Shapes of antibody binding sites: qualitative and quantitative analyses based on a geomorphic classification scheme

T-cell virtuosity in ''knowing thyself"

Major Histocompatibility Complex (MHC) I and II and the αβ T-cell antigen receptor (TCRαβ) govern fundamental traits of adaptive immunity. They form a membrane-borne ligand-receptor system weighing host proteome integrity to detect contamination by nonself proteins. MHC-I and -II exhibit the "MHC-fold", which is able to bind a large assortment of short peptides as proxies for self and nonself proteins. The ensuing varying surfaces are mandatory ligands for Ig-like TCRαβ highly mutable binding sites. Conserved molecular signatures guide TCRαβ ligand binding sites to focus on the MHC-fold (MHC-restriction) while leaving many opportunities for its most hypervariable determinants to contact the peptide. This riveting molecular strategy affords many options for binding energy compatible with specific recognition and signalling aimed to eradicated microbial pathogens and cancer cells. While the molecular foundations of αβ T-cell adaptive immunity are largely understood, uncertainty persists on how peptide-MHC binding induces the TCRαβ signals that instruct cell-fate decisions. Solving this mystery is another milestone for understanding αβ T-cells' self/nonself discrimination. Recent developments revealing the innermost links between TCRαβ structural dynamics and signalling modality should help dissipate this long-sought-after enigma.

Understanding the contagiousness of Covid-19 strains: A geometric approach

Protein-protein interaction occurs on surface patches with some degree of complementary geometric and chemical features. Building on this understanding, this study endeavors to characterize the spike protein of the SARS-CoV-2 virus at the morphological and geometrical levels in its Alpha, Delta, and Omicron variants. In particular, the affinity between different SARS-CoV-2 spike proteins and the ACE2 receptor present on the membrane of the human respiratory system cells is investigated. To achieve an adequate degree of geometrical accuracy, the 3D depth maps of the proteins in exam are filtered by developing an ad-hoc convolutional filter with a kernel implemented as a sphere of varying radius, simulating a ball rolling on the surface (similar to the 'rolling ball' filter). This ball ideally models a hypothetical molecule that could interface with the protein and is inspired by the geometric approach to macromolecule-ligand interactions proposed by Kuntz et al. in 1982. The aim is to mitigate the imperfections and to obtain a smoother surface that could be studied from a geometrical perspective for binding purposes. A set of geometric descriptors, borrowed from the 3D face analysis context is then mapped point-by-point onto protein depth maps. Following a feature extraction phase inspired by Histogram of Oriented Gradients and Local Binary Patterns, the final histogram features are used as input for a Support Vector Machine classifier to automatically classify the proteins according to their surface affinity, where a similarity in shape is observed between ACE2 and the spike protein of the SARS-CoV-2 Omicron variant. Finally, Root Mean Square Error analysis is used to quantify the geometrical affinity between the ACE2 receptor and the respective Receptor Binding Domains of the three SARS-CoV-2 variants, culminating in a geometrical explanation for the higher contagiousness of Omicron relative to the other variants under study.

Quantitative Description of Surface Complementarity of Antibody-Antigen Interfaces

Antibodies have the remarkable ability to recognise their cognate antigens with extraordinary affinity and specificity. Discerning the rules that define antibody-antigen recognition is a fundamental step in the rational design and engineering of functional antibodies with desired properties. In this study we apply the 3D Zernike formalism to the analysis of the surface properties of the antibody complementary determining regions (CDRs). Our results show that shape and electrostatic 3DZD descriptors of the surface of the CDRs are predictive of antigen specificity, with classification accuracy of 81% and area under the receiver operating characteristic curve (AUC) of 0.85. Additionally, while in terms of surface size, solvent accessibility and amino acid composition, antibody epitopes are typically not distinguishable from non-epitope, solvent-exposed regions of the antigen, the 3DZD descriptors detect significantly higher surface complementarity to the paratope, and are able to predict correct paratope-epitope interaction with an AUC = 0.75.

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

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

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

Toward defining the immunogenicity of HLA epitopes: Impact of HLA class I eplets on antibody formation during pregnancy

Eplets are functional units of structural epitopes on donor HLA, potentially recognized by complementarity-determining regions of the paratope of the recipients' B-cell receptors or antibodies (Ab). Their individual immunogenicity is poorly described, yet this feature would be of clinical importance for pretransplant risk assessment. The aim of this study was to determine the relative immunogenicity of HLA class I eplets in the pregnancy setting, where mismatched eplets are present on paternal HLA antigens of the unborn child. One hundred fifty-nine predominantly Caucasian mothers giving birth at the University Hospital Basel and their first newborns were HLA-typed at high-resolution by next-generation sequencing (NGS) (NGSgo Workflow and NGSengine from GenDx; sequencing with a Miseq from Illumina) and eplets were determined using HLAMatchmaker. HLA class I specific IgG Ab was assessed in maternal sera drawn immediately after full-term delivery, by OneLambda LABScreen single antigen ibeads. The Ab profile was subsequently evaluated for eplet-associated patterns. All 72 currently Ab-verified HLA class I eplets were examined for their immunogenicity according to the frequency of child-specific HLA Ab (CSA) directed against their structures. Four hundred twelve of 477 (86.4%) paternal HLA-A, -B or -C alleles were mismatched. CSA were present in 46 mothers (28.9%), directed against 80 (19.4%) of these mismatches. The 10 most immunogenic eplets were 62GK, 145KHA, 144TKH, 62GE, 107W, 80I, 82LR, 41T, 127K, 45KE with immunogenicity rates between 45.8% and 27.3%. This pregnancy study also identified five non-reactive eplets: 62RR, 76ESN, 80TLR, 156DA, 163RW. Based on our results, immunogenic hot and cold spots on the surface of HLA class I molecules were localized and visualized on 3D models. This study strengthens the presumption that different eplets represent different immunogenic potentials. Validation of these results in the clinical transplant setting is an essential next step in identifying those eplets representing a particularly high-risk potential.

Predicting protein-ligand binding affinity and correcting crystal structures with quantum mechanical calculations: lactate dehydrogenase A

Accurately computing the geometry and energy of host-guest and protein-ligand interactions requires a physically accurate description of the forces in action. Quantum mechanics can provide this accuracy but the calculations can require a prohibitive quantity of computational resources. The size of the calculations can be reduced by including only the atoms of the receptor that are in close proximity to the ligand. We show that when combined with log P values for the ligand (which can be computed easily) this approach can significantly improve the agreement between computed and measured binding energies. When the approach is applied to lactate dehydrogenase A, it can make quantitative predictions about conformational, tautomeric and protonation state preferences as well as stereoselectivity and even identifies potential errors in structures deposited in the Protein Data Bank for this enzyme. By broadening the evidence base for these structures from only the diffraction data, more chemically realistic structures can be proposed.

Applying Pose Clustering and MD Simulations To Eliminate False Positives in Molecular Docking

In this work, we developed a computational protocol that employs multiple molecular docking experiments, followed by pose clustering, molecular dynamic simulations (10 ns), and energy rescoring to produce reliable 3D models of antibody-carbohydrate complexes. The protocol was applied to 10 antibody-carbohydrate co-complexes and three unliganded (apo) antibodies. Pose clustering significantly reduced the number of potential poses. For each system, 15 or fewer clusters out of 100 initial poses were generated and chosen for further analysis. Molecular dynamics (MD) simulations allowed the docked poses to either converge or disperse, and rescoring increased the likelihood that the best-ranked pose was an acceptable pose. This approach is amenable to automation and can be a valuable aid in determining the structure of antibody-carbohydrate complexes provided there is no major side chain rearrangement or backbone conformational change in the H3 loop of the CDR regions. Further, the basic protocol of docking a small ligand to a known binding site, clustering the results, and performing MD with a suitable force field is applicable to any protein ligand system.

Superposition-free comparison and clustering of antibody binding sites: implications for the prediction of the nature of their antigen

We describe here a superposition free method for comparing the surfaces of antibody binding sites based on the Zernike moments and show that they can be used to quickly compare and cluster sets of antibodies. The clusters provide information about the nature of the bound antigen that, when combined with a method for predicting the number of direct antibody antigen contacts, allows the discrimination between protein and non-protein binding antibodies with an accuracy of 76%. This is of relevance in several aspects of antibody science, for example to select the framework to be used for a combinatorial antibody library.

Facets on the convex hull of d-dimensional Brownian and Lévy motion

For stationary, homogeneous Markov processes (viz., Lévy processes, including Brownian motion) in dimension d≥3, we establish an exact formula for the average number of (d-1)-dimensional facets that can be defined by d points on the process's path. This formula defines a universality class in that it is independent of the increments' distribution, and it admits a closed form when d=3, a case which is of particular interest for applications in biophysics, chemistry, and polymer science. We also show that the asymptotical average number of facets behaves as 〈F_{T}^{(d)}〉∼2[ln(T/Δt)]^{d-1}, where T is the total duration of the motion and Δt is the minimum time lapse separating points that define a facet.

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

Antibody–antigen complexes challenge our understanding, as analyses to date failed to unveil the key determinants of binding affinity and interaction specificity. We partially fill this gap based on novel quantitative analyses using two standardized databases, the IMGT/3Dstructure-DB and the structure affinity benchmark. First, we introduce a statistical analysis of interfaces which enables the classification of ligand types (protein, peptide, and chemical; cross-validated classification error of 9.6%) and yield binding affinity predictions of unprecedented accuracy (median absolute error of 0.878 kcal/mol). Second, we exploit the contributions made by CDRs in terms of position at the interface and atomic packing properties to show that in general, VH CDR3 and VL CDR3 make dominant contributions to the binding affinity, a fact also shown to be consistent with the enthalpy–entropy compensation associated with preconfiguration of CDR3. Our work suggests that the affinity prediction problem could be partially solved from databases of high resolution crystal structures of complexes with known affinity.

Shape complementarity and hydrogen bond preferences in protein-protein interfaces: implications for antibody modeling and protein-protein docking

MOTIVATIONS

Characterizing protein-protein interfaces and the hydrogen bonds is a first step to better understand proteins' structures and functions toward high-resolution protein design. However, there are few large-scale surveys of hydrogen bonds of interfaces. In addition, previous work of shape complementarity of protein complexes suggested that lower shape complementarity in antibody-antigen interfaces is related to their evolutionary origin.

RESULTS

Using 6637 non-redundant protein-protein interfaces, we revealed peculiar features of various protein complex types. In contrast to previous findings, the shape complementarity of antibody-antigen interfaces resembles that of the other interface types. These results highlight the importance of hydrogen bonds during evolution of protein interfaces and rectify the prevailing belief that antibodies have lower shape complementarity.

CONTACT

jgray@jhu.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Deciphering Antigenic Determinants of Streptococcus pneumoniae Serotype 4 Capsular Polysaccharide using Synthetic Oligosaccharides

Streptococcus pneumoniae is a major cause of mortality and morbidity worldwide. More than 90 S. pneumoniae serotypes are distinguished based on the structure of their primary targets to the human immune system, the capsular polysaccharides (CPSs). The CPS of the prevalent serotype 4 (ST4) is composed of tetrasaccharide repeating units and is included in existing pneumococcal vaccines. Still, the structural antigenic determinants that are essential for protective immunity, including the role of the rare and labile cyclic trans-(2,3) pyruvate ketal modification, remain largely unknown. Molecular insights will support the design of synthetic subunit oligosaccharide vaccines. Here, we identified the key antigenic determinants of ST4 CPS with the help of pyruvated and nonpyruvated synthetic repeating unit glycans. Glycan arrays revealed oligosaccharide antigens recognized by antibodies in the human reference serum. Selected depyruvated ST4 oligosaccharides were used to formulate neoglycoconjugates and immunologically evaluated in mice. These oligosaccharides were highly immunogenic, but the resulting antiglycan antibodies showed only limited binding to the natural CPS present on the bacterial surface. Glycan array and surface plasmon resonance analysis of murine polyclonal serum antibodies as well as monoclonal antibodies revealed that terminal sugars are important in directing the immune responses. The pyruvate modification on the oligosaccharide is needed for cross-reactivity with the native CPS. These findings are an important step toward the design of oligosaccharide-based vaccines against S. pneumoniae ST4.

Blind prediction performance of RosettaAntibody 3.0: grafting, relaxation, kinematic loop modeling, and full CDR optimization

Antibody Modeling Assessment II (AMA-II) provided an opportunity to benchmark RosettaAntibody on a set of 11 unpublished antibody structures. RosettaAntibody produced accurate, physically realistic models, with all framework regions and 42 of the 55 non-H3 CDR loops predicted to under an Ångström. The performance is notable when modeling H3 on a homology framework, where RosettaAntibody produced the best model among all participants for four of the 11 targets, two of which were predicted with sub-Ångström accuracy. To improve RosettaAntibody, we pursued the causes of model errors. The most common limitation was template unavailability, underscoring the need for more antibody structures and/or better de novo loop methods. In some cases, better templates could have been found by considering residues outside of the CDRs. De novo CDR H3 modeling remains challenging at long loop lengths, but constraining the C-terminal end of H3 to a kinked conformation allows near-native conformations to be sampled more frequently. We also found that incorrect VL -VH orientations caused models with low H3 RMSDs to score poorly, suggesting that correct VL -VH orientations will improve discrimination between near-native and incorrect conformations. These observations will guide the future development of RosettaAntibody.

Delineating the functional map of the interaction between nimotuzumab and the epidermal growth factor receptor

Molecular details of epidermal growth factor receptor (EGFR) targeting by nimotuzumab, a therapeutic anti-cancer antibody, have been largely unknown. The current study delineated a functional map of their interface, based on phage display and extensive mutagenesis of both the target antigen and the Fv antibody fragment. Five residues in EGFR domain III (R353, S356, F357, T358, and H359T) and the third hypervariable region of nimotuzumab heavy chain were shown to be major functional contributors to the interaction. Fine specificity differences between nimotuzumab and other anti-EGFR antibodies were revealed. Mapping information guided the generation of a plausible in silico binding model. Knowledge about the epitope/paratope interface opens new avenues for the study of tumor sensitivity/resistance to nimotuzumab and for further engineering of its binding site. The developed mapping platform, also validated with the well-known cetuximab epitope, allows a comprehensive exploration of antigenic regions and could be expanded to map other anti-EGFR antibodies.

Importance of ligand conformational energies in carbohydrate docking: Sorting the wheat from the chaff

Docking algorithms that aim to be applicable to a broad range of ligands suffer reduced accuracy because they are unable to incorporate ligand-specific conformational energies. Here, we develop a set of Carbohydrate Intrinsic (CHI) energy functions that quantify the conformational properties of oligosaccharides, based on the values of their glycosidic torsion angles. The relative energies predicted by the CHI energy functions mirror the conformational distributions of glycosidic linkages determined from a survey of oligosaccharide-protein complexes in the protein data bank. Addition of CHI energies to the standard docking scores in Autodock 3, 4.2, and Vina consistently improves pose ranking of oligosaccharides docked to a set of anticarbohydrate antibodies. The CHI energy functions are also independent of docking algorithm, and with minor modifications, may be incorporated into both theoretical modeling methods, and experimental NMR or X-ray structure refinement programs.

Structural basis for antibody recognition in the receptor-binding domains of toxins A and B from Clostridium difficile

Clostridium difficile infection is a serious and highly prevalent nosocomial disease in which the two large, Rho-glucosylating toxins TcdA and TcdB are the main virulence factors. We report for the first time crystal structures revealing how neutralizing and non-neutralizing single-domain antibodies (sdAbs) recognize the receptor-binding domains (RBDs) of TcdA and TcdB. Surprisingly, the complexes formed by two neutralizing antibodies recognizing TcdA do not show direct interference with the previously identified carbohydrate-binding sites, suggesting that neutralization of toxin activity may be mediated by mechanisms distinct from steric blockage of receptor binding. A camelid sdAb complex also reveals the molecular structure of the TcdB RBD for the first time, facilitating the crystallization of a strongly negatively charged protein fragment that has resisted previous attempts at crystallization and structure determination. Electrospray ionization mass spectrometry measurements confirm the stoichiometries of sdAbs observed in the crystal structures. These studies indicate how key epitopes in the RBDs from TcdA and TcdB are recognized by sdAbs, providing molecular insights into toxin structure and function and providing for the first time a basis for the design of highly specific toxin-specific therapeutic and diagnostic agents.

Prediction of site-specific interactions in antibody-antigen complexes: the proABC method and server

Motivation: Antibodies or immunoglobulins are proteins of paramount importance in the immune system. They are extremely relevant as diagnostic, biotechnological and therapeutic tools. Their modular structure makes it easy to re-engineer them for specific purposes. Short of undergoing a trial and error process, these experiments, as well as others, need to rely on an understanding of the specific determinants of the antibody binding mode.

Results: In this article, we present a method to identify, on the basis of the antibody sequence alone, which residues of an antibody directly interact with its cognate antigen. The method, based on the random forest automatic learning techniques, reaches a recall and specificity as high as 80% and is implemented as a free and easy-to-use server, named prediction of Antibody Contacts. We believe that it can be of great help in re-design experiments as well as a guide for molecular docking experiments. The results that we obtained also allowed us to dissect which features of the antibody sequence contribute most to the involvement of specific residues in binding to the antigen.

Availability:http://www.biocomputing.it/proABC.

Contact:anna.tramontano@uniroma1.it or paolo.marcatili@gmail.com

Supplementary information:Supplementary data are available at Bioinformatics online.

Computational screening of the human TF-glycome provides a structural definition for the specificity of anti-tumor antibody JAA-F11

Recombinant antibodies are of profound clinical significance; yet, anti-carbohydrate antibodies are prone to undesirable cross-reactivity with structurally related-glycans. Here we introduce a new technology called Computational Carbohydrate Grafting (CCG), which enables a virtual library of glycans to be assessed for protein binding specificity, and employ it to define the scope and structural origin of the binding specificity of antibody JAA-F11 for glycans containing the Thomsen-Friedenreich (TF) human tumor antigen. A virtual library of the entire human glycome (GLibrary-3D) was constructed, from which 1,182 TF-containing human glycans were identified and assessed for their ability to fit into the antibody combining site. The glycans were categorized into putative binders, or non-binders, on the basis of steric clashes with the antibody surface. The analysis employed a structure of the immune complex, generated by docking the TF-disaccharide (Galβ1-3GalNAcα) into a crystal structure of the JAA-F11 antigen binding fragment, which was shown to be consistent with saturation transfer difference (STD) NMR data. The specificities predicted by CCG were fully consistent with data from experimental glycan array screening, and confirmed that the antibody is selective for the TF-antigen and certain extended core-2 type mucins. Additionally, the CCG analysis identified a limited number of related putative binding motifs, and provided a structural basis for interpreting the specificity. CCG can be utilized to facilitate clinical applications through the determination of the three-dimensional interaction of glycans with proteins, thus augmenting drug and vaccine development techniques that seek to optimize the specificity and affinity of neutralizing proteins, which target glycans associated with diseases including cancer and HIV.

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.

Structural analysis of a protective epitope of the Francisella tularensis O-polysaccharide

Francisella tularensis (Ft), the Gram-negative facultative intracellular bacterium that causes tularemia, is considered a biothreat because of its high infectivity and the high mortality rate of respiratory disease. The Ft lipopolysaccharide (Ft LPS) is thought to be a main protective antigen in mice and humans, and we have previously demonstrated the protective effect of the Ft LPS-specific monoclonal antibody Ab52 in a mouse model of respiratory tularemia. Immunochemical characterization has shown that the epitope recognized by Ab52 is contained within two internal repeat units of the O-polysaccharide [O-antigen (OAg)] of Ft LPS. To further localize the Ab52 epitope and understand the molecular interactions between the antibody and the saccharide, we determined the X-ray crystal structure of the Fab fragment of Ab52 and derived an antibody-antigen complex using molecular docking. The docked complex, refined through energy minimization, reveals an antigen binding site in the shape of a large canyon with a central pocket that accommodates a V-shaped epitope consisting of six sugar residues, α-D-GalpNAcAN(1→4)-α-D-GalpNAcAN(1→3)-β-D-QuipNAc(1→2)-β-D-Quip4NFm(1→4)-α-D-GalpNAcAN(1→4)-α-D-GalpNAcAN. These results inform the development of vaccines and immunotherapeutic/immunoprophylactic antibodies against Ft by suggesting a desired topology for binding of the antibody to internal epitopes of Ft LPS. This is the first report of an X-ray crystal structure of a monoclonal antibody that targets a protective Ft B cell epitope.

Peptide inhibitors of xenoreactive antibodies mimic the interaction profile of the native carbohydrate antigens

Carbohydrate-antibody interactions mediate many cellular processes and immune responses. Carbohydrates expressed on the surface of cells serve as recognition elements for particular cell types, for example, in the ABO(H) blood group system. Antibodies that recognize host-incompatible ABO(H) system antigens exist in the bloodstream of all individuals (except AB individuals), preventing blood transfusion and organ transplantation between incompatible donors and recipients. A similar barrier exists for cross-species transplantation (xenotransplantation), in particular for pig-to-human transplantation. All humans express antibodies against the major carbohydrate xenoantigen, Galalpha (1,3)Gal (alphaGal), preventing successful xenotransplantation. Although antibody binding sites are precisely organized so as to selectively bind a specific antigen, many antibodies recognize molecules other than their native antigen. A range of peptides have been identified that can mimic carbohydrates and inhibit anti-alphaGal antibodies. However, the structural basis of how the peptides achieved this was not known. Previously, we developed an in silico method which we used to investigate carbohydrate recognition by a panel of anti-alphaGal antibodies. The method involves molecular docking of carbohydrates to antibodies and uses the docked carbohydrate poses to generate maps of th antibody binding sites in terms of prevalent hydrogen bonding and van der Waals interactions. We have applied this method to investigate peptide recognition by the anti-alphaGal antibodies. It was found that the site maps of the peptides and the carbohydrates were similar, indicating that the peptides interact with the same residues as those involved in carbohydrate recognition. This study demonstrates the potential for "design by mapping" of anti-carbohydrate antibody inhibitors.

Forchlorfenuron-mimicking haptens: from immunogen design to antibody characterization by hierarchical clustering analysis

To obtain highly-specific and selective forchlorfenuron binders, a collection of functionalized derivatives with different spacer arm locations and lengths was prepared. By immunization with target-mimicking haptens, a large battery of monoclonal and polyclonal antibodies against this synthetic cell regulator was produced and exhaustively characterized in two immunoassay formats using homologous and heterologous conjugates. Antibodies with IC(50) values lower than 0.3 nM were successfully raised from the prepared immunogens, thus evidencing the efficacy of the explored strategies. In order to identify significant epitopes in the antibody-antigen interaction, a series of new chemical forchlorfenuron analogues, with slight modifications at both rings of the target molecule, were synthesized and evaluated in competitive assays. As a novel approach in hapten recognition studies, data processing was performed by computational classification methods based on hierarchical clustering. This strategy was shown to be highly valuable for a straightforward profiling of antibodies according to analogue recognition patterns. A relationship could be established between the antigen binding properties of antibodies and the structure of the immunogen. Whereas antibodies with equivalent affinities had been obtained from all of the derivatives, their specificity was found to be largely influenced by the differential exposition of the molecule to the immune system.

Synthesis of site-heterologous haptens for high-affinity anti-pyraclostrobin antibody generation

The design and synthesis of functional chemical derivatives of small organic molecules is usually a key step for the intricate production of a variety of bioconjugates. In this respect, the derivatization site at which the spacer arm is introduced in immunizing conjugates constitutes a highly critical parameter for the generation of high-affinity and selective antibodies. However, due to the usual complexity of the required synthetic procedures, the appropriate comparison of alternative tethering positions has often been neglected. In the present study, meticulous strategies were followed to prepare synthetic derivatives of pyraclostrobin with the same linkers located at diverse rationally-chosen sites. Activity appraisal of antibodies and bioconjugates was carried out by bidimensional competitive direct and indirect immunoassays, and a superior performance of two of the three synthesized haptens was found. Finally, a detailed analysis of the conformations of the target molecule and the synthesized haptens in aqueous solution was done using computer assisted molecular modeling techniques. This study suggested that the lower titers and affinities of one set of antibodies are most probably due to conformational effects of the spacer arm in the immunizing bioconjugate.

Interaction of antibodies with aromatic ligands: the role of pi-stacking

Antibodies are responsible for antigen recognition in vertebrate organisms. Practically any molecule can be bound by antibodies. In this work structures of 73 complexes of antibodies with small antigens were taken from PDB database and compared. The main epitope of studied ligands was an aromatic ring. Antibodies bound it with a deep cavity, lying between complementary determining regions (CDR) H3 and L3 and formed by aromatic residues. In most cases the aromatic ring of ligand was placed parallel to one or two aromatic sidechains of binding site at 3.5-4 Angstrom distance. This disposition of aromatic rings is a sign of the presence of pi-stacking. It was found that small ligands with aromatics area percentage > 36% predominantly form pi-stacking interaction with antibodies. Most often this interaction was observed for residues in positions H33, H95, L32 and L93.

Molecular docking of carbohydrate ligands to antibodies: structural validation against crystal structures

Cell surface glycoproteins play vital roles in cellular homeostasis and disease. Antibody recognition of glycosylation on different cells and pathogens is critically important for immune surveillance. Conversely, adverse immune reactions resulting from antibody-carbohydrate interactions have been implicated in the development of autoimmune diseases and impact areas such as xenotransplantation and cancer treatment. Understanding the nature of antibody-carbohydrate interactions and the method by which saccharides fit into antibody binding sites is important in understanding the recognition process. In silico techniques offer attractive alternatives to experimental methods (X-ray crystallography and NMR) for the study of antibody-carbohydrate complexes. In particular, molecular docking provides information about protein-ligand interactions in systems that are difficult to study with experimental techniques. Before molecular docking can be used to investigate antibody-carbohydrate complexes, validation of an appropriate docking method is required. In this study, four popular docking programs, Glide, AutoDock, GOLD, and FlexX, were assessed for their ability to accurately dock carbohydrates to antibodies. Comparison of top ranking poses with crystal structures highlighted the strengths and weaknesses of these programs. Rigid docking, in which the protein conformation remains static, and flexible docking, where both the protein and ligand are treated as flexible, were compared. This study has revealed that generally molecular docking of carbohydrates to antibodies has been performed best by Glide.

Structural biology of carbohydrate xenoantigens

Transplantation of organs across species (xenotransplantation) is being considered to overcome the shortage of human donor organs. However, unmodified pig organs undergo an antibody-mediated hyperacute rejection that is brought about by the presence of natural antibodies to Galalpha(1,3)Gal, which is the major carbohydrate xenoantigen. Genetic modification of pig organs to remove most of the Galalpha(1,3)Gal epitopes has been achieved, but the human immune system may still recognize residual lipid-linked Galalpha(1,3)Gal carbohydrates, new (cryptic) carbohydrates or additional non-Galalpha(1,3)Gal carbohydrate xenoantigens. The structural basis for lectin and antibody recognition of Galalpha(1,3)Gal carbohydrates is starting to be understood and is discussed in this review. Antibody binding to Galalpha(1,3)Gal carbohydrates is predicted to primarily involve end-on insertion of the terminal alphaGal residue, but it is possible that groove-type binding can occur, as for some lectins. It is likely that similar antibody and lectin recognition will occur with other non-Galalpha(1,3)Gal xenoantigens, which potentially represent new barriers for pig-to-human xenotransplantation.

In silico analysis of antibody-carbohydrate interactions and its application to xenoreactive antibodies

Antibody-carbohydrate interactions play central roles in stimulating adverse immune reactions. The most familiar example of such a process is the reaction observed in ABO-incompatible blood transfusion and organ transplantation. The ABO blood groups are defined by the presence of specific carbohydrates expressed on the surface of red blood cells. Preformed antibodies in the incompatible recipient (i.e., different blood groups) recognize cells exhibiting host-incompatible ABO system antigens and proceed to initiate lysis of the incompatible cells. Pig-to-human xenotransplantation presents a similar immunological barrier. Antibodies present in humans recognize carbohydrate antigens on the surface of pig organs as foreign and proceed to initiate hyperacute xenograft rejection. The major carbohydrate xenoantigens all bear terminal Gal alpha(1,3)Gal epitopes (or alphaGal). In this study, we have developed and validated a site mapping technique to investigate protein-ligand recognition and applied it to antibody-carbohydrate systems. This site mapping technique involves the use of molecular docking to generate a series of antibody-carbohydrate complexes, followed by analysis of the hydrogen bonding and van der Waals interactions occurring in each complex. The technique was validated by application to a series of antibody-carbohydrate crystal structures. In each case, the majority of interactions made in the crystal structure complex were able to be reproduced. The technique was then applied to investigate xenoantigen recognition by a panel of monoclonal anti-alphaGal antibodies. The results indicate that there is a significant overlap of the antibody regions engaging the xenoantigens across the panel. Likewise, similar regions of the xenoantigens interact with the antibodies.

The importance of discerning shape in molecular pharmacology

Shape is a fundamentally important molecular feature that often determines the fate of a compound in terms of molecular interactions with preferred and non-preferred biological targets. Complementarity of binding in small-molecule-protein, peptide-receptor, antigen-antibody and protein-protein interactions is the key to life and survival and also to targeting molecules with bioactivity. We review the application of shape in various biological systems such as substrate recognition, ligand specificity or selectivity and antibody recognition in the context of computational methods such as docking, quantitative structure-activity relationships, classification models and similarity-search algorithms. These in silico pharmacology methods have recently demonstrated the importance and applicability of determining molecular shape in drug discovery, virtual screening and predictive toxicology. The results from recently published studies show that shape and shape-based descriptors are at least as useful as other traditional molecular descriptors.

Sensitive and specific detection of the non-human sialic Acid N-glycolylneuraminic acid in human tissues and biotherapeutic products

BACKGROUND

Humans are genetically defective in synthesizing the common mammalian sialic acid N-glycolylneuraminic acid (Neu5Gc), but can metabolically incorporate it from dietary sources (particularly red meat and milk) into glycoproteins and glycolipids of human tumors, fetuses and some normal tissues. Metabolic incorporation of Neu5Gc from animal-derived cells and medium components also results in variable contamination of molecules and cells intended for human therapies. These Neu5Gc-incorporation phenomena are practically significant, because normal humans can have high levels of circulating anti-Neu5Gc antibodies. Thus, there is need for the sensitive and specific detection of Neu5Gc in human tissues and biotherapeutic products. Unlike monoclonal antibodies that recognize Neu5Gc only in the context of underlying structures, chicken immunoglobulin Y (IgY) polyclonal antibodies can recognize Neu5Gc in broader contexts. However, prior preparations of such antibodies (including our own) suffered from some non-specificity, as well as some cross-reactivity with the human sialic acid N-acetylneuraminic acid (Neu5Ac).

METHODOLOGY/PRINCIPAL FINDINGS

We have developed a novel affinity method utilizing sequential columns of immobilized human and chimpanzee serum sialoglycoproteins, followed by specific elution from the latter column by free Neu5Gc. The resulting mono-specific antibody shows no staining in tissues or cells from mice with a human-like defect in Neu5Gc production. It allows sensitive and specific detection of Neu5Gc in all underlying glycan structural contexts studied, and is applicable to immunohistochemical, enzyme-linked immunosorbent assay (ELISA), Western blot and flow cytometry analyses. Non-immune chicken IgY is used as a reliable negative control. We show that these approaches allow sensitive detection of Neu5Gc in human tissue samples and in some biotherapeutic products, and finally show an example of how Neu5Gc might be eliminated from such products, by using a human cell line grown under defined conditions.

CONCLUSIONS

We report a reliable antibody-based method for highly sensitive and specific detection of the non-human sialic acid Neu5Gc in human tissues and biotherapeutic products that has not been previously described.

Diversity in specificity, abundance, and composition of anti-Neu5Gc antibodies in normal humans: potential implications for disease

Human heterophile antibodies that agglutinate animal erythrocytes are known to detect the nonhuman sialic acid N-glycolylneuraminic acid (Neu5Gc). This monosaccharide cannot by itself fill the binding site (paratope) of an antibody and can also be modified and presented in various linkages, on diverse underlying glycans. Thus, we hypothesized that the human anti-Neu5Gc antibody response is diverse and polyclonal. Here, we use a novel set of natural and chemoenzymatically synthesized glycans to show that normal humans have an abundant and diverse spectrum of such anti-Neu5Gc antibodies, directed against a variety of Neu5Gc-containing epitopes. High sensitivity and specificity assays were achieved by using N-acetylneuraminic acid (Neu5Ac)-containing probes (differing from Neu5Gc by one less oxygen atom) as optimal background controls. The commonest anti-Neu5Gc antibodies are of the IgG class. Moreover, the range of reactivity and Ig classes of antibodies vary greatly amongst normal humans, with some individuals having remarkably large amounts, even surpassing levels of some well-known natural blood group and xenoreactive antibodies. We purified these anti-Neu5Gc antibodies from individual human sera using a newly developed affinity method and showed that they bind to wild-type but not Neu5Gc-deficient mouse tissues. Moreover, they bind back to human carcinomas that have accumulated Neu5Gc in vivo. As dietary Neu5Gc is primarily found in red meat and milk products, we suggest that this ongoing antigen-antibody reaction may generate chronic inflammation, possibly contributing to the high frequency of diet-related carcinomas and other diseases in humans.

Hapten synthesis and monoclonal antibody-based immunoassay development for detection of the fungicide trifloxystrobin

High-affinity and selective monoclonal antibodies have been produced against the strobilurin fungicide trifloxystrobin. A battery of functionalized haptens has been synthesized, and conjugate-coated enzyme-linked immunosorbent assays following different procedures have been developed. On the one hand, a two-step conjugate-coated immunoassay was optimized using extended or short incubation times, with limits of detection of 0.10 ng/mL for the extended assay and 0.17 ng/mL for the rapid assay. On the other hand, an immunoassay in the conjugate-coated format was optimized following a procedure consisting of just one incubation step. This one-step assay had a limit of detection of 0.21 ng/mL. All of these assays showed detection limits for trifloxystrobin in the low parts per billion range, well below the common maximum residue limits for this pesticide in foodstuffs (50 microg/kg).