolutionary Spandrels for Pathway-Amplifying Protective Antibodies

Humoral immunity is seeded by affinity between the B cell receptor (BCR) and cognate antigen. While the BCR is a chimeric display of diverse antigen engagement solutions, we discuss its functional activity as an ‘innate-like’ immune receptor, wherein genetically hardwired antigen complementarity can serve as reproducible templates for pathway-amplifying otherwise immunologically recessive antibody responses. We propose that the capacity for germline reactivity to new antigen emerged as a set of evolutionary spandrels or coupled traits, which can now be exploited by rational vaccine design to focus humoral immunity upon conventionally immune-subdominant antibody targets. Accordingly, we suggest that evolutionary spandrels account for the necessary but unanticipated antigen reactivity of the germline antibody repertoire.

A Collection of Designed Peptides to Target SARS-CoV-2 Spike RBD-ACE2 Interaction

The angiotensin-converting enzyme 2 (ACE2) is the receptor used by SARS-CoV and SARS-CoV-2 coronaviruses to attach to cells via the receptor-binding domain (RBD) of their viral spike protein. Since the start of the COVID-19 pandemic, several structures of protein complexes involving ACE2 and RBD as well as monoclonal antibodies and nanobodies have become available. We have leveraged the structural data to design peptides to target the interaction between the RBD of SARS-CoV-2 and ACE2 and SARS-CoV and ACE2, as contrasting exemplar, as well as the dimerization surface of ACE2 monomers. The peptides were modelled using our original method: PiPreD that uses native elements of the interaction between the targeted protein and cognate partner(s) that are subsequently included in the designed peptides. These peptides recapitulate stretches of residues present in the native interface plus novel and highly diverse conformations surrogating key interactions at the interface. To facilitate the access to this information we have created a freely available and dedicated web-based repository, PepI-Covid19 database, providing convenient access to this wealth of information to the scientific community with the view of maximizing its potential impact in the development of novel therapeutic and diagnostic agents.

A Single Human VH-gene Allows for a Broad-Spectrum Antibody Response Targeting Bacterial Lipopolysaccharides in the Blood

B cell receptors (BCRs) display a combination of variable (V)-gene-encoded complementarity determining regions (CDRs) and adaptive/hypervariable CDR3 loops to engage antigens. It has long been proposed that the former tune for recognition of pathogens or groups of pathogens. To experimentally evaluate this within the human antibody repertoire, we perform immune challenges in transgenic mice that bear diverse human CDR3 and light chains but are constrained to different human V_H-genes. We find that, of six commonly deployed V_H sequences, only those CDRs encoded by IGHV1-2^∗02 enable polyclonal antibody responses against bacterial lipopolysaccharide (LPS) when introduced to the bloodstream. The LPS is from diverse strains of gram-negative bacteria, and the V_H-gene-dependent responses are directed against the non-variable and universal saccrolipid substructure of this antigen. This reveals a broad-spectrum anti-LPS response in which germline-encoded CDRs naturally hardwire the human antibody repertoire for recognition of a conserved microbial target.

Antibody Focusing to Conserved Sites of Vulnerability: The Immunological Pathways for 'Universal' Influenza Vaccines

Influenza virus remains a serious public health burden due to ongoing viral evolution. Vaccination remains the best measure of prophylaxis, yet current seasonal vaccines elicit strain-specific neutralizing responses that favor the hypervariable epitopes on the virus. This necessitates yearly reformulations of seasonal vaccines, which can be limited in efficacy and also shortchange pandemic preparedness. Universal vaccine development aims to overcome these deficits by redirecting antibody responses to functionally conserved sites of viral vulnerability to enable broad coverage. However, this is challenging as such antibodies are largely immunologically silent, both following vaccination and infection. Defining and then overcoming the immunological basis for such subdominant or ‘immuno-recessive’ antibody targeting has thus become an important aspect of universal vaccine development. This, coupled with structure-guided immunogen design, has led to proof-of-concept that it is possible to rationally refocus humoral immunity upon normally ‘unseen’ broadly neutralizing antibody targets on influenza virus.

Seeking high-priority mutations enabling successful antibody-breeding: systematic analysis of a mutant that gained over 100-fold enhanced affinity

"Antibody-breeding" has provided therapeutic/diagnostic antibody mutants with greater performance than native antibodies. Typically, random point mutations are introduced into the VH and VL domains of parent antibodies to generate diverse libraries of single-chain Fv fragments (scFvs), from which evolved mutants are selected. We produced an scFv against estradiol-17β with 11 amino acid substitutions and a >100-fold improved affinity constant (Ka = 1.19 × 1010 M-1) over the parent scFv, enabling immunoassays with >30-fold higher sensitivity. We systematically analyzed contributions of these substitutions to the affinity enhancement. Comparing various partial scFv revertants based on their Kas indicated that a revertant with four substitutions (VH-L100gQ, VL-I29V, -L36M, -S77G) exhibited somewhat higher affinity (Ka = 1.46 × 1010 M-1). Finally, the VH-L100gQ substitution, occurring in VH complementarity-determining region (CDR) 3, was found to be the highest-priority for improving the affinity, and VL-I29V and/or VL-L36M cooperated significantly. These findings encouraged us to reconsider the potential of VH-CDR3-targeting mutagenesis, which has been frequently attempted. The substitution(s) wherein might enable a "high rate of return" in terms of selecting mutants with dramatically enhanced affinities. The "high risk" of generating a tremendous excess of "junk mutants" can be overcome with the efficient selection systems that we developed.

Understanding the Significance and Implications of Antibody Numbering and Antigen-Binding Surface/Residue Definition

Monoclonal antibodies are playing an increasing role in both human and animal health. Different strategies of protein and chemical engineering, including humanization techniques of non-human antibodies were applied successfully to optimize clinical performances of antibodies. Despite the emergence of techniques allowing the development of fully human antibodies such as transgenic Xeno-mice, antibody humanization remains a standard procedure for therapeutic antibodies. An important prerequisite for antibody humanization requires standardized numbering methods to define precisely complementary determining regions (CDR), frameworks and residues from the light and heavy chains that affect the binding affinity and/or specificity of the antibody-antigen interaction. The recently generated deep-sequencing data and the increasing number of solved three-dimensional structures of antibodies from human and non-human origins have led to the emergence of numerous databases. However, these different databases use different numbering conventions and CDR definitions. In addition, the large fluctuation of the variable chain lengths, especially in CDR3 of heavy chains (CDRH3), hardly complicates the comparison and analysis of antibody sequences and the identification of the antigen binding residues. This review compares and discusses the different numbering schemes and "CDR" definition that were established up to date. Furthermore, it summarizes concepts and strategies used for numbering residues of antibodies and CDR residues identification. Finally, it discusses the importance of specific sets of residues in the binding affinity and/or specificity of immunoglobulins.

Role of Non-local Interactions between CDR Loops in Binding Affinity of MR78 Antibody to Marburg Virus Glycoprotein

An atomic-detail model of the Marburg virus glycoprotein in complex with a neutralizing human monoclonal antibody designated MR78 was constructed using Phenix.Rosetta starting from a 3.6Å crystallographic density map. The Asp at T6 in the HCDR3's bulged torso cannot form the canonical salt bridge as position T2 lacks an Arg or Lys residue. It instead engages in a hydrogen bond interaction with a Tyr contributed by the HCDR1 loop. This inter-CDR loop interaction stabilizes the bulged conformation needed for binding to the viral glycoprotein: a Tyr to Phe mutant displays a binding affinity reduced by a factor of at least 10. We found that 5% of a database of 465 million human antibody sequences has the same residues at T2 and T6 positions in HCDR3 and Tyr in HCDR1 that could potentially form this Asp-Tyr interaction, and that this interaction might contribute to a non-canonical bulged torso conformation.

Length-independent structural similarities enrich the antibody CDR canonical class model

Complementarity-determining regions (CDRs) are antibody loops that make up the antigen binding site. Here, we show that all CDR types have structurally similar loops of different lengths. Based on these findings, we created length-independent canonical classes for the non-H3 CDRs. Our length variable structural clusters show strong sequence patterns suggesting either that they evolved from the same original structure or result from some form of convergence. We find that our length-independent method not only clusters a larger number of CDRs, but also predicts canonical class from sequence better than the standard length-dependent approach.

To demonstrate the usefulness of our findings, we predicted cluster membership of CDR-L3 sequences from 3 next-generation sequencing datasets of the antibody repertoire (over 1,000,000 sequences). Using the length-independent clusters, we can structurally classify an additional 135,000 sequences, which represents a ∼20% improvement over the standard approach. This suggests that our length-independent canonical classes might be a highly prevalent feature of antibody space, and could substantially improve our ability to accurately predict the structure of novel CDRs identified by next-generation sequencing.

Computational Tools for Aiding Rational Antibody Design

Antibodies are a group of proteins responsible for mediating immune reactions in vertebrates. They are able to bind a variety of structural motifs on noxious molecules tagging them for elimination from the organism. As a result of their versatile binding properties, antibodies are currently one of the most important classes of biopharmaceuticals. In this chapter, we discuss how knowledge-based computational methods can aid experimentalists in the development of potent antibodies. When using common experimental methods for antibody development, we often know the sequence of an antibody that binds to our molecule, antigen, of interest. We may also have a structure or model of the antigen. In these cases, computational methods can help by both modeling the antibody and identifying the antibody-antigen contact residues. This information can then play a key role in the rational design of more potent antibodies.

Site-Directed Chemical Mutations on Abzymes: Large Rate Accelerations in the Catalysis by Exchanging the Functionalized Small Nonprotein Components

Taking advantage of antibody molecules to generate tailor-made binding sites, we propose a new class of protein modifications, termed as "site-directed chemical mutation." In this modification, chemically synthesized catalytic components with a variety of steric and electronic properties can be noncovalently and nongenetically incorporated into specific sites in antibody molecules to induce enzymatic activity. Two catalytic antibodies, 25E2 and 27C1, possess antigen-combining sites which bind catalytic components and act as apoproteins in catalytic reactions. By simply exchanging these components, antibodies 25E2 and 27C1 can catalyze a wide range of chemical transformations including acyl-transfer, β-elimination, aldol, and decarboxylation reactions. Although both antibodies were generated with the same hapten, phosphonate diester 1, they showed different catalytic activity. When phenylacetic acid 4 was used as the catalytic component, 25E2 efficiently catalyzed the elimination reaction of β-haloketone 2, whereas 27C1 showed no catalytic activity. In this work, we focused on the β-elimination reaction and examined the site-directed chemical mutation of 27C1 to induce activity and elucidate the catalytic mechanism. Molecular models showed that the cationic guanidyl group of Arg^H52 in 27C1 makes a hydrogen bond with the P═O oxygen in the hapten. This suggested that during β-elimination, Arg^H52 of 27C1 would form a salt bridge with the carboxylate of 4, thus destroying reactivity. Therefore, we utilized site-directed chemical mutation to change the charge properties of the catalytic components. When amine components 7-10 were used, 27C1 efficiently catalyzed the β-elimination reaction. It is noteworthy that chemical mutation with secondary amine 8 provided extremely high activity, with a rate acceleration [(k_cat/K_m 2)/k_uncat] of 1 000 000. This catalytic activity likely arises from the proximity effect, plus general-base catalysis associated the electrostatic interactions. In 27C1, the cationic guanidyl group of Arg^H52 is spatially close to the nitrogen of the amine components. In this microenvironment, the intrinsic pK_a of the amine is perturbed and shifts to a lower pK_a, which efficiently abstracts the α-proton during the reaction. This mechanism is consistent with the observed kinetic isotope effect (E2 or E1cB mechanism). Thus, site-directed chemical mutation provides a better understanding of enzyme functions and opens new avenues in biocatalyst research.

The origin of CDR H3 structural diversity

Antibody complementarity determining region (CDR) H3 loops are critical for adaptive immunological functions. Although the other five CDR loops adopt predictable canonical structures, H3 conformations have proven unclassifiable, other than an unusual C-terminal "kink" present in most antibodies. To determine why the majority of H3 loops are kinked and to learn whether non-antibody proteins have loop structures similar to those of H3, we searched a set of 15,679 high-quality non-antibody structures for regions geometrically similar to the residues immediately surrounding the loop. By incorporating the kink into our search, we identified 1,030 H3-like loops from 632 protein families. Some protein families, including PDZ domains, appear to use the identified region for recognition and binding. Our results suggest that the kink is conserved in the immunoglobulin heavy chain fold because it disrupts the β-strand pairing at the base of the loop. Thus, the kink is a critical driver of the observed structural diversity in CDR H3.

PyIgClassify: a database of antibody CDR structural classifications

Classification of the structures of the complementarity determining regions (CDRs) of antibodies is critically important for antibody structure prediction and computational design. We have previously performed a clustering of antibody CDR conformations and defined a systematic nomenclature consisting of the CDR, length and an integer starting from the largest to the smallest cluster in the data set (e.g. L1-11-1). We present PyIgClassify (for Python-based immunoglobulin classification; available at http://dunbrack2.fccc.edu/pyigclassify/), a database and web server that provides access to assignments of all CDR structures in the PDB to our classification system. The database includes assignments to the IMGT germline V regions for heavy and light chains for several species. For humanized antibodies, the assignment of the frameworks is to human germlines and the CDRs to the germlines of mice or other species sources. The database can be searched by PDB entry, cluster identifier and IMGT germline group (e.g. human IGHV1). The entire database is downloadable so that users may filter the data as needed for antibody structure analysis, prediction and design.

Antibody modeling using the prediction of immunoglobulin structure (PIGS) web server [corrected]

Antibodies (or immunoglobulins) are crucial for defending organisms from pathogens, but they are also key players in many medical, diagnostic and biotechnological applications. The ability to predict their structure and the specific residues involved in antigen recognition has several useful applications in all of these areas. Over the years, we have developed or collaborated in developing a strategy that enables researchers to predict the 3D structure of antibodies with a very satisfactory accuracy. The strategy is completely automated and extremely fast, requiring only a few minutes (∼10 min on average) to build a structural model of an antibody. It is based on the concept of canonical structures of antibody loops and on our understanding of the way light and heavy chains pack together.

Defining the recognition elements of Lewis Y-reactive antibodies

Antibody response to carbohydrate antigens is often independent of T cells and the process of affinity/specificity improvement is considered strictly dependent on the germinal centers. Antibodies induced during a T cell-independent type 2 (TI-2) response are less variable and less functionally versatile than those induced with T cell help. The antigen specificity consequences of accumulation of somatic mutations in antibodies during TI-2 responses of Marginal Zone (MZ) B cells is a fact that still needs explanation. Germline genes that define carbohydrate-reactive antibodies are known to sculpt antibody-combining sites containing innate, key side-chain contacts that define the antigen recognition step. However, substitutions associated with MZ B cell derived antibodies might affect the mobility and polyspecificity of the antibody. To examine this hypothesis, we analyzed antibodies reactive with the neolactoseries antigen Lewis Y (LeY) to define the residue subset required for the reactive repertoire for the LeY antigen. Our molecular simulation studies of crystallographically determined and modeled antibody-LeY complexes suggests that the heavy-chain germline gene VH7183.a13.20 and the light-chain Vκ cr1 germline gene are sufficient to account for the recognition of the trisaccharide-H determinant Types 1–4, while the specificity for LeY is driven by the CDR3 backbone conformation of the heavy chain and not the side chain interactions. These results confirm that these monoclonals use germline-encoded amino acids to recognize simple carbohydrate determinants like trisaccharide-H but relies on somatic mutations in the periphery of the combining site to modify affinity for LeY through electrostatic interactions that leads to their optimized binding. These observations bring further attention to the role of mutations in T-cell independent antibodies to distinguish self from non-self carbohydrate antigens.

A complete, multi-level conformational clustering of antibody complementarity-determining regions

Classification of antibody complementarity-determining region (CDR) conformations is an important step that drives antibody modelling and engineering, prediction from sequence, directed mutagenesis and induced-fit studies, and allows inferences on sequence-to-structure relations. Most of the previous work performed conformational clustering on a reduced set of structures or after application of various structure pre-filtering criteria. In this study, it was judged that a clustering of every available CDR conformation would produce a complete and redundant repertoire, increase the number of sequence examples and allow better decisions on structure validity in the future. In order to cope with the potential increase in data noise, a first-level statistical clustering was performed using structure superposition Root-Mean-Square Deviation (RMSD) as a distance-criterion, coupled with second- and third-level clustering that employed Ramachandran regions for a deeper qualitative classification. The classification of a total of 12,712 CDR conformations is thus presented, along with rich annotation and cluster descriptions, and the results are compared to previous major studies. The present repertoire has procured an improved image of our current CDR Knowledge-Base, with a novel nesting of conformational sensitivity and specificity that can serve as a systematic framework for improved prediction from sequence as well as a number of future studies that would aid in knowledge-based antibody engineering such as humanisation.

Antibody structure determination using a combination of homology modeling, energy-based refinement, and loop prediction

We present the blinded prediction results in the Second Antibody Modeling Assessment (AMA-II) using a fully automatic antibody structure prediction method implemented in the programs BioLuminate and Prime. We have developed a novel knowledge based approach to model the CDR loops, using a combination of sequence similarity, geometry matching, and the clustering of database structures. The homology models are further optimized with a physics-based energy function (VSGB2.0), which improves the model quality significantly. H3 loop modeling remains the most challenging task. Our ab initio loop prediction performs well for the H3 loop in the crystal structure context, and allows improved results when refining the H3 loops in the context of homology models. For the 10 human and mouse derived antibodies in this assessment, the average RMSDs for the homology model Fv and framework regions are 1.19 Å and 0.74 Å, respectively. The average RMSDs for five non-H3 CDR loops range from 0.61 Å to 1.05 Å, and the H3 loop average RMSD is 2.91 Å using our knowledge-based loop prediction approach. The ab initio H3 loop predictions yield an average RMSD of 1.28 Å when performed in the context of the crystal structure and 2.67 Å in the context of the homology modeled structure. Notably, our method for predicting the H3 loop in the crystal structure environment ranked first among the seven participating groups in AMA-II, and our method made the best prediction among all participants for seven of the ten targets.

Antibody modeling assessment II. Structures and models

To assess the state-of-the-art in antibody structure modeling, a blinded study was conducted. Eleven unpublished Fab crystal structures were used as a benchmark to compare Fv models generated by seven structure prediction methodologies. In the first round, each participant submitted three non-ranked complete Fv models for each target. In the second round, CDR-H3 modeling was performed in the context of the correct environment provided by the crystal structures with CDR-H3 removed. In this report we describe the reference structures and present our assessment of the models. Some of the essential sources of errors in the predictions were traced to the selection of the structure template, both in terms of the CDR canonical structures and VL/VH packing. On top of this, the errors present in the Protein Data Bank structures were sometimes propagated in the current models, which emphasized the need for the curated structural database devoid of errors. Modeling non-canonical structures, including CDR-H3, remains the biggest challenge for antibody structure prediction.

Computer-aided antibody design

Recent clinical trials using antibodies with low toxicity and high efficiency have raised expectations for the development of next-generation protein therapeutics. However, the process of obtaining therapeutic antibodies remains time consuming and empirical. This review summarizes recent progresses in the field of computer-aided antibody development mainly focusing on antibody modeling, which is divided essentially into two parts: (i) modeling the antigen-binding site, also called the complementarity determining regions (CDRs), and (ii) predicting the relative orientations of the variable heavy (V(H)) and light (V(L)) chains. Among the six CDR loops, the greatest challenge is predicting the conformation of CDR-H3, which is the most important in antigen recognition. Further computational methods could be used in drug development based on crystal structures or homology models, including antibody-antigen dockings and energy calculations with approximate potential functions. These methods should guide experimental studies to improve the affinities and physicochemical properties of antibodies. Finally, several successful examples of in silico structure-based antibody designs are reviewed. We also briefly review structure-based antigen or immunogen design, with application to rational vaccine development.

Phage display-based strategies for cloning and optimization of monoclonal antibodies directed against human pathogens

In the last two decades, several phage display-selected monoclonal antibodies (mAbs) have been described in the literature and a few of them have managed to reach the clinics. Among these, the anti-respiratory syncytial virus (RSV) Palivizumab, a phage-display optimized mAb, is the only marketed mAb directed against microbial pathogens. Palivizumab is a clear example of the importance of choosing the most appropriate strategy when selecting or optimizing an anti-infectious mAb. From this perspective, the extreme versatility of phage-display technology makes it a useful tool when setting up different strategies for the selection of mAbs directed against human pathogens, especially when their possible clinical use is considered. In this paper, we review the principal phage display strategies used to select anti-infectious mAbs, with particular attention focused on those used against hypervariable pathogens, such as HCV and influenza viruses.

Predicting antibody complementarity determining region structures without classification

Antibodies are used extensively in medical and biological research. Their complementarity determining regions (CDRs) define the majority of their antigen binding functionality. CDR structures have been intensively studied and classified (canonical structures). Here we show that CDR structure prediction is no different from the standard loop structure prediction problem and predict them without classification. FREAD, a successful database loop prediction technique, is able to produce accurate predictions for all CDR loops (0.81, 0.42, 0.96, 0.98, 0.88 and 2.25 Å RMSD for CDR-L1 to CDR-H3). In order to overcome the relatively poor predictions of CDR-H3, we developed two variants of FREAD, one focused on sequence similarity (FREAD-S) and another which includes contact information (ConFREAD). Both of the methods improve accuracy for CDR-H3 to 1.34 Å and 1.23 Å respectively. The FREAD variants are also tested on homology models and compared to RosettaAntibody (CDR-H3 prediction on models: 1.98 and 2.62 Å for ConFREAD and RosettaAntibody respectively). CDRs are known to change their structural conformations upon binding the antigen. Traditional CDR classifications are based on sequence similarity and do not account for such environment changes. Using a set of antigen-free and antigen-bound structures, we compared our FREAD variants. ConFREAD which includes contact information successfully discriminates the bound and unbound CDR structures and achieves an accuracy of 1.35 Å for bound structures of CDR-H3.

A new clustering of antibody CDR loop conformations

Previous analyses of the complementarity-determining regions (CDRs) of antibodies have focused on a small number of "canonical" conformations for each loop. This is primarily the result of the work of Chothia and coworkers, most recently in 1997. Because of the widespread utility of antibodies, we have revisited the clustering of conformations of the six CDR loops with the much larger amount of structural information currently available. In this work, we were careful to use a high-quality data set by eliminating low-resolution structures and CDRs with high B-factors or high conformational energies. We used a distance function based on directional statistics and an effective clustering algorithm with affinity propagation. With this data set of over 300 nonredundant antibody structures, we were able to cover 28 CDR-length combinations (e.g., L1 length 11, or "L1-11" in our CDR-length nomenclature) for L1, L2, L3, H1, and H2. The Chothia analysis covered only 20 CDR-lengths. Only four of these had more than one conformational cluster, of which two could easily be distinguished by gene source (mouse/human; κ/λ) and one could easily be distinguished purely by the presence and the positions of Pro residues (L3-9). Thus, using the Chothia analysis does not require the complicated set of "structure-determining residues" that is often assumed. Of our 28 CDR-lengths, 15 have multiple conformational clusters, including 10 for which the Chothia analysis had only one canonical class. We have a total of 72 clusters for non-H3 CDRs; approximately 85% of the non-H3 sequences can be assigned to a conformational cluster based on gene source and/or sequence. We found that earlier predictions of "bulged" versus "nonbulged" conformations based on the presence or the absence of anchor residues Arg/Lys94 and Asp101 of H3 have not held up, since all four combinations lead to a majority of conformations that are bulged. Thus, the earlier analyses have been significantly enhanced by the increased data. We believe that the new classification will lead to improved methods for antibody structure prediction and design.

Antibodies as a model system for comparative model refinement

Predicting the conformations of loops is a critical aspect of protein comparative (homology) modeling. Despite considerable advances in developing loop prediction algorithms, refining loops in homology models remains challenging. In this work, we use antibodies as a model system to investigate strategies for more robustly predicting loop conformations when the protein model contains errors in the conformations of side chains and protein backbone surrounding the loop in question. Specifically, our test system consists of partial models of antibodies in which the "scaffold" (i.e., the portion other than the complementarity determining region, CDR, loops) retains native backbone conformation, whereas the CDR loops are predicted using a combination of knowledge-based modeling (H1, H2, L1, L2, and L3) and ab initio loop prediction (H3). H3 is the most variable of the CDRs. Using a previously published method, a test set of 10 shorter H3 loops (5-7 residues) are predicted to an average backbone (N-C alpha-C-O) RMSD of 2.7 A while 11 longer loops (8-9 residues) are predicted to 5.1 A, thus recapitulating the difficulties in refining loops in models. By contrast, in control calculations predicting the same loops in crystal structures, the same method reconstructs the loops to an average of 0.5 and 1.4 A for the shorter and longer loops, respectively. We modify the loop prediction method to improve the ability to sample near-native loop conformations in the models, primarily by reducing the sensitivity of the sampling to the loop surroundings, and allowing the other CDR loops to optimize with the H3 loop. The new method improves the average accuracy significantly to 1.3 A RMSD and 3.1 A RMSD for the shorter and longer loops, respectively. Finally, we present results predicting 8-10 residue loops within complete comparative models of five nonantibody proteins. While anecdotal, these mixed, full-model results suggest our approach is a promising step toward more accurately predicting loops in homology models. Furthermore, while significant challenges remain, our method is a potentially useful tool for predicting antibody structures based on a known Fv scaffold.

Neutralizing antibodies of botulinum neurotoxin serotype A screened from a fully synthetic human antibody phage display library

The botulinum neurotoxins (BoNTs) produced by Clostridium botulinum are the most poisonous protein substances known. The neutralizing antibodies against botulinum neurotoxin can effectively prevent and cure the toxicosis. Using purified Hc fragments of botulinum neurotoxin serotype A (BoNT/A-Hc) as antigen, 2 specific neutralizing antibodies mapping different epitopes were selected from a fully synthetic human antibody library. The 2 antibodies can effectively inhibit the binding between BoNT/A-Hc and differentiated PC-12 cells in vitro, and the neutralization was evaluated in vivo. Although no single mAb completely protected mice from toxin, they both could prolong time to death when challenged with 20 LD(50)s (50% lethal doses) of BoNT/A. When used together, the mAbs completely neutralized 1000 LD(50)s/mg Ab, suggesting their high neutralizing potency in vivo. The results would lead to further production of neutralizing antibody drugs against BoNT/A. It also proved that it was a quick method to obtain human therapeutic antibodies by selecting from the fully synthetic human antibody phage display library.

Structural classification of CDR-H3 revisited: a lesson in antibody modeling

Among the six complementarity-determining regions (CDRs) in the variable domains of an antibody, the third CDR of the heavy chain (CDR-H3), which lies in the center of the antigen-binding site, plays a particularly important role in antigen recognition. CDR-H3 shows significant variability in its length, sequence, and structure. Although difficult, model building of this segment is the most critical step in antibody modeling. Since our first proposal of the "H3-rules," which classify CDR-H3 structure based on amino acid sequence, the number of experimentally determined antibody structures has increased. Here, we revise these H3-rules and propose an improved classification scheme for CDR-H3 structure modeling. In addition, we determine the common features of CDR-H3 in antibody drugs as well as discuss the concept of "antibody druggability," which can be applied as an indicator of antibody evaluation during drug discovery.

The anti-non-gal xenoantibody response to xenoantigens on gal knockout pig cells is encoded by a restricted number of germline progenitors

Antibodies directed at non-gal xenoantigens are responsible for acute humoral xenograft rejection when gal knockout (GalTKO) pig organs are transplanted into nonhuman primates. We generated IgM and IgG gene libraries using peripheral blood lymphocytes of rhesus monkeys initiating active xenoantibody responses after immunization with GalTKO pig endothelial cells and used these libraries to identify IgV(H) genes that encode antibody responses to non-gal pig xenoantigens. Immunoglobulin genes derived from the IGHV3-21 germline progenitor encode xenoantibodies directed at non-gal xenoantigens. Transduction of GalTKO cells with lentiviral vectors expressing the porcine alpha1,3 galactosyltransferase gene responsible for gal carbohydrate expression results in a higher level of binding of 'anti-non-gal' xenoantibodies to transduced GalTKO cells expressing the gal carbohydrate, suggesting that anti-non-gal xenoantibodies cross react with carbohydrate xenoantigens. The galactosyltransferase two gene encoding isoglobotriaosylceramide synthase (iGb3 synthase) is not expressed in GalTKO pig cells. Our results demonstrate that anti-non-gal xenoantibodies in primates are encoded by IgV(H) genes that are restricted to IGHV3-21 and bind to an epitope that is structurally related to but distinct from the Gal carbohydrate.

Dissecting NGF interactions with TrkA and p75 receptors by structural and functional studies of an anti-NGF neutralizing antibody

The anti-nerve growth factor (NGF) monoclonal antibody alphaD11 is a potent antagonist that neutralizes the biological functions of its antigen in vivo. NGF antagonism is expected to be a highly effective and safe therapeutic approach in many pain states. A comprehensive functional and structural analysis of alphaD11 monoclonal antibody was carried out, showing its ability to neutralize NGF binding to either tropomyosine receptor kinase A (TrkA) or p75 receptors. The 3-D structure of the alphaD11 Fab fragment was solved at 1.7 A resolution. A computational docking model of the alphaD11 Fab-NGF complex, based on epitope mapping using a pool of 44 NGF mutants and experimentally validated by small-angle X-ray scattering, provided the structural basis for identifying the residues involved in alphaD11 Fab binding. The present study pinpoints loop II of NGF to be an important structural determinant for NGF biological activity mediated by TrkA receptor.

Including Functional Annotations and Extending the Collection of Structural Classifications of Protein Loops (ArchDB)

Loops represent an important part of protein structures. The study of loop is critical for two main reasons: First, loops are often involved in protein function, stability and folding. Second, despite improvements in experimental and computational structure prediction methods, modeling the conformation of loops remains problematic. Here, we present a structural classification of loops, ArchDB, a mine of information with application in both mentioned fields: loop structure prediction and function prediction. ArchDB ( http://sbi.imim.es/archdb ) is a database of classified protein loop motifs. The current database provides four different classification sets tailored for different purposes. ArchDB-40, a loop classification derived from SCOP40, well suited for modeling common loop motifs. Since features relevant to loop structure or function can be more easily determined on well-populated clusters, we have developed ArchDB-95, a loop classification derived from SCOP95. This new classification set shows a ~40% increase in the number of subclasses, and a large 7-fold increase in the number of putative structure/function-related subclasses. We also present ArchDB-EC, a classification of loop motifs from enzymes, and ArchDB-KI, a manually annotated classification of loop motifs from kinases. Information about ligand contacts and PDB sites has been included in all classification sets. Improvements in our classification scheme are described, as well as several new database features, such as the ability to query by conserved annotations, sequence similarity, or uploading 3D coordinates of a protein. The lengths of classified loops range between 0 and 36 residues long. ArchDB offers an exhaustive sampling of loop structures. Functional information about loops and links with related biological databases are also provided. All this information and the possibility to browse/query the database through a web-server outline an useful tool with application in the comparative study of loops, the analysis of loops involved in protein function and to obtain templates for loop modeling.

Modeling the structure of mAb 14B7 bound to the anthrax protective antigen

The anthrax protective antigen (PA) is a key component of the tripartite anthrax toxin. Monoclonal antibody (mAb) 14B7 and its engineered, affinity-matured variants have been shown to be effective in blocking PA binding to cellular receptors and mitigating anthrax toxicity. Here, we perform computational structural modeling of the mAb 14B7-PA interaction. Our objectives are to determine the structure of the 14B7-PA complex, to deduce a structural explanation for the affinity maturation from the docking models, and to study the effect of inaccuracies in the antibody homology model on docking. We used the RosettaDock program to dock PA with the mAb 14B7 crystal structure or homology model. Our simulations generate two distinct binding orientations consistent with experimental residue mutations that diminish 14B7-PA binding. Furthermore, the models suggest new site-directed mutations to positively identify one of these two solutions as the correct 14B7-PA docking orientation. The models indicate that PA regions 648-660 and 712-720 may be important for 14B7 binding in addition to the known PA epitope, and the binding interfaces are similar to that seen in the PA complex with cellular receptor CMG2. Antibody residues involved in affinity maturation do not contact the antigen in the docking models, suggesting that affinity maturation in the 14B7 family does not result from direct enhancements of antibody-antigen contacts. Docking the homology model produces low-resolution representations of the crystal structure docking orientations, but homology model docking is frustrated by antibody H3 loop conformation errors. This work demonstrates the usefulness and limitations of computational structure prediction for the development of antibody therapeutics, and reemphasizes the need for flexible backbone docking algorithms to achieve high-resolution docking using homology models.

Use of molecular modeling and site-directed mutagenesis to define the structural basis for the immune response to carbohydrate xenoantigens

BACKGROUND

Natural antibodies directed at carbohydrates reject porcine xenografts. They are initially expressed in germline configuration and are encoded by a small number of structurally-related germline progenitors. The transplantation of genetically-modified pig organs prevents hyperacute rejection, but delayed graft rejection still occurs, partly due to humoral responses. IgVH genes encoding induced xenoantibodies are predominantly, not exclusively, derived from germline progenitors in the VH3 family. We have previously identified the immunoglobulin heavy chain genes encoding VH3 xenoantibodies in patients and primates. In this manuscript, we complete the structural analysis of induced xenoantibodies by identifying the IgVH genes encoding the small proportion of VH4 xenoantibodies and the germline progenitors encoding xenoantibody light chains. This information has been used to define the xenoantibody/carbohydrate binding site using computer-simulated modeling.

RESULTS

The VH4-59 gene encodes antibodies in the VH4 family that are induced in human patients mounting active xenoantibody responses. The light chain of xenoantibodies is encoded by DPK5 and HSIGKV134. The structural information obtained by sequencing analysis was used to create computer-simulated models. Key contact sites for xenoantibody/carbohydrate interaction for VH3 family xenoantibodies include amino acids in sites 31, 33, 50, 57, 58 and the CDR3 region of the IgVH gene. Site-directed mutagenesis indicates that mutations in predicted contact sites alter binding to carbohydrate xenoantigens. Computer-simulated modeling suggests that the CDR3 region directly influences binding.

CONCLUSION

Xenoantibodies induced during early and delayed xenograft responses are predominantly encoded by genes in the VH3 family, with a small proportion encoded by VH4 germline progenitors. This restricted group can be identified by the unique canonical structure of the light chain, heavy chain and CDR3. Computer-simulated models depict this structure with accuracy, as confirmed by site-directed mutagenesis. Computer-simulated drug design using computer-simulated models may now be applied to develop new drugs that may enhance the survival of xenografted organs.

Antibody structure, instability, and formulation

The number of therapeutic monoclonal antibody in development has increased tremendously over the last several years and this trend continues. At present there are more than 23 approved antibodies on the US market and an estimated 200 or more are in development. Although antibodies share certain structural similarities, development of commercially viable antibody pharmaceuticals has not been straightforward because of their unique and somewhat unpredictable solution behavior. This article reviews the structure and function of antibodies and the mechanisms of physical and chemical instabilities. Various aspects of formulation development have been examined to identify the critical attributes for the stabilization of antibodies.

Saturating representation of loop conformational fragments in structure databanks

Background

Short fragments of proteins are fundamental starting points in various structure prediction applications, such as in fragment based loop modeling methods but also in various full structure build-up procedures. The applicability and performance of these approaches depend on the availability of short fragments in structure databanks.

Results

We studied the representation of protein loop fragments up to 14 residues in length. All possible query fragments found in sequence databases (Sequence Space) were clustered and cross referenced with available structural fragments in Protein Data Bank (Structure Space). We found that the expansion of PDB in the last few years resulted in a dense coverage of loop conformational fragments. For each loops of length 8 in the current Sequence Space there is at least one loop in Structure Space with 50% or higher sequence identity. By correlating sequence and structure clusters of loops we found that a 50% sequence identity generally guarantees structural similarity. These percentages of coverage at 50% sequence cutoff drop to 96, 94, 68, 53, 33 and 13% for loops of length 9, 10, 11, 12, 13, and 14, respectively. There is not a single loop in the current Sequence Space at any length up to 14 residues that is not matched with a conformational segment that shares at least 20% sequence identity. This minimum observed identity is 40% for loops of 12 residues or shorter and is as high as 50% for 10 residue or shorter loops. We also assessed the impact of rapidly growing sequence databanks on the estimated number of new loop conformations and found that while the number of sequentially unique sequence segments increased about six folds during the last five years there are almost no unique conformational segments among these up to 12 residues long fragments.

Conclusion

The results suggest that fragment based prediction approaches are not limited any more by the completeness of fragments in databanks but rather by the effective scoring and search algorithms to locate them. The current favorable coverage and trends observed will be further accentuated with the progress of Protein Structure Initiative that targets new protein folds and ultimately aims at providing an exhaustive coverage of the structure space.

A supersecondary structure library and search algorithm for modeling loops in protein structures

We present a fragment-search based method for predicting loop conformations in protein models. A hierarchical and multidimensional database has been set up that currently classifies 105 950 loop fragments and loop flanking secondary structures. Besides the length of the loops and types of bracing secondary structures the database is organized along four internal coordinates, a distance and three types of angles characterizing the geometry of stem regions. Candidate fragments are selected from this library by matching the length, the types of bracing secondary structures of the query and satisfying the geometrical restraints of the stems and subsequently inserted in the query protein framework where their fit is assessed by the root mean square deviation (r.m.s.d.) of stem regions and by the number of rigid body clashes with the environment. In the final step remaining candidate loops are ranked by a Z-score that combines information on sequence similarity and fit of predicted and observed ϕ/ψ main chain dihedral angle propensities. Confidence Z-score cut-offs were determined for each loop length that identify those predicted fragments that outperform a competitive ab initio method. A web server implements the method, regularly updates the fragment library and performs prediction. Predicted segments are returned, or optionally, these can be completed with side chain reconstruction and subsequently annealed in the environment of the query protein by conjugate gradient minimization. The prediction method was tested on artificially prepared search datasets where all trivial sequence similarities on the SCOP superfamily level were removed. Under these conditions it is possible to predict loops of length 4, 8 and 12 with coverage of 98, 78 and 28% with at least of 0.22, 1.38 and 2.47 Å of r.m.s.d. accuracy, respectively. In a head-to-head comparison on loops extracted from freshly deposited new protein folds the current method outperformed in a ∼5:1 ratio an earlier developed database search method.

Large-scale conformational dynamics of the HIV-1 integrase core domain and its catalytic loop mutants

HIV-1 integrase is one of the three essential enzymes required for viral replication and has great potential as a novel target for anti-HIV drugs. Although tremendous efforts have been devoted to understanding this protein, the conformation of the catalytic core domain around the active site, particularly the catalytic loop overhanging the active site, is still not well characterized by experimental methods due to its high degree of flexibility. Recent studies have suggested that this conformational dynamics is directly correlated with enzymatic activity, but the details of this dynamics is not known. In this study, we conducted a series of extended-time molecular dynamics simulations and locally enhanced sampling simulations of the wild-type and three loop hinge mutants to investigate the conformational dynamics of the core domain. A combined total of >480 ns of simulation data was collected which allowed us to study the conformational changes that were not possible to observe in the previously reported short-time molecular dynamics simulations. Among the main findings are a major conformational change (>20 A) in the catalytic loop, which revealed a gatinglike dynamics, and a transient intraloop structure, which provided a rationale for the mutational effects of several residues on the loop including Q(148), P(145), and Y(143). Further, clustering analyses have identified seven major conformational states of the wild-type catalytic loop. Their implications for catalytic function and ligand interaction are discussed. The findings reported here provide a detailed view of the active site conformational dynamics and should be useful for structure-based inhibitor design for integrase.

Humanization and epitope mapping of the H23 anti-MUC1 monoclonal antibody reveals a dual epitope specificity

The tumor-associated antigen MUC1 is a cell surface mucin that is expressed on the apical surface of most glandular epithelial cells, including the ducts of the breast, ovary, pancrease, lung and colon. During malignancy, epithelial tissues regularly display elevated levels of MUC1 in a non-polar fashion and in an underglycosylated form, exposing cryptic peptide and carbohydrate epitopes. As such, MUC1 is regarded a potential target for immunotherapeutical intervention. Murine monoclonal H23 antibody specifically recognizes a MUC1 epitope on the surface of human breast cancer cells. We describe the cloning of the variable domains of H23 and their expression in (Escherichia coli) E. coli as maltose-binding protein-scFv (MBP-scFv) fusions. We humanized H23 and evaluated the binding properties of the murine and the humanized recombinant forms, which were similar in affinity and specificity, but lower in apparent affinity in comparison to the original monoclonal IgG. We mapped the epitope of humanized H23 by affinity-selecting a phage-displayed random peptide library on humanized H23 scFv-displaying bacteria. Our results show that humanized H23 binds an epitope corresponding to the MUC1 tandem repeat and an additional epitope not related to MUC1. These epitopes are competitive, bound with similar affinities and are recognized by the original murine H23 monoclonal antibody as well.

Classification of common functional loops of kinase super-families

A structural classification of loops has been obtained from a set of 141 protein structures classified as kinases. A total of 1813 loops was classified into 133 subclasses (9 betabeta(links), 15 betabeta(hairpins), 31 alpha-alpha, 46 alpha-beta and 32 beta-alpha). Functional information and specific features relating subclasses and function were included in the classification. Functional loops such as the P-loop (shared by different folds) or the Gly-rich-loop, among others, were classified into structural motifs. As a result, a common mechanism of catalysis and substrate binding was proved for most kinases. Additionally, the multiple-alignment of loop sequences made within each subclass was shown to be useful for comparative modeling of kinase loops. The classification is summarized in a kinase loop database located at http://sbi.imim.es/archki.

VH gene usage in immunoglobulin E responses of seasonal rhinitis patients allergic to grass pollen is oligoclonal and antigen driven

BACKGROUND

IgE is the pivotal-specific effector molecule of allergic reactions yet it remains unclear whether the elevated production of IgE in atopic individuals is due to superantigen activation of B cell populations, increased antibody class switching to IgE or oligoclonal allergen-driven IgE responses.

OBJECTIVES

To increase our understanding of the mechanisms driving IgE responses in allergic disease we examined immunoglobulin variable regions of IgE heavy chain transcripts from three patients with seasonal rhinitis due to grass pollen allergy.

METHODS

Variable domain of heavy chain-epsilon constant domain 1 cDNAs were amplified from peripheral blood using a two-step semi-nested PCR, cloned and sequenced.

RESULTS

The VH gene family usage in subject A was broadly based, but there were two clusters of sequences using genes VH 3-9 and 3-11 with unusually low levels of somatic mutations, 0-3%. Subject B repeatedly used VH 1-69 and subject C repeatedly used VH 1-02, 1-46 and 5a genes. Most clones were highly mutated being only 86-95% homologous to their germline VH gene counterparts and somatic mutations were more abundant at the complementarity determining rather than framework regions. Multiple sequence alignment revealed both repeated use of particular VH genes as well as clonal relatedness among clusters of IgE transcripts.

CONCLUSION

In contrast to previous studies we observed no preferred VH gene common to IgE transcripts of the three subjects allergic to grass pollen. Moreover, most of the VH gene characteristics of the IgE transcripts were consistent with oligoclonal antigen-driven IgE responses.

ArchDB: automated protein loop classification as a tool for structural genomics

The annotation of protein function has become a crucial problem with the advent of sequence and structural genomics initiatives. A large body of evidence suggests that protein structural information is frequently encoded in local sequences, and that folds are mainly made up of a number of simple local units of super-secondary structural motifs, consisting of a few secondary structures and their connecting loops. Moreover, protein loops play an important role in protein function. Here we present ArchDB, a classification database of structural motifs, consisting of one loop plus its bracing secondary structures. ArchDB currently contains 12,665 super-secondary elements classified into 1496 motif subclasses. The database provides an easy way to retrieve functional information from protein structures sharing a common motif, to search motifs found in a given SCOP family, superfamily or fold, or to search by keywords on proteins with classified loops. The ArchDB database of loops is located at http://sbi.imim.es/archdb.

Expressed murine and human CDR-H3 intervals of equal length exhibit distinct repertoires that differ in their amino acid composition and predicted range of structures

Immunoglobulin junctional diversity is concentrated in the third complementarity-determining region of the heavy chain (CDR-H3), which often plays a dominant role in antigen binding. The range of CDR-H3 lengths in mouse is shorter than in human, and thus the murine repertoire could be presumed to be a subset of the human one. To test this presumption, we analyzed 4751 human and 2170 murine unique, functional, published CDR-H3 intervals. Although tyrosine, glycine, and serine were found to predominate in both species, the human sequences contained fewer tyrosine residues, more proline residues, and more hydrophobic residues (p<0.001, respectively). While changes in amino acid utilization as a function of CDR-H3 length followed similar trends in both species, murine and human CDR-H3 intervals of identical length were found to differ from each other. These differences reflect both divergence of germline diversity and joining gene sequence and somatic selection. Together, these factors promote the production of a rather uniform repertoire in mice of tyrosine-enriched CDR-H3 loops with stabilized hydrogen bond-ladders versus a much more diverse repertoire in human that contains CDR-H3 loops sculpted by the presence of intra-chain disulfide bonds due to germline-encoded cysteine residues as well as the enhanced presence of somatically generated proline residues that preclude hydrogen bond ladder formation. Thus, despite the presumed need to recognize a similar range of antigen epitopes, the murine CDR-H3 repertoire is clearly distinct from its human counterpart in its amino acid composition and its predicted range of structures. These findings represent a benchmark to which CDR-H3 repertoires can be compared to better characterize and understand the shaping of the CDR-H3 repertoire over evolution and during immune responses. This information may also be useful for the design of species-specific CDR-H3 sequences in synthetic antibody libraries.

Hybrid Monte Carlo with multidimensional replica exchanges: conformational equilibria of the hypervariable regions of a llama VHH antibody domain

Since the structural repertoire of the hypervariable regions of human antibodies is known to be more restricted than what is implied by sequence variability, a common approach to structural prediction is to use a knowledge-based (KB) method, such as the canonical structure model (C. Chothia and A. M. Lesk, Journal of Molecular Biology, 1987, Vol. 196, pp. 901-917). However, this model is less successful when applied to camelid heavy chain antibodies. In this study, molecular simulations were used to examine the conformational equilibria of the hypervariable regions (H1, H2, and H3) of a llama heavy chain variable domain, for which KB predictions are poor. Simulations were carried out using both conventional molecular dynamics (MD) and hybrid Monte Carlo with multidimensional replica exchanges (HYMREX). The advantage of the latter method is its ability to selectively target parts of the Hamiltonian that can most readily improve sampling. A novel variant of HYMREX was implemented in which, besides the temperature, torsional interactions and the range of nonbonded interactions were varied. To compare the sampling abilities of MD and this HYMREX scheme, simulations were started from a misfolded conformational state. Overall, MD yielded final conformations more similar to the initial state, implying quasi-ergodic sampling. In contrast, HYMREX achieved more ergodic sampling, and the majority of conformations that it sampled agreed well with the known crystal structure. The HYMREX simulation results were used to help identify the chief interactions governing the conformational equilibria and to reexamine the key assumptions underlying the KB predictions. The data show that the H1 region exhibited significant conformational freedom, in support of the hypothesis that main-chain structural variability in this region could play a greater role in antigen binding in camelid antibodies than it does in normal antibodies. Key H1 residues and associated inter-loop interactions are conjectured to account for the poor KB predictions.

New variation on a theme: structure and mechanism of action of hydrolytic antibody 7F11, an aspartate rich relation of catalytic antibodies 17E8 and 29G11

A computer model, based on homology, of the catalytic antibody 7F11 that catalyses the decomposition of the benzoate ester of a dioxetane resulting in chemiluminescence is reported. Antibody 7F11 has 89% identity in the V(L) domain, and 72% identity in the V(H) domain with hydrolytic antibodies 17E8 and 29G11 previously reported by Scanlan et al. These were also raised against a phosphonate containing hapten. The antigen-binding site of antibody 7F11 whilst similar to that of 17E8 has aspartic acids at positions 33H and 35H, reminiscent in position of the catalytic residues found in aspartate proteinases such as pepsin. AutoDock 3.0 has been used to identify the best binding mode for the hapten. Molecular dynamic simulations have also been undertaken to examine any major conformational changes induced by hapten binding. A mechanism for benzoate ester hydrolysis involving the aspartic acid side-chains is proposed. Construction of a single-chain variable fragment (scFv) of 7F11 is also reported.

Antiferritin VL homodimer binds human spleen ferritin with high specificity

The antiferritin variable light domain (VL) dimer binds human spleen ferritin ( approximately 85% L subunits) but with approximately 50-fold lower affinity, K(a)=4 x 10(7) x M(-1), than the parent F11 antibody (K(a)=2.1 x 10(9) x M(-1)). The VL dimer does not recognize either rL (100% L subunits) or rH (100% H subunits) human ferritin, whereas the parent antibody recognizes rL-ferritin. To help explain the differences in ferritin binding affinities and specificities, the crystal structure of the VL domain (2.8A resolution) was determined by molecular replacement and models of the antiferritin VL-VH dimer were made on the basis of antilysozyme antibody D1.3. The domain interface is smaller in the VL dimer but a larger number of interdomain hydrogen bonds may prevent rearrangement on antigen binding. The antigen binding surface of the VL dimer is flatter, lacking a negatively charged pocket found in the VL-VH models, contributed by the CDR3 loop of the VH domain. Loop CDR2 (VL dimer) is located away from the antigen binding site, while the corresponding loop of the VH domain would be located within the antigen binding site. Together these differences lead to 50-fold lower binding affinity in the VL dimer and to more restricted specificity than is seen for the parent antibody.

Epitope recognition by diverse antibodies suggests conformational convergence in an antibody response

Crystal structures of distinct mAbs that recognize a common epitope of a peptide Ag have been determined and analyzed in the unbound and bound forms. These Abs display dissimilar binding site structures in the absence of the Ag. The dissimilarity is primarily expressed in the conformations of complementarity-determining region H3, which is responsible for defining the epitope specificity. Interestingly, however, the three Abs exhibit similar complementarity-determining region conformations in the Ag binding site while recognizing the common epitope, indicating that different pathways of binding are used for Ag recognition. The epitope also exhibits conformational similarity when bound to each of these Abs, although the peptide Ag was otherwise flexible. The observed conformational convergence in the epitope and the Ag binding site was facilitated by the plasticity in the nature of interactions.

Structural insights into steroid hormone binding: the crystal structure of a recombinant anti-testosterone Fab fragment in free and testosterone-bound forms

The monoclonal anti-testosterone antibody (3-C(4)F(5)) has a relatively high affinity (3 x 10(8) m(-1)) with an overall good specificity profile. However, the earlier characterized binding properties have shown that both the affinity and specificity of this antibody must be improved if it is intended for use in clinical immunoassays. In this paper, the crystal structures of the recombinant anti-testosterone (3-C(4)F(5)) Fab fragment have been determined in the testosterone-bound and free form at resolutions of 2.60 and 2.72 A, respectively. The high affinity binding of the (3-C(4)F(5)) Fab is mainly determined by shape complementarity between the protein and testosterone. Only one direct hydrogen bond is formed between the hydroxyl group of the testosterone D-ring and the main-chain oxygen of Gly100(J)H. The testosterone is deeply bound in a hydrophobic pocket, and the close shape complementarity is mainly formed by the third complementarity-determining regions (CDR) of the heavy and light chain. Comparison of the bound structure with the free structure indicates conformational changes in the protein upon testosterone binding. The conformational changes of the side chains of two residues Glu95H and Tyr99H in the CDR-H3 are particularly essential for the binding. Interesting similarities in the binding of different steroids were also observed upon comparison of the available structures of anti-steroid antibodies.

Crystal structure of a recombinant anti-estradiol Fab fragment in complex with 17beta -estradiol

The crystal structure of a Fab fragment of an anti-17beta-estradiol antibody 57-2 was determined in the absence and presence of the steroid ligand, 17beta-estradiol (E2), at 2.5 and 2.15-A resolutions, respectively. The antibody binds the steroid in a deep hydrophobic pocket formed at the interface between the variable domains. No major structural rearrangements take place upon ligand binding; however, a large part of the heavy chain variable domain near the binding pocket is unusually flexible and is partly stabilized when the steroid is bound. The nonpolar steroid skeleton of E2 is recognized by a number of hydrophobic interactions, whereas the two hydroxyl groups of E2 are hydrogen-bonded to the protein. Especially, the 17-hydroxyl group of E2 is recognized by an intricate hydrogen bonding network in which the 17-hydroxyl itself forms a rare four-center hydrogen bond with three polar amino acids; this hydrogen bonding arrangement accounts for the low cross-reactivity of the antibody with other estrogens such as estrone. The CDRH3 loop plays a prominent role in ligand binding. All the complementarity-determining regions of the light chain make direct contacts with the steroid, even CDRL2, which is rarely directly involved in the binding of haptens.