Origins of specificity and affinity in antibody-protein interactions

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.

Alternative target recognition elements for chimeric antigen receptor (CAR) T cells: beyond standard antibody fragments

BACKGROUND AIMS

Chimeric antigen receptor T (CAR-T) cells are a remarkably efficacious, highly promising and rapidly evolving strategy in the field of immuno-oncology. The precision of these targeted cellular therapies is driven by the specificity of the antigen recognition element (the "binder") encoded in the CAR. This binder redirects these immune effector cells precisely toward a defined antigen on the surface of cancer cells, leading to T-cell receptor-independent tumor lysis. Currently, for tumor targeting most CAR-T cells are designed using single-chain variable fragments (scFvs) derived from murine or human immunoglobulins. However, there are several emerging alternative binder modalities that are finding increasing utility for improved CAR function beyond scFvs.

METHODS

Here we review the most recent developments in the use of non-canonical protein binding domains in CAR design, including nanobodies, DARPins, natural ligands, and de novo-designed protein elements.

RESULTS

Overall, we describe how new protein binder formats, with their unique structural properties and mechanisms of action, may possess key advantages over traditional scFv CAR designs.

CONCLUSIONS

These alternative binder designs may contribute to enhanced CAR-T therapeutic options and, ultimately, improved outcomes for cancer patients.

Structural, Biophysical, and Computational Studies of a Murine Light Chain Dimer

Antibodies are widely used in medicinal and scientific research due to their ability to bind to a specific antigen. Most often, antibodies are composed of heavy and light chain domains. Under physiological conditions, light chains are produced in excess, as compared to the heavy chain. It is now known that light chains are not silent partners of the heavy chain and can modulate the immune response independently. In this work, the first crystal structure of a light chain dimer originating from mice is described. It represents the light chain dimer of 6A8, a monoclonal antibody specific to the allergen Der f 1. Building on the unexpected occurrence of this kind of dimer, we have demonstrated that this light chain is stable in solution alone. Moreover, enzyme-linked immunosorbent assays (ELISA) have revealed that, when the light chain is not partnered to its corresponding heavy chain, it interacts non-specifically with a wide range of proteins. Computational studies were used to provide insight on the role of the 6A8 heavy chain domain in the specific binding to Der f 1. Overall, this work demonstrates and supports the ongoing notion that light chains can function by themselves and are not silent partners of heavy chains.

Understanding the Specific Implications of Amino Acids in the Antibody Development

As the demand for immunotherapy to treat and manage cancers, infectious diseases and other disorders grows, a comprehensive understanding of amino acids and their intricate role in antibody engineering has become a prime requirement. Naturally produced antibodies may not have the most suitable amino acids at the complementarity determining regions (CDR) and framework regions, for therapeutic purposes. Therefore, to enhance the binding affinity and therapeutic properties of an antibody, the specific impact of certain amino acids on the antibody's architecture must be thoroughly studied. In antibody engineering, it is crucial to identify the key amino acid residues that significantly contribute to improving antibody properties. Therapeutic antibodies with higher binding affinity and improved functionality can be achieved through modifications or substitutions with highly suitable amino acid residues. Here, we have indicated the frequency of amino acids and their association with the binding free energy in CDRs. The review also analyzes the experimental outcome of two studies that reveal the frequency of amino acids in CDRs and provides their significant correlation between the outcomes. Additionally, it discusses the various bond interactions within the antibody structure and antigen binding. A detailed understanding of these amino acid properties should assist in the analysis of antibody sequences and structures needed for designing and enhancing the overall performance of therapeutic antibodies.

Functional insights of Tyr37 in framework region 2 directly contributing to the binding affinities and dissociation kinetics in single-domain VHH antibodies

Single-domain VHH antibody is regarded as one of the promising antibody classes for therapeutic and diagnostic applications. VHH antibodies have amino acids in framework region 2 that are distinct from those in conventional antibodies, such as the Val37Phe/Tyr (V37F/Y) substitution. Correlations between the residue type at position 37 and the conformation of the CDR3 in VHH antigen recognition have been previously reported. However, few studies focused on the meaning of harboring two residue types in position 37 of VHH antibodies, and the concrete roles of Y37 have been little to be elucidated. Here, we investigated the functional states of position 37 in co-crystal structures and performed analyses of three model antibodies with either F or Y at position 37. Our analysis indicates that Y at position 37 enhances the dissociation rate, which is highly correlated with drug efficacy. Our findings help to explain the molecular mechanisms that distinguish VHH antibodies from conventional antibodies.

Animal-derived food allergen: A review on the available crystal structure and new insights into structural epitope

Immunoglobulin E (IgE)-mediated food allergy is a rapidly growing public health problem. The interaction between allergens and IgE is at the core of the allergic response. One of the best ways to understand this interaction is through structural characterization. This review focuses on animal-derived food allergens, overviews allergen structures determined by X-ray crystallography, presents an update on IgE conformational epitopes, and explores the structural features of these epitopes. The structural determinants of allergenicity and cross-reactivity are also discussed. Animal-derived food allergens are classified into limited protein families according to structural features, with the calcium-binding protein and actin-binding protein families dominating. Progress in epitope characterization has provided useful information on the structural properties of the IgE recognition region. The data reveals that epitopes are located in relatively protruding areas with negative surface electrostatic potential. Ligand binding and disulfide bonds are two intrinsic characteristics that influence protein structure and impact allergenicity. Shared structures, local motifs, and shared epitopes are factors that lead to cross-reactivity. The structural properties of epitope regions and structural determinants of allergenicity and cross-reactivity may provide directions for the prevention, diagnosis, and treatment of food allergies. Experimentally determined structure, especially that of antigen-antibody complexes, remains limited, and the identification of epitopes continues to be a bottleneck in the study of animal-derived food allergens. A combination of traditional immunological techniques and emerging bioinformatics technology will revolutionize how protein interactions are characterized.

Unveiling the affinity-stability relationship in anti-measles virus antibodies: a computational approach for hotspots prediction

Recent years have seen an uptick in the use of computational applications in antibody engineering. These tools have enhanced our ability to predict interactions with antigens and immunogenicity, facilitate humanization, and serve other critical functions. However, several studies highlight the concern of potential trade-offs between antibody affinity and stability in antibody engineering. In this study, we analyzed anti-measles virus antibodies as a case study, to examine the relationship between binding affinity and stability, upon identifying the binding hotspots. We leverage in silico tools like Rosetta and FoldX, along with molecular dynamics (MD) simulations, offering a cost-effective alternative to traditional in vitro mutagenesis. We introduced a pattern in identifying key residues in pairs, shedding light on hotspots identification. Experimental physicochemical analysis validated the predicted key residues by confirming significant decrease in binding affinity for the high-affinity antibodies to measles virus hemagglutinin. Through the nature of the identified pairs, which represented the relative hydropathy of amino acid side chain, a connection was proposed between affinity and stability. The findings of the study enhance our understanding of the interactions between antibody and measles virus hemagglutinin. Moreover, the implications of the observed correlation between binding affinity and stability extend beyond the field of anti-measles virus antibodies, thereby opening doors for advancements in antibody research.

Revisiting Protein-Copolymer Binding Mechanisms: Insights beyond the "Lock-and-Key" Model

The "lock-and-key" model that emphasizes the concept of chemical-structural complementary is the key mechanism for explaining the selectivity between small ligands and a larger adsorbent molecule. In this work, concerning the copolymer chain using only the combination of N-isopropylacrylamide (NIPAm) and hydrophobic N-tert-butylacrylamide (TBAm) monomers and by large-scale atomistic molecular dynamics simulations, our results show that the flexible copolymer chain may exhibit strong binding affinity for the biomarker protein epithelial cell adhesion molecule, in the absence of hydrophobic matching and strong structural complementarity. This surprising binding behavior, which cannot be anticipated by the "lock-and-key" model, can be attributed to the preferential interactions established by the copolymer with the protein's hydrophilic exterior. We observe that increasing the fraction of incorporated TBAm monomers leads to a prevalence of interactions with asparagine and glutamine amino acids due to the emerging hydrogen bonding with both NIPAm and TBAm monomers. Our findings suggest the appearance of highly specific and high-affinity binding sites on the protein created by engineering the copolymer composition, which motivates the applications of copolymers as protein affinity reagents.

Molecular surface descriptors to predict antibody developability: sensitivity to parameters, structure models, and conformational sampling

In silico assessment of antibody developability during early lead candidate selection and optimization is of paramount importance, offering a rapid and material-free screening approach. However, the predictive power and reproducibility of such methods depend heavily on the selection of molecular descriptors, model parameters, accuracy of predicted structure models, and conformational sampling techniques. Here, we present a set of molecular surface descriptors specifically designed for predicting antibody developability. We assess the performance of these descriptors by benchmarking their correlations with an extensive array of experimentally determined biophysical properties, including viscosity, aggregation, hydrophobic interaction chromatography, human pharmacokinetic clearance, heparin retention time, and polyspecificity. Further, we investigate the sensitivity of these surface descriptors to methodological nuances, such as the choice of interior dielectric constant, hydrophobicity scales, structure prediction methods, and the impact of conformational sampling. Notably, we observe systematic shifts in the distribution of surface descriptors depending on the structure prediction method used, driving weak correlations of surface descriptors across structure models. Averaging the descriptor values over conformational distributions from molecular dynamics mitigates the systematic shifts and improves the consistency across different structure prediction methods, albeit with inconsistent improvements in correlations with biophysical data. Based on our benchmarking analysis, we propose six in silico developability risk flags and assess their effectiveness in predicting potential developability issues for a set of case study molecules.

B-Cell Epitope Prediction for Antipeptide Paratopes with the HAPTIC2/HEPTAD User Toolkit (HUT)

B-cell epitope prediction is key to developing peptide-based vaccines and immunodiagnostics along with antibodies for prophylactic, therapeutic and/or diagnostic use. This entails estimating paratope binding affinity for variable-length peptidic sequences subject to constraints on both paratope accessibility and antigen conformational flexibility, as described herein for the HAPTIC2/HEPTAD User Toolkit (HUT). HUT comprises the Heuristic Affinity Prediction Tool for Immune Complexes 2 (HAPTIC2), the HAPTIC2-like Epitope Prediction Tool for Antigen with Disulfide (HEPTAD) and the HAPTIC2/HEPTAD Input Preprocessor (HIP). HIP enables tagging of residues (e.g., in hydrophobic blobs, ordered regions and glycosylation motifs) for exclusion from downstream analyses by HAPTIC2 and HEPTAD. HAPTIC2 estimates paratope binding affinity for disulfide-free disordered peptidic antigens (by analogy between flexible-ligand docking and protein folding), from terms attributed to compaction (in view of sequence length, charge and temperature-dependent polyproline-II helical propensity), collapse (disfavored by residue bulkiness) and contact (with glycine and proline regarded as polar residues that hydrogen bond with paratopes). HEPTAD analyzes antigen sequences that each contain two cysteine residues for which the impact of disulfide pairing is estimated as a correction to the free-energy penalty of compaction. All of HUT is freely accessible online ( https://freeshell.de/~badong/hut.htm ).

Statistical Analysis and Tokenization of Epitopes to Construct Artificial Neoepitope Libraries

Epitopes are specific regions on an antigen's surface that the immune system recognizes. Epitopes are usually protein regions on foreign immune-stimulating entities such as viruses and bacteria, and in some cases, endogenous proteins may act as antigens. Identifying epitopes is crucial for accelerating the development of vaccines and immunotherapies. However, mapping epitopes in pathogen proteomes is challenging using conventional methods. Screening artificial neoepitope libraries against antibodies can overcome this issue. Here, we applied conventional sequence analysis and methods inspired in natural language processing to reveal specific sequence patterns in the linear epitopes deposited in the Immune Epitope Database (www.iedb.org) that can serve as building blocks for the design of universal epitope libraries. Our results reveal that amino acid frequency in annotated linear epitopes differs from that in the human proteome. Aromatic residues are overrepresented, while the presence of cysteines is practically null in epitopes. Byte pair encoding tokenization shows high frequencies of tryptophan in tokens of 5, 6, and 7 amino acids, corroborating the findings of the conventional sequence analysis. These results can be applied to reduce the diversity of linear epitope libraries by orders of magnitude.

MILKSHAKE Western blot and Sundae ELISA: We all scream for better antibody validation

Knowing that an antibody's sensitivity and specificity is accurate is crucial for reliable data collection. This certainty is especially difficult to achieve for antibodies (Abs) which bind post-translationally modified proteins. Here we describe two validation methods using surrogate proteins in western blot and ELISA. The first method, which we termed "MILKSHAKE" is a modified maltose binding protein, hence the name, that is enzymatically conjugated to a peptide from the chosen target which is either modified or non-modified at the residue of interest. The surety of the residue's modification status can be used to confirm Ab specificity to the target's post-translational modification (PTM). The second method uses a set of surrogate proteins, which we termed "Sundae". Sundae consists of a set of modified maltose binding proteins with a genetically encoded target sequence, each of which contains a single amino acid substitution at one position of interest. With Sundae, Abs can be evaluated for binding specificities to all twenty amino acids at a single position. Combining MILKSHAKE and Sundae methods, Ab specificity can be determined at a single-residue resolution. These data improve evaluation of commercially available Abs and identify off-target effects for Research-Use-Only and therapeutic Abs.

Experimentally Observed Conformational Changes in Antibodies Due to Binding and Paratope-epitope Asymmetries

Understanding binding related changes in antibody conformations is important for epitope prediction and antibody refinement. The increase of available data in the PDB allowed a more detailed investigation of the conformational landscape for free and bound antibodies. A dataset containing a total of 835 unique PDB entries of antibodies that were crystallized in complex with their antigen and in a free state was constructed. It was examined for binding related conformation changes. We present further evidence supporting the theory of a pre-existing-equilibrium in experimental data. Multiple sequence alignments did not show binding induced tendencies in the solvent accessibility of residues in any specific position. Evaluating the changes in solvent accessibility per residue revealed a certain binding induced increase for several amino acids. Antibody-antigen interaction statistics were established and quantify a significant directional asymmetry between many interacting antibody and antigen residue pairs, especially a richness in tyrosine in the antibody epitope compared to its paratope. This asymmetry could potentially facilitate an increase in the success rate of computationally guided antibody refinement.

SARS-CoV-2 Virus-like Particles with Plasmonic Au Cores and S1-Spike Protein Coronas

The COVID-19 pandemic has stimulated the scientific world to intensify virus-related studies aimed at the development of quick and safe ways of detecting viruses in the human body, studying the virus-antibody and virus-cell interactions, and designing nanocarriers for targeted antiviral therapies. However, research on dangerous viruses can only be performed in certified laboratories that follow strict safety procedures. Thus, developing deactivated virus constructs or safe-to-use virus-like objects, which imitate real viruses and allow performing virus-related studies in any research laboratory, constitutes an important scientific challenge. Such species, called virus-like particles (VLPs), contain instead of capsids with viral DNA/RNA empty or synthetic cores with real virus proteins attached to them. We have developed a method for the preparation of VLPs imitating the virus responsible for the COVID-19 disease: the SARS-CoV-2. The particles have Au cores surrounded by "coronas" of S1 domains of the virus's spike protein. Importantly, they are safe to use and specifically interact with SARS-CoV-2 antibodies. Moreover, Au cores exhibit localized surface plasmon resonance (LSPR), which makes the synthesized VLPs suitable for biosensing applications. During the studies, the effect allowed us to visualize the interaction between the VLPs and the antibodies and identify the characteristic vibrational signals. What is more, additional functionalization of the particles with a fluorescent label revealed their potential in studying specific virus-related interactions. Notably, the universal character of the developed synthesis method makes it potentially applicable for fabricating VLPs imitating other life-threatening viruses.

AREA-AFFINITY: A Web Server for Machine Learning-Based Prediction of Protein-Protein and Antibody-Protein Antigen Binding Affinities

Protein-Protein binding affinity reflects the binding strength between the binding partners. The prediction of protein-protein binding affinity is important for elucidating protein functions and also for designing protein-based therapeutics. The geometric characteristics such as area (both interface and surface areas) in the structure of a protein-protein complex play an important role in determining protein-protein interactions and their binding affinity. Here, we present a free web server for academic use, AREA-AFFINITY, for prediction of protein-protein or antibody-protein antigen binding affinity based on interface and surface areas in the structure of a protein-protein complex. AREA-AFFINITY implements 60 effective area-based protein-protein affinity predictive models and 37 effective area-based models specific for antibody-protein antigen binding affinity prediction developed in our recent studies. These models take into consideration the roles of interface and surface areas in binding affinity by using areas classified according to different amino acid types with different biophysical nature. The models with the best performances integrate machine learning methods such as neural network or random forest. These newly developed models have superior or comparable performance compared to the commonly used existing methods. AREA-AFFINITY is available for free at: https://affinity.cuhk.edu.cn/.

Design of Oscillatory Networks through Post-Translational Control of Network Components

Many essential functions in biological systems, including cell cycle progression and circadian rhythm regulation, are governed by the periodic behaviors of specific molecules. These periodic behaviors arise from the precise arrangement of components in biomolecular networks that generate oscillatory output signals. The dynamic properties of individual components of these networks, such as maturation delays and degradation rates, often play a key role in determining the network's oscillatory behavior. In this study, we explored the post-translational modulation of network components as a means to generate genetic circuits with oscillatory behaviors and perturb the oscillation features. Specifically, we used the NanoDeg platform-A bifunctional molecule consisting of a target-specific nanobody and a degron tag-to control the degradation rates of the circuit's components and predicted the effect of NanoDeg-mediated post-translational depletion of a key circuit component on the behavior of a series of proto-oscillating network topologies. We modeled the behavior of two main classes of oscillators, namely relaxation oscillator topologies (the activator-repressor and the Goodwin oscillator) and ring oscillator topologies (repressilators). We identified two main mechanisms by which non-oscillating networks could be induced to oscillate through post-translational modulation of network components: an increase in the separation of timescales of network components and mitigation of the leaky expression of network components. These results are in agreement with previous findings describing the effect of timescale separation and mitigation of leaky expression on oscillatory behaviors. This work thus validates the use of tools to control protein degradation rates as a strategy to modulate existing oscillatory signals and construct oscillatory networks. In addition, this study provides the design rules to implement such an approach based on the control of protein degradation rates using the NanoDeg platform, which does not require genetic manipulation of the network components and can be adapted to virtually any cellular protein. This work also establishes a framework to explore the use of tools for post-translational perturbations of biomolecular networks and generates desired behaviors of the network output.

Aromatic Residues on the Side Surface of Cry4Ba-Domain II of Bacillus thuringiensis subsp. israelensis Function in Binding to Their Counterpart Residues on the Aedes aegypti Alkaline Phosphatase Receptor

Receptor binding is a prerequisite process to exert the mosquitocidal activity of the Cry4Ba toxin of Bacillus thuringiensis subsp. israelensis. The beta-sheet prism (domain II) and beta-sheet sandwich (domain III) of the Cry4Ba toxin have been implicated in receptor binding, albeit the precise binding mechanisms of these remain unclear. In this work, alanine scanning was used to determine the contribution to receptor binding of some aromatic and hydrophobic residues on the surface of domains II and III that are predicted to be responsible for binding to the Aedes aegypti membrane-bound alkaline phosphatase (Aa-mALP) receptor. Larvicidal activity assays against A. aegypti larvae revealed that aromatic residues (Trp₃₂₇ on the β2 strand, Tyr₃₄₇ on the β3-β4 loop, and Tyr₃₅₉ on the β4 strand) of domain II were important to the toxicity of the Cry4Ba toxin. Quantitative binding assays using enzyme-linked immunosorbent assay (ELISA) showed similar decreasing trends in binding to the Aa-mALP receptor and in toxicity of the Cry4Ba mutants Trp327Ala, Tyr347Ala, and Tyr359Ala, suggesting that a possible function of these surface-exposed aromatic residues is receptor binding. In addition, binding assays of the Cry4Ba toxin to the mutants of the binding residues Gly₅₁₃, Ser₄₉₀, and Phe₄₉₇ of the Aa-mALP receptor supported the binding function of Trp₃₂₇, Tyr₃₄₇, and Tyr₃₅₉ of the Cry4Ba toxin, respectively. Altogether, our results showed for the first time that aromatic residues on a side surface of the Cry4Ba domain II function in receptor binding. This finding provides greater insight into the possible molecular mechanisms of the Cry4Ba toxin.

A window into the human immune system: comprehensive characterization of the complexity of antibody complementary-determining regions in functional antibodies

The human immune system uses antibodies to neutralize foreign antigens. They are composed of heavy and light chains, both with constant and variable regions. The variable region has six hypervariable loops, also known as complementary-determining regions (CDRs) that determine antibody diversity and antigen specificity. Knowledge of their significance, and certain residues present in these areas, is vital for antibody therapeutics development. This study includes an analysis of more than 11,000 human antibody sequences from the International Immunogenetics information system (IMGT). The analysis included parameters such as length distribution, overall amino acid diversity, amino acid frequency per CDR and residue position within antibody chains. Overall, our findings confirm existing knowledge, such as CDRH3's high length diversity and amino acid variability, increased aromatic residue usage, particularly tyrosine, charged and polar residues like aspartic acid, serine, and the flexible residue glycine. Specific residue positions within each CDR influence these occurrences, implying a unique amino acid type distribution pattern. We compared amino acid type usage in CDRs and non-CDR regions, both in globular and transmembrane proteins, which revealed distinguishing features, such as increased frequency of tyrosine, serine, aspartic acid, and arginine. These findings should prove useful for future optimization, improvement of affinity, synthetic antibody library design, or the creation of antibodies de-novo in silico.

Computational Analysis of Antibody Paratopes for Antibody Sequences in Antibody Libraries

To ensure the functionalities of the antibodies in phage-displayed synthetic antibody libraries, we use computational method to evaluate the designs of the antibody libraries. The computational methodologies developed in our lab for designing antibody library provide rich information on the function of the designed antibody sequences-adequate antibody designs for a specific antigen type should have predicted paratopes for the antigen type. This computational assessment of the designed antibody sequences helps eliminate non-functional designs before proceeding to construct the library designs in the wet lab. As such, only reasonable antibody designs are constructed for antibody discoveries.

HDX-MS study on garadacimab binding to activated FXII reveals potential binding interfaces through differential solvent exposure

Hageman factor (FXII) is an essential component in the intrinsic coagulation cascade and a therapeutic target for the prophylactic treatment of hereditary angioedema (HAE). CSL312 (garadacimab) is a novel high-affinity human antibody capable of blocking activated FXII activity that is currently undergoing Phase 3 clinical trials in HAE. Structural studies using hydrogen/deuterium exchange coupled to mass spectrometry revealed evidence of interaction between the antibody and regions surrounding the S1 specificity pocket of FXII, including the 99-loop, 140-loop, 180-loop, and neighboring regions. We propose complementarity-determining regions (CDRs) in heavy-chain CDR2 and CDR3 as potential paratopes on garadacimab, and the 99-loop, 140-loop, 180-loop, and 220-loop as binding sites on the beta chain of activated FXII (β-FXIIa).

AbAgIntPre: A deep learning method for predicting antibody-antigen interactions based on sequence information

Introduction

Antibody-mediated immunity is an essential part of the immune system in vertebrates. The ability to specifically bind to antigens allows antibodies to be widely used in the therapy of cancers and other critical diseases. A key step in antibody therapeutics is the experimental identification of antibody-antigen interactions, which is generally time-consuming, costly, and laborious. Although some computational methods have been proposed to screen potential antibodies, the dependence on 3D structures still limits the application of these methods.

Methods

Here, we developed a deep learning-assisted prediction method (i.e., AbAgIntPre) for fast identification of antibody-antigen interactions that only relies on amino acid sequences. A Siamese-like convolutional neural network architecture was established with the amino acid composition encoding scheme for both antigens and antibodies.

Results and Discussion

The generic model of AbAgIntPre achieved satisfactory performance with the Area Under Curve (AUC) of 0.82 on a high-quality generic independent test dataset. Besides, this approach also showed competitive performance on the more specific SARS-CoV dataset. We expect that AbAgIntPre can serve as an important complement to traditional experimental methods for antibody screening and effectively reduce the workload of antibody design. The web server of AbAgIntPre is freely available at http://www.zzdlab.com/AbAgIntPre.

Development of a Single-Chain Variable Fragment of CR3022 for a Plasmonic-Based Biosensor Targeting the SARS-CoV-2 Spike Protein

Two years after SARS-CoV-2 caused the first case of COVID-19, we are now in the "new normal" period, where people's activity has bounced back, followed by the easing of travel policy restrictions. The lesson learned is that the wide availability of accurate and rapid testing procedures is crucial to overcome possible outbreaks in the future. Therefore, many laboratories worldwide have been racing to develop a new point-of-care diagnostic test. To aid continuous innovation, we developed a plasmonic-based biosensor designed explicitly for portable Surface Plasmon Resonance (SPR). In this study, we designed a single chain variable fragment (scFv) from the CR3022 antibody with a particular linker that inserted a cysteine residue at the second position. It caused the linker to have a strong affinity to the gold surface through thiol-coupling and possibly become a ready-to-use bioreceptor toward a portable SPR gold chip without purification steps. The theoretical affinity of this scFv on spike protein was -64.7 kcal/mol, computed using the Molecular Mechanics Generalized Born Surface Area (MM/GBSA) method from the 100 ns molecular dynamics trajectory. Furthermore, the scFv was produced in Escherichia coli BL21 (DE3) as a soluble protein. The binding activity toward Spike Receptor Binding Domain (RBD) SARS-CoV-2 was confirmed with a spot-test, and the experimental binding free energy of -10.82 kcal/mol was determined using portable SPR spectroscopy. We hope this study will be useful in designing specific and low-cost bioreceptors, particularly early in an outbreak when the information on antibody capture is still limited.

Antibody CDR amino acids underlying the functionality of antibody repertoires in recognizing diverse protein antigens

Antibodies recognize protein antigens with exquisite specificity in a complex aqueous environment, where interfacial waters are an integral part of the antibody–protein complex interfaces. In this work, we elucidate, with computational analyses, the principles governing the antibodies’ specificity and affinity towards their cognate protein antigens in the presence of explicit interfacial waters. Experimentally, in four model antibody–protein complexes, we compared the contributions of the interaction types in antibody–protein antigen complex interfaces with the antibody variants selected from phage-displayed synthetic antibody libraries. Evidently, the specific interactions involving a subset of aromatic CDR (complementarity determining region) residues largely form the predominant determinant underlying the specificity of the antibody–protein complexes in nature. The interfacial direct/water-mediated hydrogen bonds accompanying the CDR aromatic interactions are optimized locally but contribute little in determining the epitope location. The results provide insights into the phenomenon that natural antibodies with limited sequence and structural variations in an antibody repertoire can recognize seemingly unlimited protein antigens. Our work suggests guidelines in designing functional artificial antibody repertoires with practical applications in developing novel antibody-based therapeutics and diagnostics for treating and preventing human diseases.

Simple diagnosis of cancer by detecting CEA and CYFRA 21-1 in saliva using electronic sensors

One way of early diagnosis of cancer is by detecting the biomarkers that get introduced into easily accessible body fluids. We report the development of portable and rapid electronic biosensors for quantitative detection of two secretive cancer biomarkers-Carcinoembryonic antigen (CEA) and Cytokeratin fragment 19 (CYFRA 21-1). The reduced graphene oxide (rGO)/ melamine (MEL)/antibodies/ bovine serum albumin (BSA) based devices were tested for 1 pg/mL to 800 ng/mL of CEA and CYFRA 21-1. The responses of the sensors ranged from 7.14 to 59.1% and from 6.18 to 64% for 1 pg/mL to 800 ng/mL CEA and CYFRA 21-1 respectively. A read-out circuit was assembled to develop a portable prototype which was used to assess the concentrations of the two antigens present in saliva samples of 14 subjects. The prototype could accurately discriminate between 9 oral squamous cell carcinoma patients and 5 healthy controls.

Prediction of the structural interface between fibroblast growth factor23 and Burosumab using alanine scanning and molecular docking

Burosumab, an FGF23 targeting monoclonal antibody, was approved by the FDA in 2018 for use in children and adults with X-linked hypophosphatemia (or XLH). While several clinical studies have demonstrated the long-term safety and efficacy of Burosumab, the molecular basis of FGF23-Burosumab interaction which underpins its mechanism of action remains unknown. In this study, we employed molecular docking combined with alanine scanning of epitope and paratope to predict a model of FGF23-Burosumab interaction. Then, we used the model to understand the species-species cross-reactivity of Burosumab and to reverse engineer mouse FGF23 with 'back to human' mutations to bind Burosumab. Finally, we redesigned the CDRs with two mutations to engineer an affinity enhanced variant of the antibody. Our study provides insights into the FGF23-Burosumab interaction and demonstrates that alanine-scanning coupled with molecular docking can be used to optimize antibody candidates (e.g., structure-guided affinity maturation) for therapeutic use.

Repertoire-scale measures of antigen binding

Antibodies and T cell receptors (TCRs) are the fundamental building blocks of adaptive immunity. Repertoire-scale functionality derives from their epitope-binding properties, just as macroscopic properties like temperature derive from microscopic molecular properties. However, most approaches to repertoire-scale measurement, including sequence diversity and entropy, are not based on antibody or TCR function in this way. Thus, they potentially overlook key features of immunological function. Here we present a framework that describes repertoires in terms of the epitope-binding properties of their constituent antibodies and TCRs, based on analysis of thousands of antibody-antigen and TCR-peptide-major-histocompatibility-complex binding interactions and over 400 high-throughput repertoires. We show that repertoires consist of loose overlapping classes of antibodies and TCRs with similar binding properties. We demonstrate the potential of this framework to distinguish specific responses vs. bystander activation in influenza vaccinees, stratify cytomegalovirus (CMV)-infected cohorts, and identify potential immunological "super-agers." Classes add a valuable dimension to the assessment of immune function.

Antibody-Antigen Binding Interface Analysis in the Big Data Era

Antibodies have become the Swiss Army tool for molecular biology and nanotechnology. Their outstanding ability to specifically recognise molecular antigens allows their use in many different applications from medicine to the industry. Moreover, the improvement of conventional structural biology techniques (e.g., X-ray, NMR) as well as the emergence of new ones (e.g., Cryo-EM), have permitted in the last years a notable increase of resolved antibody-antigen structures. This offers a unique opportunity to perform an exhaustive structural analysis of antibody-antigen interfaces by employing the large amount of data available nowadays. To leverage this factor, different geometric as well as chemical descriptors were evaluated to perform a comprehensive characterization.

Charge-Modulated Accessibility of Tyrosine Residues for Silk-Elastin Copolymer Cross-Linking

The modulation of reaction kinetics with horseradish peroxidase (HRP)-catalyzed cross-linking of proteins remains a useful strategy to modulate hydrogel formation. Here, we demonstrate that the presence of positively charged lysines in silk-elastin-like polymers impacts the thermal transition temperature of these proteins, while the location in the primary sequence modulates the reactivity of the tyrosines. The positively charged lysine side chains decreased π-π interactions among the tyrosines and reduced the rate of formation and number of HRP-mediated dityrosine bonds, dependent on the proximity of the charged group to the tyrosine. The results suggest that the location of repulsive charges can be used to tailor the reaction kinetics for enzymatic cross-linking, providing further control of gelation rates for in situ gel formation and the resulting protein-based gel characteristics.

Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics

Therapeutic monoclonal antibodies and their derivatives are key components of clinical pipelines in the global biopharmaceutical industry. The availability of large datasets of antibody sequences, structures, and biophysical properties is increasingly enabling the development of predictive models and computational tools for the "developability assessment" of antibody drug candidates. Here, we provide an overview of the antibody informatics tools applicable to the prediction of developability issues such as stability, aggregation, immunogenicity, and chemical degradation. We further evaluate the opportunities and challenges of using biopharmaceutical informatics for drug discovery and optimization. Finally, we discuss the potential of developability guidelines based on in silico metrics that can be used for the assessment of antibody stability and manufacturability.

Assessment of Therapeutic Antibody Developability by Combinations of In Vitro and In Silico Methods

Although antibodies have become the fastest-growing class of therapeutics on the market, it is still challenging to develop them for therapeutic applications, which often require these molecules to withstand stresses that are not present in vivo. We define developability as the likelihood of an antibody candidate with suitable functionality to be developed into a manufacturable, stable, safe, and effective drug that can be formulated to high concentrations while retaining a long shelf life. The implementation of reliable developability assessments from the early stages of antibody discovery enables flagging and deselection of potentially problematic candidates, while focussing available resources on the development of the most promising ones. Currently, however, thorough developability assessment requires multiple in vitro assays, which makes it labor intensive and time consuming to implement at early stages. Furthermore, accurate in vitro analysis at the early stage is compromised by the high number of potential candidates that are often prepared at low quantities and purity. Recent improvements in the performance of computational predictors of developability potential are beginning to change this scenario. Many computational methods only require the knowledge of the amino acid sequences and can be used to identify possible developability issues or to rank available candidates according to a range of biophysical properties. Here, we describe how the implementation of in silico tools into antibody discovery pipelines is increasingly offering time- and cost-effective alternatives to in vitro experimental screening, thus streamlining the drug development process. We discuss in particular the biophysical and biochemical properties that underpin developability potential and their trade-offs, review various in vitro assays to measure such properties or parameters that are predictive of developability, and give an overview of the growing number of in silico tools available to predict properties important for antibody development, including the CamSol method developed in our laboratory.

Missense variants in the TNFA epitopes and their effects on interaction with therapeutic antibodies-in silico analysis

BACKGROUND

Tumor necrosis factor alpha (TNFA) is an important cytokine that influences multiple biological processes. It is one of the key mediators of acute and chronic systemic inflammatory reactions and plays a central role in several autoimmune diseases. A number of approved monoclonal antibodies (mAbs) are widely used to subside these autoimmune diseases. However, there is a high rate of non-responsiveness to treatments with these mAbs. Therefore, it is important to be able to predict responses of the patients in an individualistic manner to these therapeutic antibodies before administration. In the present study, we used in silico tools to explore the effects of missense variants in the respective epitopes of four therapeutic anti-TNFA mAbs-adalimumab (ADA), certolizumab pegol (CZP), golimumab (GLM), and infliximab (IFX)-on their interactions with TNFA.

RESULTS

The binding affinities of CZP and ADA to corresponding epitopes appear to be reduced by four (TNFAR131Q, TNFAE135G, TNFAR138Q, and TNFAR138W) and two (TNFAG66C and TNFAG66S) variants, respectively. The binding of GLM and IFX appears to be affected by TNFAR141S and TNFAR138W, respectively. TNFAG66C and TNFAG66S may be associated with autoimmune diseases, whereas TNFAE135G, TNFAR138W, and TNFAR141S may be pathogenic per se.

CONCLUSION

These variants may contribute to the observed inter-individual variability in response to anti-TNFA mAbs treatments and be used as markers to predict responses, and thus optimize therapeutic benefits to the patients.

Leveraging Sequential and Spatial Neighbors Information by Using CNNs Linked With GCNs for Paratope Prediction

Antibodies consisting of variable and constant regions, are a special type of proteins playing a vital role in immune system of the vertebrate. They have the remarkable ability to bind a large range of diverse antigens with extraordinary affinity and specificity. This malleability of binding makes antibodies an important class of biological drugs and biomarkers. In this article, we propose a method to identify which amino acid residues of an antibody directly interact with its associated antigen based on the features from sequence and structure. Our algorithm uses convolution neural networks (CNNs) linked with graph convolution networks (GCNs) to make use of information from both sequential and spatial neighbors to understand more about the local environment of target amino acid residue. Furthermore, we process the antigen partner of an antibody by employing an attention layer. Our method improves on the state-of-the-art methodology.

Comparative Analysis of Antibodies and Heavily Glycosylated Macromolecular Immune Complexes by Size-Exclusion Chromatography Multi-Angle Light Scattering, Native Charge Detection Mass Spectrometry, and Mass Photometry

Qualitative and quantitative mass analysis of antibodies and related macromolecular immune complexes is a prerequisite for determining their identity, binding partners, stoichiometries, and affinities. A plethora of bioanalytical technologies exist to determine such characteristics, typically based on size, interaction with functionalized surfaces, light scattering, or direct mass measurements. While these methods are highly complementary, they also exhibit unique strengths and weaknesses. Here, we benchmark mass photometry (MP), a recently introduced technology for mass measurement, against native mass spectrometry (MS) and size exclusion chromatography multi-angle light scattering (SEC-MALS). We examine samples of variable complexity, namely, IgG4Δhinge dimerizing half-bodies, IgG-RGY hexamers, heterogeneously glycosylated IgG:sEGFR antibody-antigen complexes, and finally megadalton assemblies involved in complement activation. We thereby assess the ability to determine (1) binding affinities and stoichiometries, (2) accurate masses, for extensively glycosylated species, and (3) assembly pathways of large heterogeneous immune complexes. We find that MP provides a sensitive approach for characterizing antibodies and stable assemblies, with dissociation correction enabling us to expand the measurable affinity range. In terms of mass resolution and accuracy, native MS performs the best but is occasionally hampered by artifacts induced by electrospray ionization, and its resolving power diminishes when analyzing extensively glycosylated proteins. In the latter cases, MP performs well, but single-particle charge detection MS can also be useful in this respect, measuring masses of heterogeneous assemblies even more accurately. Both methods perform well compared to SEC-MALS, still being the most established method in biopharma. Together, our data highlight the complementarity of these approaches, each having its unique strengths and weaknesses.

Key role of a structural water molecule for the specificity of 14F7-An antitumor antibody targeting the NeuGc GM3 ganglioside

Tumor-associated glycolipids such as NeuGc GM3 are auspicious molecular targets in antineoplastic therapies and vaccine strategies. 14F7 is a monoclonal IgG1 with high clinical potential in cancer immunotherapy as it displays extraordinary specificity for NeuGc GM3, while it does not recognize the very similar, ubiquitous NeuAc GM3. Here we present the 2.3 Å crystal structure of the 14F7 antigen-binding domain (14F7 scFv) in complex with the NeuGc GM3 trisaccharide. Modeling analysis and previous mutagenesis data suggest that 14F7 may also bind to an alternative NeuGc GM3 conformation, not observed in the crystal structure. The most intriguing finding, however, was that a water molecule centrally placed in the complementarity-determining region directly mediates the specificity of 14F7 to NeuGc GM3. This has profound impact on the complexity of engineering in the binding site and provides an excellent example of the importance in understanding the water structure in antibody-antigen interactions.

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.

A review on the effect of prolyl isomerization on immune response aberration and hypersensitivity reactions: A unifying hypothesis

Little is known about the causes and mechanisms of ectopic immune responses, including different types of hypersensitivity, superantigens, and cytokine storms. Two of the most questionable phenomena observed in immunology are why the intensity and extent of immune responses to different antigens are different, and why some self-antigens are attacked as foreign. The secondary structure of the peptides involved in the immune system, such as the epitope-paratope interfaces plays a pivotal role in the resulting immune responses. Prolyl cis/trans isomerization plays a fundamental role in the form of the secondary structure and the folding of proteins. This review covers some of the emerging evidence indicating the impact of prolyl isomerization on protein conformation, aberration of immune responses, and the development of hypersensitivity reactions.

Secondary Structure and Conformational Stability of the Antigen Residues Making Contact with Antibodies

Antibodies are crucial biomolecules that bring high therapeutic efficacy in medicine and accurate molecular detection in diagnosis. Many studies have been devoted to analyzing the antigen-antibody interaction from the importance of understanding the antibody recognition mechanism. However, most of the previous studies examined the characteristic of the antibody for interaction. It is also informative to clarify the significant antigen residues contributing to the binding. To characterize the molecular interaction of antigens, we computationally analyzed 350 antigen-antibody complex structures by molecular mechanics (MM) calculations and molecular dynamics (MD) simulations. Based on the MM calculations, the antigen residues contributing to the binding were extracted from all the 350 complexes. The extracted residues are located at the antigen-antibody interface and are responsible for making contact with the antibody. The appearances of the charged polar residues, Asp, Glu, Arg, and Lys, were noticeably large. In contrast, the populations of the hydrophobic residues, Leu, Val, and Ala, were relatively low. The appearance frequencies of the other amino acid residues were almost close to the abundance of general proteins of eukaryotes. The binding score indicated that the hydrophilic interaction was dominant at the antigen-antibody contact instead of the hydrophobic one. The positively charged residues, Arg and Lys, remarkably contributed to the binding compared to the negatively charged ones, Asp and Glu. Considerable contributions were also observed for the noncharged polar residues, Asn and Gln. The analysis of the secondary structures of the extracted antigen residues suggested that there was no marked difference in recognition by antibodies among helix, sheet, turn, and coil. A long helix of the antigen sometimes made contact with antibody complementarity-determining regions, and a large sheet also frequently covered the antibody heavy and light chains. The turn structure was the most popularly observed at the contact with antibody among 350 complexes. Three typical complexes were picked up for each of the four secondary structures. MD simulations were performed to examine the stability of the interfacial structures of the antigens for these 12 complex models. The alterations of secondary structures were monitored through the simulations. The structural fluctuations of the contact residues were low compared with the other domains of antigen molecules. No drastic conversion was observed for every model during the 100 ns simulation. The motions of the interfacial antigen residues were small compared to the other residues on the protein surface. Therefore, diverse molecular conformations are possible for antibody recognition as long as the target areas are polar, nonflexible, and protruding on the protein surface.

Systematic Engineering of Optimized Autonomous Heavy-Chain Variable Domains

Autonomous heavy-chain variable (V_H) domains are the smallest functional antibody fragments, and they possess unique features, including small size and convex paratopes, which provide enhanced targeting of concave epitopes that are difficult to access with larger conventional antibodies. However, human V_H domains have evolved to fold and function with a light chain partner, and alone, they typically suffer from low stability and high aggregation propensity. Development of autonomous human V_H domains, in which aggregation propensity is reduced without compromising antigen recognition, has proven challenging. Here, we used an autonomous human V_H domain as a scaffold to construct phage-displayed synthetic libraries in which aspartate was systematically incorporated at different paratope positions. In selections, the library yielded many anti-EphA1 receptor V_H domains, which were characterized in detail. Structural analyses of a parental anti-EphA1 V_H domain and an improved variant provided insights into the effects of aspartate and other substitutions on preventing aggregation while retaining function. Our naïve libraries and in vitro selection procedures offer a systematic approach to generating highly functional autonomous human V_H domains that resist aggregation and could be used for basic research and biomedical applications.

Recent advances in nanomaterials-based electrochemical immunosensors and aptasensors for HER2 assessment in breast cancer

Human epidermal growth factor receptor 2 (HER2) is one of the key molecular targets in breast cancer pathogenesis. Overexpression and/or amplification of HER2 in approximately 15-20% of breast cancer patients is associated with high mortality and poor prognosis. Accumulating evidence shows that accurate and sensitive detection of HER2 improves the survival outcomes for HER2-positive breast cancer patients from targeted therapies. The current methods of clinical determination of HER2 expression levels are based on slide-based assays that rely on invasively collected primary tumours. Alternatively, ELISA-based detection of the shredded HER2 extracellular domain (HER2-ECD) of has been suggested as a surrogate method for monitoring disease progress and treatment response in breast cancer patients. In the past decade, biosensors have emerged as an alternative modality for the detection of circulating HER2-ECD in human serum samples. In particular, electrochemical biosensors based on nanomaterials and antibodies and aptamers have been increasingly developed as promising tools for rapid, sensitive, and cost-effective detection of HER2-ECD. These biosensors harness the high affinity and specificity of antibodies and aptamers, and unique conductive properties, biocompatibility, large surface area, and chemical stability of nanomaterials for selective and sensitive assessment of the HER2. This review provides an overview of the recent advances in the application of nanomaterials-based immunosensors and aptasensors for detection of circulating HER2-ECD. In particular, various electrochemical techniques, detection approaches, and nanomaterials are discussed. Further, analytical figures of merit of various HER2 immunosensors and aptasensors are compared. Finally, possible challenges and potential opportunities for biosensor-based detection of HER2-ECD are discussed.

Polyclonal antibody responses to HIV Env immunogens resolved using cryoEM

Engineered ectodomain trimer immunogens based on BG505 envelope glycoprotein are widely utilized as components of HIV vaccine development platforms. In this study, we used rhesus macaques to evaluate the immunogenicity of several stabilized BG505 SOSIP constructs both as free trimers and presented on a nanoparticle. We applied a cryoEM-based method for high-resolution mapping of polyclonal antibody responses elicited in immunized animals (cryoEMPEM). Mutational analysis coupled with neutralization assays were used to probe the neutralization potential at each epitope. We demonstrate that cryoEMPEM data can be used for rapid, high-resolution analysis of polyclonal antibody responses without the need for monoclonal antibody isolation. This approach allowed to resolve structurally distinct classes of antibodies that bind overlapping sites. In addition to comprehensive mapping of commonly targeted neutralizing and non-neutralizing epitopes in BG505 SOSIP immunogens, our analysis revealed that epitopes comprising engineered stabilizing mutations and of partially occupied glycosylation sites can be immunogenic.

Eradicating mesothelin-positive human gastric and pancreatic tumors in xenograft models with optimized anti-mesothelin antibody-drug conjugates from synthetic antibody libraries

Mesothelin (MSLN) is an attractive candidate of targeted therapy for several cancers, and hence there are increasing needs to develop MSLN-targeting strategies for cancer therapeutics. Antibody–drug conjugates (ADCs) targeting MSLN have been demonstrated to be a viable strategy in treating MSLN-positive cancers. However, developing antibodies as targeting modules in ADCs for toxic payload delivery to the tumor site but not to normal tissues is not a straightforward task with many potential hurdles. In this work, we established a high throughput engineering platform to develop and optimize anti-MSLN ADCs by characterizing more than 300 scFv CDR-variants and more than 50 IgG CDR-variants of a parent anti-MSLN antibody as candidates for ADCs. The results indicate that only a small portion of the complementarity determining region (CDR) residues are indispensable in the MSLN-specific targeting. Also, the enhancement of the hydrophilicity of the rest of the CDR residues could drastically increase the overall solubility of the optimized anti-MSLN antibodies, and thus substantially improve the efficacies of the ADCs in treating human gastric and pancreatic tumor xenograft models in mice. We demonstrated that the in vivo treatments with the optimized ADCs resulted in almost complete eradication of the xenograft tumors at the treatment endpoints, without detectable off-target toxicity because of the ADCs’ high specificity targeting the cell surface tumor-associated MSLN. The technological platform can be applied to optimize the antibody sequences for more effective targeting modules of ADCs, even when the candidate antibodies are not necessarily feasible for the ADC development due to the antibodies’ inferior solubility or affinity/specificity to the target antigen.

Analysis of binding residues in monoclonal antibody with high affinity for the head domain of the rat P2X4 receptor

P2X4 receptor is known to be involved in neuropathic pain. In order to detect the expression of P2X4 receptor on microglia at the time of onset of neuropathic pain, one approach consists on the preparation of the monoclonal antibodies with both selective binding and high affinity. We have recently established a monoclonal antibody (named 12-10H) which had high affinity to rat P2X4 receptor expressed in 1321N1 cells. The dissociation constants of the complex between the monoclonal antibodies obtained so far and the head domain (HD) in the rat P2X4 receptor were in the nanomolar range. To improve the affinity by rational mutations, we need to know the precious location of the binding site in these monoclonal antibodies. Here, we have analysed and identified the binding residues in the monoclonal antibody (12-10H) with high affinity for the HD of the rat P2X4 receptor by site-directed mutagenesis.

Hydroxyl radical induced structural perturbations make insulin highly immunogenic and generate an auto-immune response in type 2 diabetes mellitus

Reactive oxygen species (ROS) cause oxidative damage to proteins and generate deleterious by-products which induce a breakdown of immune tolerance and produce antibodies against host macromolecules with implication in human diseases. This study characterizes the hydroxyl radical (OH) modifications of insulin, evaluates its cytotoxicity and immunogenicity, and probes its role in type 2 diabetes (T2DM) autoimmunity. The results demonstrate susceptibility of insulin to modifications induced by OH, causing exposure of its chromophoric aromatic amino acid residues, quenching of tyrosine fluorescence intensity, loss of α-helix and gain in β content. Modification causes re-arrangement of native interactions of the aromatic residues in insulin. It enhanced the carbonyl content in insulin, exposed its hydrophobic patches and generated non-fibrillar, amorphous type of aggregates that are cytotoxic in nature. Native insulin induced low titre antibodies in immunized rabbits, whereas OH modified insulin generated a strong immune response. Competitive ELISA studies showed high specificity of antibodies generated against OH modified insulin towards the modified protein. Cross reaction studies showed the presence of common antigenic determinants on various oxidised proteins. Since T2DM patients show increased ROS production, oxidation of insulin is expected to occur, which might amplify autoimmune reactions against insulin. True to the assumption, direct binding ELISA showed the presence of anti-OH insulin circulating antibodies in T2DM patients which are specific for the oxidized insulin. In conclusion, insulin loses structural integrity to OH, forms cytotoxic amorphous aggregates, turns highly immunogenic and elicits humoral response in T2DM patients.

Biased anti-idiotype response in rabbits leads to high-affinity monoclonal antibodies to biologics

Antibody formation to human(ized) therapeutic antibodies in humans is highly skewed toward anti-idiotype responses, probably because the idiotype is the only 'foreign' part of the antibody molecule. Here, we analyzed antibody responses to F(ab')2 fragments of a panel of 17 human(ized) therapeutic antibodies in rabbits. Homology between the rabbit germline and the human(ized) antibodies is moderate not only for the variable domains (both the complementarity-determining regions and the framework regions), but also for the constant domains (66% or less). Nevertheless, we observed a highly skewed anti-idiotype response in all cases, with up to >90% of the antibodies directed toward the idiotype. These results indicate that the idiotype may be inherently immunodominant. We used these biased responses to raise monoclonal rabbit anti-idiotype antibodies against secukinumab, ustekinumab, reslizumab, mepolizumab, palivizumab, and dupilumab and demonstrate the potential to develop sensitive pharmacokinetic assays with these antibodies.

Magnetic-molecularly imprinted polymers in electrochemical sensors and biosensors

Magnetic particles, as well as molecularly imprinted polymers, have revolutionized separation and bioanalytical methodologies in the 1980s due to their wide range of applications. Today, biologically modified magnetic particles are used in many scientific and technological applications and are integrated in more than 50,000 diagnostic instruments for the detection of a huge range of analytes. However, the main drawback of this material is their stability and high cost. In this work, we review recent advances in the synthesis and characterization of hybrid molecularly imprinted polymers with magnetic properties, as a cheaper and robust alternative for the well-known biologically modified magnetic particles. The main advantages of these materials are, besides the magnetic properties, the possibility to be stored at room temperature without any loss in the activity. Among all the applications, this work reviews the direct detection of electroactive analytes based on the preconcentration by using magnetic-MIP integrated on magneto-actuated electrodes, including food safety, environmental monitoring, and clinical and pharmaceutical analysis. The main features of these electrochemical sensors, including their analytical performance, are summarized. This simple and rapid method will open the way to incorporate this material in different magneto-actuated devices with no need for extensive sample pretreatment and sophisticated instruments.

Molecular recognition of structurally disordered Pro/Ala-rich sequences (PAS) by antibodies involves an Ala residue at the hot spot of the epitope

Pro/Ala-rich sequences (PAS) are polypeptides that were developed as a biological alternative to poly-ethylene glycol (PEG) to generate biopharmaceuticals with extended plasma half-life. Like PEG, PAS polypeptides are conformationally disordered and show high solubility in water. Devoid of any charged or prominent hydrophobic side chains, these biosynthetic polymers represent an extreme case of intrinsically disordered proteins. Despite lack of immunogenicity of PAS tags in numerous animal studies we now succeeded in generating monoclonal antibodies (MAbs) against three different PAS versions. To this end, mice were immunized with a PAS#1, P/A#1 or APSA 40mer peptide conjugated to keyhole limpet hemocyanin as highly immunogenic carrier protein. In each case, one MAb with high binding activity and specificity towards a particular PAS motif was obtained. The apparent affinity was strongly dependent on the avidity effect and most pronounced for the bivalent MAb when interacting with a long PAS repeat. X-ray structural analysis of four representative anti-PAS Fab fragments in complex with their cognate PAS epitope peptides revealed interactions dominated by hydrogen bond networks involving the peptide backbone as well as multiple Van der Waals contacts arising from intimate shape complementarity. Surprisingly, Ala, the L-amino acid with the smallest side chain, emerged as a crucial feature for epitope recognition, contributing specific contacts at the center of the paratope in several anti-PAS complexes. Apart from these insights into how antibodies can recognize feature-less peptides without secondary structure, the MAbs characterized in this study offer valuable reagents for the preclinical and clinical development of PASylated biologics.

Gold nanoparticles-mediated fluorescent chemical nose sensor for pathogenic diagnosis and phenotype

Pathogens are one of the important factors affecting national economic construction. An ideal detection system for pathogen control with excellent sensitivity, high specificity, and time-saving is needed. Here, we reported a method for bacterial detection using gold nanoparticles-mediated fluorescent "chemical nose" sensors (GFCEs). The technique consists of gold nanoparticles-coated magnetic particle using benzaldehyde, octyl aldehyde, and pyrimidine-4-formaldehyde modified, respectively. And these positively charged nanocompound interacting with three different fluorescent proteins (FPs) to form three kinds of GFCEs, respectively, named GFCE1, GFCE2, and GFCE3. Upon binding with pathogenic cells, functionalized gold nanoparticles could identify patches on hydrophobic/functional surfaces of microorganisms, and self-assemble with living bacteria by complementary electrostatic interactions. The binding ability between GFCEs and bacteria determines the change of fluorescence response of three FPs from GFCEs. These feature fluorescent level are pathogen-specific, highly repeatable, and can be analyzed by Linear Discriminant Analysis (LDA). The combination of GFCE1 and GFCE2 has the best performance when detecting pathogens with concentrations of 10⁶  cfu mL^-1 . The first discriminant within 15 minutes is 93.8%, which could be used for subsequent identification of unknown samples. The commonly applicable system provides a simple way for the rapid bacterial detection without preprocessing procedures.

Nonspecific Binding-Fundamental Concepts and Consequences for Biosensing Applications

Nature achieves differentiation of specific and nonspecific binding in molecular interactions through precise control of biomolecules in space and time. Artificial systems such as biosensors that rely on distinguishing specific molecular binding events in a sea of nonspecific interactions have struggled to overcome this issue. Despite the numerous technological advancements in biosensor technologies, nonspecific binding has remained a critical bottleneck due to the lack of a fundamental understanding of the phenomenon. To date, the identity, cause, and influence of nonspecific binding remain topics of debate within the scientific community. In this review, we discuss the evolution of the concept of nonspecific binding over the past five decades based upon the thermodynamic, intermolecular, and structural perspectives to provide classification frameworks for biomolecular interactions. Further, we introduce various theoretical models that predict the expected behavior of biosensors in physiologically relevant environments to calculate the theoretical detection limit and to optimize sensor performance. We conclude by discussing existing practical approaches to tackle the nonspecific binding challenge in vitro for biosensing platforms and how we can both address and harness nonspecific interactions for in vivo systems.