Leveraging Cross-Linking Mass Spectrometry for Modeling Antibody-Antigen Complexes.

Elucidating antibody-antigen complexes at the atomic level is of utmost interest for understanding immune responses and designing better therapies. Cross-linking mass spectrometry (XL-MS) has emerged as a powerful tool for mapping protein-protein interactions, suggesting valuable structural insights. However, the use of XL-MS studies to enable epitope/paratope mapping of antibody-antigen complexes is still limited up to now. XL-MS data can be used to drive integrative modeling of antibody-antigen complexes, where cross-links information serves as distance restraints for the precise determination of binding interfaces. In this approach, XL-MS data are employed to identify connections between binding interfaces of the antibody and the antigen, thus informing molecular modeling. Current literature provides minimal input about the impact of XL-MS data on the integrative modeling of antibody-antigen complexes. Here, we applied XL-MS to retrieve information about binding interfaces of three antibody-antigen complexes. We leveraged XL-MS data to perform integrative modeling using HADDOCK (active-passive residues and distance restraints strategies) and AlphaLink2. We then compared these three approaches with initial predictions of investigated antibody-antigen complexes by AlphaFold Multimer. This work emphasizes the importance of cross-linking data in resolving conformational dynamics of antibody-antigen complexes, ultimately enhancing the design of better protein therapeutics and vaccines.

Unveiling CD59-Antibody Interactions to Design Paratope-Mimicking Peptides for Complement Modulation.

CD59 is an abundant immuno-regulatory human protein that protects cells from damage by inhibiting the complement system. CD59 inhibits the assembly of the Membrane Attack Complex (MAC), the bactericidal pore-forming toxin of the innate immune system. In addition, several pathogenic viruses, including HIV-1, escape complement-mediated virolysis by incorporating this complement inhibitor in their own viral envelope. This makes human pathogenic viruses, such as HIV-1, not neutralised by the complement in human fluids. CD59 is also overexpressed in several cancer cells to resist the complement attack. Consistent with its importance as a therapeutical target, CD59-targeting antibodies have been proven to be successful in hindering HIV-1 growth and counteracting the effect of complement inhibition by specific cancer cells. In this work, we make use of bioinformatics and computational tools to identify CD59 interactions with blocking antibodies and to describe molecular details of the paratope-epitope interface. Based on this information, we design and produce paratope-mimicking bicyclic peptides able to target CD59. Our results set the basis for the development of antibody-mimicking small molecules targeting CD59 with potential therapeutic interest as complement activators.

SARS-CoV-2 Spike Protein Interaction Space.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a +sense single-strand RNA virus. The virus has four major surface proteins: spike (S), envelope (E), membrane (M), and nucleocapsid (N), respectively. The constitutive proteins present a high grade of symmetry. Identifying a binding site is difficult. The virion is approximately 50-200 nm in diameter. Angiotensin-converting enzyme 2 (ACE2) acts as the cell receptor for the virus. SARS-CoV-2 has an increased affinity to human ACE2 compared with the original SAR strain. Topological space, and its symmetry, is a critical component in molecular interactions. By exploring this space, a suitable ligand space can be characterized accordingly. A spike protein (S) computational model in a complex with ACE 2 was generated using silica methods. Topological spaces were probed using high computational throughput screening techniques to identify and characterize the topological space of both SARS and SARS-CoV-2 spike protein and its ligand space. In order to identify the symmetry clusters, computational analysis techniques, together with statistical analysis, were utilized. The computations are based on crystallographic protein data bank PDB-based models of constitutive proteins. Cartesian coordinates of component atoms and some cluster maps were generated and analyzed. Dihedral angles were used in order to compute a topological receptor space. This computational study uses a multimodal representation of spike protein interactions with some fragment proteins. The chemical space of the receptors (a dimensional volume) suggests the relevance of the receptor as a drug target. The spike protein S of SARS and SARS-CoV-2 is analyzed and compared. The results suggest a mirror symmetry of SARS and SARS-CoV-2 spike proteins. The results show thatSARS-CoV-2 space is variable and has a distinct topology. In conclusion, surface proteins grant virion variability and symmetry in interactions with a potential complementary target (protein, antibody, ligand). The mirror symmetry of dihedral angle clusters determines a high specificity of the receptor space.

Antibody Binding Captures High Energy State of an Antigen: The Case of Nsp1 SARS-CoV-2 as Revealed by Hydrogen-Deuterium Exchange Mass Spectrometry.

We describe an investigation using structural mass spectrometry (MS) of the impact of two antibodies, 15497 and 15498, binding the highly flexible SARS-CoV-2 Nsp1 protein. We determined the epitopes and paratopes involved in the antibody-protein interactions by using hydrogen-deuterium exchange MS (HDX-MS). Notably, the Fab (Fragment antigen binding) for antibody 15498 captured a high energy form of the antigen exhibiting significant conformational changes that added flexibility over most of the Nsp1 protein. The Fab for antibody 15497, however, showed usual antigen binding behavior, revealing local changes presumably including the binding site. These findings illustrate an unusual antibody effect on an antigen and are consistent with the dynamic nature of the Nsp1 protein. Our studies suggest that this interaction capitalizes on the high flexibility of Nsp1 to undergo conformational change and be trapped in a higher energy state by binding with a specific antibody.

G-Quadruplex Recognition by DARPIns through Epitope/Paratope Analogy.

We investigated the mechanisms leading to the specific recognition of Guanine Guadruplex (G4) by DARPins peptides, which can lead to the design of G4 s specific sensors. To this end we carried out all-atom molecular dynamic simulations to unravel the interactions between specific nucleic acids, including human-telomeric (h-telo), Bcl-2, and c-Myc, with different peptides, forming a DARPin/G4 complex. By comparing the sequences of DARPin with that of a peptide known for its high affinity for c-Myc, we show that the recognition cannot be ascribed to sequence similarity but, instead, depends on the complementarity between the three-dimensional arrangement of the molecular fragments involved: the α-helix/loops domain of DARPin and the G4 backbone. Our results reveal that DARPins tertiary structure presents a charged hollow region in which G4 can be hosted, thus the more complementary the structural shapes, the more stable the interaction.

Downsizing antibodies: Towards complementarity-determining region (CDR)-based peptide mimetics.

Monoclonal antibodies emerged as an important therapeutic drug class with remarkable specificity and binding affinity. Nonetheless, these heterotetrameric immunoglobulin proteins come with high manufacturing and therapeutic costs which can take extraordinary proportions, besides other limitations such as their limited in cellulo access imposed by their molecular size (ca. 150 kDa). These drawbacks stimulated the development of downsized functional antibody fragments (ca. 15-50 kDa), together with smaller synthetic peptides (ca. 1-3 kDa) derived from the antibodies' crucial complementarity-determining regions (CDR). Despite the general lack of success in the literal translation of CDR loops in peptide mimetics, rational structure-based and computational approaches have shown their potential for obtaining functional CDR-based peptide mimetics. In this review, we describe the efforts made in the development of antibody and nanobody paratope-derived peptide mimetics with particular focus on the used design strategies, in addition to highlighting the challenges associated with their development.

Conformational epitope matching and prediction based on protein surface spiral features.

BACKGROUND: A conformational epitope (CE) is composed of neighboring amino acid residues located on an antigenic protein surface structure. CEs bind their complementary paratopes in B-cell receptors and/or antibodies. An effective and efficient prediction tool for CE analysis is critical for the development of immunology-related applications, such as vaccine design and disease diagnosis. RESULTS: We propose a novel method consisting of two sequential modules: matching and prediction. The matching module includes two main approaches. The first approach is a complete sequence search (CSS) that applies BLAST to align the sequence with all known antigen sequences. Fragments with high epitope sequence identities are identified and the predicted residues are annotated on the query structure. The second approach is a spiral vector search (SVS) that adopts a novel surface spiral feature vector for large-scale surface patch detection when queried against a comprehensive epitope database. The prediction module also contains two proposed subsystems. The first system is based on knowledge-based energy and geometrical neighboring residue contents, and the second system adopts combinatorial features, including amino acid contents and physicochemical characteristics, to formulate corresponding geometric spiral vectors and compare them with all spiral vectors from known CEs. An integrated testing dataset was generated for method evaluation, and our two searching methods effectively identified all epitope regions. The prediction results show that our proposed method outperforms previously published systems in terms of sensitivity, specificity, positive predictive value, and accuracy. CONCLUSIONS: The proposed method significantly improves the performance of traditional epitope prediction. Matching followed by prediction is an efficient and effective approach compared to predicting directly on specific surfaces containing antigenic characteristics.

Antibody-mediated enzyme formation: Its legacy at age fifty-four.

Antibody-mediated enzyme formation is a phenomenon first described in 1968 and further studied by molecular Immunologists and Biochemists over the following five decades. The present review is made mainly by analyzing the 27 articles concerned with AMEF that appeared over the course of 47 years, commenting 16 original figures selected to be re-printed in AMEF's Legacy. We, the reviewers, started by revisiting our own "insider's" experience of discovery, and followed by considering all results, our own and of members of other AMEF Labs. We had planned to conclude the review by correlating the various AMEF mutants to a detailed knowledge of the consensus betaGal structure. However, we became aware of several "robust" papers, published between 1989 and 2014, by authors outside of AMEF Labs. We familiarly called this surge: "The Second Wave" and adorned it with a doodle in Hokusai style. We were thrilled and happy to take them on board and properly examined their data. A team of this second wave had imagined unique uses for AMEF, and new doors to modern biotechnology. Another one had used AMEF as Tool and Marker to attain high levels of crystallography, solving puzzles of conformation, and ultimate structure. Together, they doubled our motivation to review AMEF. Serendipity gives us back the pleasure of finding, a treat at any age.

HSymM-guided engineering of the immunodominant p53 transactivation domain putative peptide antigen for improved binding to its anti-p53 monoclonal antibody.

A novel engineering strategy to improve autoantibody detection with peptide fragments derived from the parent antigen is presented. The model system studied was the binding of the putative p53 TAD peptide antigen (residues 46-55) to its cognate anti-p53 antibody, ab28. Each engineered peptide contained the full decapeptide epitope and differed only in the flanking regions. Since minimal structural information was available to guide the design, a simple epitope:paratope binding model was applied. The Hidden Symmetry Model, which we recently reported, was used to guide peptide design and estimate per-residue contributions to interaction free energy as a function of added C- and N-terminal flanking peptides. Twenty-four peptide constructs were designed, synthesized, and assessed for binding affinity to ab28 by surface plasmon resonance, and a subset of these peptides were evaluated in a simulated immunoassay for limit of detection. Many peptides exhibited over 200-fold enhancements in binding affinity and improved limits of detection. The epitope was reevaluated and is proposed to be the undecapeptide corresponding to residues 45-55. HSymM calculated binding free energy and experimental data were found to be in good agreement (R2 > 0.75).

Active-site MMP-selective antibody inhibitors discovered from convex paratope synthetic libraries.

Proteases are frequent pharmacological targets, and their inhibitors are valuable drugs in multiple pathologies. The catalytic mechanism and the active-site fold, however, are largely conserved among the protease classes, making the development of the selective inhibitors exceedingly challenging. In our departure from the conventional strategies, we reviewed the structure of known camelid inhibitory antibodies, which block enzyme activities via their unusually long, convex-shaped paratopes. We synthesized the human Fab antibody library (over 1.25 × 109 individual variants) that carried the extended, 23- to 27-residue, complementarity-determining region (CDR)-H3 segments. As a proof of principle, we used the catalytic domain of matrix metalloproteinase-14 (MMP-14), a promalignant protease and a drug target in cancer, as bait. In our screens, we identified 20 binders, of which 14 performed as potent and selective inhibitors of MMP-14 rather than as broad-specificity antagonists. Specifically, Fab 3A2 bound to MMP-14 in the vicinity of the active pocket with a high 4.8 nM affinity and was similarly efficient (9.7 nM) in inhibiting the protease cleavage activity. We suggest that the convex paratope antibody libraries described here could be readily generalized to facilitate the design of the antibody inhibitors to many additional enzymes.

Deciphering evolution of immune recognition in antibodies.

BACKGROUND: Antibody, the primary effector molecule of the immune system, evolves after initial encounter with the antigen from a precursor form to a mature one to effectively deal with the antigen. Antibodies of a lineage diverge through antigen-directed isolated pathways of maturation to exhibit distinct recognition potential. In the context of evolution in immune recognition, diversity of antigen cannot be ignored. While there are reports on antibody lineage, structural perspective with respect to diverse recognition potential in a lineage has never been studied. Hence, it is crucial to evaluate how maturation leads to topological tailoring within a lineage enabling them to interact with significantly distinct antigens. RESULTS: A data-driven approach was undertaken for the study. Global experimental mouse and human antibody-antigen complex structures from PDB were compiled into a coherent database of germline-linked antibodies bound with distinct antigens. Structural analysis of all lineages showed variations in CDRs of both H and L chains. Observations of conformational adaptation made from analysis of static structures were further evaluated by characterizing dynamics of interaction in two lineages, mouse VH1-84 and human VH5-51. Sequence and structure analysis of the lineages explained that somatic mutations altered the geometries of individual antibodies with common structural constraints in some CDRs. Additionally, conformational landscape obtained from molecular dynamics simulations revealed that incoming pathogen led to further conformational divergence in the paratope (as observed across datasets) even while maintaining similar overall backbone topology. MM-GB/SA analysis showed binding energies to be in physiological range. Results of the study are coherent with experimental observations. CONCLUSIONS: The findings of this study highlight basic structural principles shaping the molecular evolution of a lineage for significantly diverse antigens. Antibodies of a lineage follow different developmental pathways while preserving the imprint of the germline. From the study, it can be generalized that structural diversification of the paratope is an outcome of natural selection of a conformation from an available ensemble, which is further optimized for antigen interaction. The study establishes that starting from a common lineage, antibodies can mature to recognize a wide range of antigens. This hypothesis can be further tested and validated experimentally.

Augmented Binary Substitution: Single-pass CDR germ-lining and stabilization of therapeutic antibodies.

Although humanized antibodies have been highly successful in the clinic, all current humanization techniques have potential limitations, such as: reliance on rodent hosts, immunogenicity due to high non-germ-line amino acid content, v-domain destabilization, expression and formulation issues. This study presents a technology that generates stable, soluble, ultrahumanized antibodies via single-step complementarity-determining region (CDR) germ-lining. For three antibodies from three separate key immune host species, binary substitution CDR cassettes were inserted into preferred human frameworks to form libraries in which only the parental or human germ-line destination residue was encoded at each position. The CDR-H3 in each case was also augmented with 1 ± 1 random substitution per clone. Each library was then screened for clones with restored antigen binding capacity. Lead ultrahumanized clones demonstrated high stability, with affinity and specificity equivalent to, or better than, the parental IgG. Critically, this was mainly achieved on germ-line frameworks by simultaneously subtracting up to 19 redundant non-germ-line residues in the CDRs. This process significantly lowered non-germ-line sequence content, minimized immunogenicity risk in the final molecules and provided a heat map for the essential non-germ-line CDR residue content of each antibody. The ABS technology therefore fully optimizes the clinical potential of antibodies from rodents and alternative immune hosts, rendering them indistinguishable from fully human in a simple, single-pass process.

Indirect Microcontact Printing to Create Functional Patterns of Physisorbed Antibodies.

Microcontact printing (µCP) is a practical and versatile approach to create nanostructured patterns of biomolecular probes, but it involves conformational changes on the patterned bioreceptors that often lead to a loss on the biological activity of the resulting structures. Herein we introduce indirect µCP to create functional patterns of bioreceptors on solid substrates. This is a simple strategy that relies on physisorbing biomolecular probes of interest in the nanostructured gaps that result after patterning backfilling agents by standard µCP. This study presents the approach, assesses bovine serum albumin as backfilling agent for indirect µCP on different materials, reports the limitations of standard µCP on the functionality of patterned antibodies, and demonstrates the capabilities of indirect µCP to solve this issue. Bioreceptors were herein structured as diffractive gratings and used to measure biorecognition events in label-free conditions. Besides, as a preliminary approach towards sensing biomarkers, this work also reports the implementation of indirect µCP in an immunoassay to detect human immunoglobulin E.

Origins of specificity and affinity in antibody-protein interactions.

Natural antibodies are frequently elicited to recognize diverse protein surfaces, where the sequence features of the epitopes are frequently indistinguishable from those of nonepitope protein surfaces. It is not clearly understood how the paratopes are able to recognize sequence-wise featureless epitopes and how a natural antibody repertoire with limited variants can recognize seemingly unlimited protein antigens foreign to the host immune system. In this work, computational methods were used to predict the functional paratopes with the 3D antibody variable domain structure as input. The predicted functional paratopes were reasonably validated by the hot spot residues known from experimental alanine scanning measurements. The functional paratope (hot spot) predictions on a set of 111 antibody-antigen complex structures indicate that aromatic, mostly tyrosyl, side chains constitute the major part of the predicted functional paratopes, with short-chain hydrophilic residues forming the minor portion of the predicted functional paratopes. These aromatic side chains interact mostly with the epitope main chain atoms and side-chain carbons. The functional paratopes are surrounded by favorable polar atomistic contacts in the structural paratope-epitope interfaces; more that 80% these polar contacts are electrostatically favorable and about 40% of these polar contacts form direct hydrogen bonds across the interfaces. These results indicate that a limited repertoire of antibodies bearing paratopes with diverse structural contours enriched with aromatic side chains among short-chain hydrophilic residues can recognize all sorts of protein surfaces, because the determinants for antibody recognition are common physicochemical features ubiquitously distributed over all protein surfaces.

Antibody promiscuity: Understanding the paradigm shift in antigen recognition.

Affinity maturation is associated with reduced malleability of the paratope that optimizes an antibody to bind to the bonafide antigen with high specificity and affinity. However, it has been illustrated that mature antibodies tend to exhibit promiscuity despite acquisition of a relatively rigid binding pocket. Such an attribute is contrary to the established paradigm of specificity in antigen recognition. In this review, an explicit dissection of the underlying mechanisms fostering such versatility in mature antibodies has been done. Polyspecificity is essentially achieved by undergoing minimal structural rearrangement at the paratope complemented with plasticity in interaction with antigen. Besides, the structural invariance of the antigen across species could modulate mature antibody specificity. Polyreactivity has been well documented for germline antibodies as broad spectrum antibody repertoire amplification is primarily governed by recombination event of the genetic machinery, which is further expanded at the structural and functional level of interaction. Degenerate specificity in antigen recognition obviates the need to produce distinct antibody for every incoming epitope.

Protein Crystallography in Vaccine Research and Development.

The use of protein X-ray crystallography for structure-based design of small-molecule drugs is well-documented and includes several notable success stories. However, it is less well-known that structural biology has emerged as a major tool for the design of novel vaccine antigens. Here, we review the important contributions that protein crystallography has made so far to vaccine research and development. We discuss several examples of the crystallographic characterization of vaccine antigen structures, alone or in complexes with ligands or receptors. We cover the critical role of high-resolution epitope mapping by reviewing structures of complexes between antigens and their cognate neutralizing, or protective, antibody fragments. Most importantly, we provide recent examples where structural insights obtained via protein crystallography have been used to design novel optimized vaccine antigens. This review aims to illustrate the value of protein crystallography in the emerging discipline of structural vaccinology and its impact on the rational design of vaccines.

Cation-π, amino-π, π-π, and H-bond interactions stabilize antigen-antibody interfaces.

The identification of immunogenic regions on the surface of antigens, which are able to stimulate an immune response, is a major challenge for the design of new vaccines. Computational immunology aims at predicting such regions--in particular B-cell epitopes--but is far from being reliably applicable on a large scale. To gain understanding into the factors that contribute to the antigen-antibody affinity and specificity, we perform a detailed analysis of the amino acid composition and secondary structure of antigen and antibody surfaces, and of the interactions that stabilize the complexes, in comparison with the composition and interactions observed in other heterodimeric protein interfaces. We make a distinction between linear and conformational B-cell epitopes, according to whether they consist of successive residues along the polypeptide chain or not. The antigen-antibody interfaces were shown to differ from other protein-protein interfaces by their smaller size, their secondary structure with less helices and more loops, and the interactions that stabilize them: more H-bond, cation-π, amino-π, and π-π interactions, and less hydrophobic packing; linear and conformational epitopes can clearly be distinguished. Often, chains of successive interactions, called cation/amino-π and π-π chains, are formed. The amino acid composition differs significantly between the interfaces: antigen-antibody interfaces are less aliphatic and more charged, polar and aromatic than other heterodimeric protein interfaces. Moreover, paratopes and epitopes-albeit to a lesser extent-have amino acid compositions that are distinct from general protein surfaces. This specificity holds promise for improving B-cell epitope prediction.

Mapping the epitope of PD-L1 to the paratope of the antibody durvalumab using molecular dynamics simulation.

OBJECTIVES: Durvalumab, a human monoclonal antibody that stops PD-L1 from attaching itself to CD80 and PD-1, was approved by the Food and Drug Administration for use in cancer therapy. An essential stage in antibody optimization is mapping paratope residues to epitope residues. In this study, our earlier computer-aided method based on molecular dynamics (MD) simulations was used to observe the paratope residues on durvalumab and their companions on PD-L1. METHODS: The durvalumab/PD-L1 complex model was obtained from the Protein Data Bank and used in a rectangular box for solvation. On durvalumab, the paratope residues and their companions on PD-L1 were identified using MD simulations. The interface residues were ranked on the basis of their contributions to the binding of durvalumab and PD-L1 by assessing the stability of hydrogen bonds and salt bridges. This assessment was conducted using free and guided MD simulations. RESULTS: Seventeen residues, including ASP26, GLU58, GLU60, ASP61, ARG113, ARG125, and THR127 on PD-L1 and H31ARG, H52LYS, H53GLN, H57GLU, H99GLU, H103PHE, H113ARG, L28ARG, L31SER, and L92TYR on durvalumab, were expected to be necessary for the binding of durvalumab to PD-L1. ASP26, ARG113, and ARG125 on PD-L1 were essential for its binding to PD-1. Eight residues (GLU60, ASP61, and THR127 on PD-L1 and L31SER, H99GLU, H53GLU, H31ARG, and H113ARG on durvalumab) were newly found, and two residues (LYS124 on PD-L1 and L94SER on durvalumab) proven nonessential for complexation, compared to the findings from the examined crystal structure. CONCLUSIONS: The antithrombotic antibody of durvalumab's paratope may be effectively mapped to the PD-L1 epitope using the existing computer method. This information will help optimize durvalumab.

Machine-learning-based structural analysis of interactions between antibodies and antigens.

Computational analysis of paratope-epitope interactions between antibodies and their corresponding antigens can facilitate our understanding of the molecular mechanism underlying humoral immunity and boost the design of new therapeutics for many diseases. The recent breakthrough in artificial intelligence has made it possible to predict protein-protein interactions and model their structures. Unfortunately, detecting antigen-binding sites associated with a specific antibody is still a challenging problem. To tackle this challenge, we implemented a deep learning model to characterize interaction patterns between antibodies and their corresponding antigens. With high accuracy, our model can distinguish between antibody-antigen complexes and other types of protein-protein complexes. More intriguingly, we can identify antigens from other common protein binding regions with an accuracy of higher than 70% even if we only have the epitope information. This indicates that antigens have distinct features on their surface that antibodies can recognize. Additionally, our model was unable to predict the partnerships between antibodies and their particular antigens. This result suggests that one antigen may be targeted by more than one antibody and that antibodies may bind to previously unidentified proteins. Taken together, our results support the precision of antibody-antigen interactions while also suggesting positive future progress in the prediction of specific pairing.

Structural trends in antibody-antigen binding interfaces: a computational analysis of 1833 experimentally determined 3D structures.

Antibodies are attractive therapeutic candidates due to their ability to bind cognate antigens with high affinity and specificity. Still, the underlying molecular rules governing the antibody-antigen interface remain poorly understood, making in silico antibody design inherently difficult and keeping the discovery and design of novel antibodies a costly and laborious process. This study investigates the characteristics of antibody-antigen binding interfaces through a computational analysis of more than 850,000 atom-atom contacts from the largest reported set of antibody-antigen complexes with 1833 nonredundant, experimentally determined structures. The analysis compares binding characteristics of conventional antibodies and single-domain antibodies (sdAbs) targeting both protein- and peptide antigens. We find clear patterns in the number antibody-antigen contacts and amino acid frequencies in the paratope. The direct comparison of sdAbs and conventional antibodies helps elucidate the mechanisms employed by sdAbs to compensate for their smaller size and the fact that they harbor only half the number of complementarity-determining regions compared to conventional antibodies. Furthermore, we pinpoint antibody interface hotspot residues that are often found at the binding interface and the amino acid frequencies at these positions. These findings have direct potential applications in antibody engineering and the design of improved antibody libraries.