Antigen-antibody Reaction Research Articles

A Comparison of Antibody-Antigen Complex Sequence-to-Structure Prediction Methods and their Systematic Biases.

The ability to accurately predict antibody-antigen complex structures from their sequences could greatly advance our understanding of the immune system and would aid in the development of novel antibody therapeutics. There have been considerable recent advancements in predicting protein-protein interactions (PPIs) fueled by progress in machine learning (ML). To understand the current state of the field, we compare six representative methods for predicting antibody-antigen complexes from sequence, including two deep learning approaches trained to predict PPIs in general (AlphaFold-Multimer, RoseTTAFold), two composite methods that initially predict antibody and antigen structures separately and dock them (using antibody-mode ClusPro), local refinement in Rosetta (SnugDock) of globally docked poses from ClusPro, and a pipeline combining homology modeling with rigid-body docking informed by ML-based epitope and paratope prediction (AbAdapt). We find that AlphaFold-Multimer outperformed other methods, although the absolute performance leaves considerable room for improvement. AlphaFold-Multimer models of lower-quality display significant structural biases at the level of tertiary motifs (TERMs) towards having fewer structural matches in non-antibody containing structures from the Protein Data Bank (PDB). Specifically, better models exhibit more common PDB-like TERMs at the antibody-antigen interface than worse ones. Importantly, the clear relationship between performance and the commonness of interfacial TERMs suggests that scarcity of interfacial geometry data in the structural database may currently limit application of machine learning to the prediction of antibody-antigen interactions.

Functional surface expression of immunoglobulin cleavage systems in a candidate Mycoplasma vaccine chassis

The Mycoplasma Immunoglobulin Binding/Protease (MIB-MIP) system is a candidate ‘virulence factor present in multiple pathogenic species of the Mollicutes, including the fast-growing species Mycoplasma feriruminatoris. The MIB-MIP system cleaves the heavy chain of host immunoglobulins, hence affecting antigen-antibody interactions and potentially facilitating immune evasion. In this work, using -omics technologies and 5’RACE, we show that the four copies of the M. feriruminatoris MIB-MIP system have different expression levels and are transcribed as operons controlled by four different promoters. Individual MIB-MIP gene pairs of M. feriruminatoris and other Mollicutes were introduced in an engineered M. feriruminatoris strain devoid of MIB-MIP genes and were tested for their functionality using newly developed oriC-based plasmids. The two proteins are functionally expressed at the surface of M. feriruminatoris, which confirms the possibility to display large membrane-associated proteins in this bacterium. However, functional expression of heterologous MIB-MIP systems introduced in this engineered strain from phylogenetically distant porcine Mollicutes like Mesomycoplasma hyorhinis or Mesomycoplasma hyopneumoniae could not be achieved. Finally, since M. feriruminatoris is a candidate for biomedical applications such as drug delivery, we confirmed its safety in vivo in domestic goats, which are the closest livestock relatives to its native host the Alpine ibex.

Accurate prediction of CDR-H3 loop structures of antibodies with deep learning

Accurate prediction of the structurally diverse complementarity determining region heavy chain 3 (CDR-H3) loop structure remains a primary and long-standing challenge for antibody modeling. Here, we present the H3-OPT toolkit for predicting the 3D structures of monoclonal antibodies and nanobodies. H3-OPT combines the strengths of AlphaFold2 with a pre-trained protein language model and provides a 2.24 Å average RMSDCα between predicted and experimentally determined CDR-H3 loops, thus outperforming other current computational methods in our non-redundant high-quality dataset. The model was validated by experimentally solving three structures of anti-VEGF nanobodies predicted by H3-OPT. We examined the potential applications of H3-OPT through analyzing antibody surface properties and antibody–antigen interactions. This structural prediction tool can be used to optimize antibody–antigen binding and engineer therapeutic antibodies with biophysical properties for specialized drug administration route.

Accurate prediction of CDR-H3 loop structures of antibodies with deep learning.

Accurate prediction of the structurally diverse complementarity determining region heavy chain 3 (CDR-H3) loop structure remains a primary and long-standing challenge for antibody modeling. Here, we present the H3-OPT toolkit for predicting the 3D structures of monoclonal antibodies and nanobodies. H3-OPT combines the strengths of AlphaFold2 with a pre-trained protein language model and provides a 2.24 Å average RMSDCα between predicted and experimentally determined CDR-H3 loops, thus outperforming other current computational methods in our non-redundant high-quality dataset. The model was validated by experimentally solving three structures of anti-VEGF nanobodies predicted by H3-OPT. We examined the potential applications of H3-OPT through analyzing antibody surface properties and antibody-antigen interactions. This structural prediction tool can be used to optimize antibody-antigen binding and engineer therapeutic antibodies with biophysical properties for specialized drug administration route.

Macrocarpal B blocks the binding between the phospholipase A2 receptor and its antibodies

The pathogenic role of anti-phospholipase A2 receptor (PLA2R) antibodies in primary membranous nephropathy (MN) has been well-established. This study aimed to identify potential small-molecule inhibitors against the PLA2R-antibody interaction, offering potential therapeutic benefits. A comprehensive screening of over 4000 small-molecule compounds was conducted by ELISA to assess their inhibitory effects on the binding between the immobilized full-length extracellular PLA2R and its antibodies. The affinity of anti-PLA2R IgG from MN patients and the inhibitory efficacy of each compound were evaluated via surface plasmon resonance (SPR). Human podocyte injuries were analyzed using CCK-8 assay, wound healing assay, western blot analysis, and immunofluorescence, after exposure to MN plasma +/- blocking compound. Fifteen compounds were identified as potential inhibitors, demonstrating inhibition rates >20 % for the PLA2R-antibody interaction. Anti-PLA2R IgG exhibited a consistent affinity among patients (KD = 10−8 M). Macrocarpal B emerged as the most potent inhibitor, reducing the antigen–antibody interaction by nearly 30 % in a dose-dependent manner, comparable to the performance of the 31-mer peptide from the CysR domain. Macrocarpal B bound to the immobilized PLA2R with an affinity of 1.47 × 10−6 M, while showing no binding to anti-PLA2R IgG. Human podocytes exposed to MN plasma showed decreased podocin expression, impaired migration function, and reduced cell viability. Macrocarpal B inhibited the binding of anti-PLA2R IgG to podocytes and reduced the cellular injuries.

Tetrahedral DNA-linked aptamer-antibody-based sandwich-type electrochemical sensor with Ag@Au core-shell nanoparticles as a signal amplifier for highly sensitive detection of α-fetoprotein.

The conventional electrochemical detection strategy for alpha-fetoprotein (AFP) is limited by the antigen-antibody (Ag-Ab) reactions and suffers from low sensitivity and poor reproducibility due to the inconsistency of Ab-modified electrodes. Herein, we designed and explored a sandwich-type electrochemical sensor for highly sensitive detection of AFP based on aptamer (Apt)-AFP-Ab interaction mode with silver@gold (Ag@Au) core-shell nanoparticles (NPs) as a signal amplifier. AuNPs were electrodeposited onto MXene (Ti3C2TX)-modified glassy carbon electrode (GCE) to get AuNPs/MXene/GCE and further used as the signal amplification substrate. The tetrahedral DNA-linked AFP aptamers were immobilized onto AuNPs/MXene/GCE surface via Au-S bonds and used as the sensing and recognition platform for AFP capturing. Ag@AuNPs with core-shell structures were synthesized, characterized, and bound with Ab as detection elements by catalyzing H2O2 reduction. In thepresence of AFP, a stable Apt-AFP-Ab sandwich structure was formed owing to the high affinities of aptamer and Ab toward the target AFP. The catalytic current produced by H2O2 reduction increased linearly with thelogarithm ofAFP concentration from 5 × 10-4 ng/mL to 1 × 105 ng/mL, accompanied by a low detection limit (1.6 × 10-4 ng/mL). Moreover, the novel sandwich-type electrochemical sensor shows high sensitivity, outstanding selectivity, and promising performance in the analysis of actual samples, displaying a broad application prospect in bioanalysis.

The treatment strategies of autoimmune hemolytic anemia

Autoimmune hemolytic anemia (AIHA) is characterized by the accelerated destruction of erythrocytes due to the presence of antibodies and/or complement that bind to antigens on erythrocytes. It can be subdivided into warm, cold or mixed AIHA based on the type of autoantibody and the optimal temperature of antigen-antibody reaction. Glucocorticoid with or without rituximab is the first-line treatment of warm AIHA (wAIHA), and splenectomy was once the preferred second-line treatment for relapsed or refractory wAIHA. However, due to the various complications of splenectomy, rituximab has gradually become the preferred treatment for patients who have failed glucocorticoid therapy. Other available treatments including immunosuppressants and plasma exchange can be chosen. Rituximab with or without bendamustine is generally taken as the first-line regimen for cold autoimmune hemolytic anemia (cAIHA), while glucocorticoid and splenectomy are ineffective. Sutimlimab, a kind of complement inhibitor, has been approved for the treatment of cold agglutinin disease (CAD). In recent years, many new drugs have emerged as treatment options for AIHA. Emerging therapies, including B-cell-directed therapies, plasma cell-directed therapies, complement inhibitors, and phagocytosis inhibition, provide a new perspective for AIHA therapy, showing great potential for clinical applications.

Electrochemical and Plasmonic Detection of Myocardial Infarction Using Microfluidic Biochip Incorporated with Mesoporous Nanoscaffolds.

This paper reports a microfluidic device for the electrochemical and plasmonic detection of cardiac myoglobin (cMb) and cardiac troponin I (cTnI) with noticeable limits of detection (LoD) as low as a few picograms per milliliter (pg/mL) ranges, achieved in a short detection time. The device features two working electrodes, each with a mesoporous Ni3V2O8 nanoscaffold grafted with reduced graphene oxide (rGO) that improves the interaction of diffusing analyte molecules with the sensing surface by providing a high surface area and reaction kinetics. Electrochemical studies reveal sensitivities as high as 9.68 μA ng/mL and a LoD of 2.0 pg/mL for cTnI, and 8.98 μA ng/mL and 4.7 pg/mL for cMb. Additionally, the surface plasmon resonance (SPR) studies demonstrate a low-level LoD of 8.8 pg/mL for cMb and 7.3 pg/mL for cTnI. The dual-modality sensor enables dynamic tracking of kinetic antigen-antibody interactions during sensing, self-verification through providing signals of two modes, and reduced false readout. This study demonstrates the complementary nature of the electrochemical and SPR modes in biosensing, with the electrochemical mode being highly sensitive and the SPR mode providing superior tracking of molecular recognition behaviors. The presented sensor represents a significant innovation in cardiovascular disease management and can be applied to monitor other clinically important biomolecules.

Ultrasensitive detection of interleukin-6 enabled by three-dimensional spatial amplification

Accurately measuring interleukin-6 (IL-6) levels is crucial in both clinical medicine and research due to its role in various physiological and pathological processes. We present proof-of-concept of a novel three-dimensional (3D) spatial amplification method for developing highly sensitive and miniaturized IL-6 biosensors. We designed an IL-6 immunosensor platform utilizing a unique 3D microfluidic structure fabricated with femtosecond-pulsed laser processing. This design features a porous transducer element with over 4500 microcavities, thereby increasing the reaction area by 13-fold compared to traditional 2D designs while maintaining transparency. By combining this 3D structure with a competitive antigen-antibody reaction, the sensor achieved an exceptional limit of detection of 0.27 pg/mL for IL-6 in human blood samples. Additionally, we demonstrated effective control of liquid flow within the porous element using a reduction of pressure and centrifugation speeds. This 3D spatial amplification method offers a promising approach for developing highly sensitive and compact IL-6 biosensors. Such miniaturized sensors hold potential for point-of-care testing devices, enabling convenient and timely IL-6 analysis.

Aspergillus oryzae as a host for SARS-CoV-2 RBD and NTD expression

The COVID-19 pandemic has increased demand for effective diagnostics, and extensive research has been conducted on the N-terminal domain (NTD) and the receptor-binding domain (RBD) of the SARS-CoV-2 spike glycoprotein, which are critical for viral binding. This study focuses on the expression of NTD and RBD in pyrG auxotrophic Aspergillus oryzae for the first time. Recombinant NTD and RBD were expressed as glucoamylase-fusion proteins and purified using metal affinity chromatography. Size-exclusion chromatography was used to confirm the correct folding and purity of the recombinant proteins. Employing an enzyme-linked immunosorbent assay, the binding ability of the fusion proteins to human anti-IgG antibodies in serum samples was evaluated. The results indicated a significant and concentration-dependent interaction, affirming the functionality of the NTD and RBD fusion proteins and establishing their efficacy in antigen-antibody interactions. This study not only elucidates the usage potential of the fusion proteins in immunoassays but also addresses the suitability of the A. oryzae expression system as a biotechnological platform to produce SARS-CoV-2 proteins. Furthermore, this study lays the foundation for scalable and cost-effective mass production of effective NTD and RBD proteins in A. oryzae, opening up a new era of COVID-19 research, vaccine development, and immunoassay design.

The Application of MD Simulation to Lead Identification, Vaccine Design, and Structural Studies in Combat against Leishmaniasis - A Review.

Drug discovery, vaccine design, and protein interaction studies are rapidly moving toward the routine use of molecular dynamics simulations (MDS) and related methods. As a result of MDS, it is possible to gain insights into the dynamics and function of identified drug targets, antibody-antigen interactions, potential vaccine candidates, intrinsically disordered proteins, and essential proteins. The MDS appears to be used in all possible ways in combating diseases such as cancer, however, it has not been well documented as to how effectively it is applied to infectious diseases such as Leishmaniasis. As a result, this review aims to survey the application of MDS in combating leishmaniasis. We have systematically collected articles that illustrate the implementation of MDS in drug discovery, vaccine development, and structural studies related to Leishmaniasis. Of all the articles reviewed, we identified that only a limited number of studies focused on the development of vaccines against Leishmaniasis through MDS. Also, the PCA and FEL studies were not carried out in most of the studies. These two were globally accepted utilities to understand the conformational changes and hence it is recommended that this analysis should be taken up in similar approaches in the future.

Point-of-care monitoring of milk quality by rapid Immunofluorescence with mechanical deformation of the hydrogel microspheres

Point-of-care testing (POCT) of chloramphenicol (CAP) in fresh milk from private ranches is essential to ensure food safety. It’s still a great challenge to meet simultaneously rapid and accurate during POCT of CAP. Here, we present a POCT device integrating hydrogel microsphere immunoassay and smartphone Fourier analysis for fast and sensitive detection of CAP. Based on the excellent biocompatibility and outstanding mechanical properties of hydrogel microspheres (100 μm), the antibody activity can be maintained and the mechanical deformation of the hydrogel cross-linking lattice is induced by squeezing to accelerate the antigen-antibody reaction of CAP and anti-CAP antibodies. The signal-to-noise ratio of the detection result is optimized adaptively by a designed microsphere Fourier analysis algorithm for precise CAP concentration detection. The results show that the method decreases incubation time from 60 min to 25 min and meets detection limit requirements of industrial production (0.05 ng/mL, with a dynamic range of 0.05–8 ng/mL). This method not only achieves rapid and accurate on-site detection of CAP in milk but also applies to other low concentrations of harmful substances detection in food field.

Surface Acoustic Wave-Driven Enhancement of Enzyme-Linked Immunosorbent Assays: ELISAW.

Enzyme-linked immunosorbent assays (ELISAs) are widely used in biology and clinical diagnosis. Relying on antigen-antibody interaction through diffusion, the standard ELISA protocol can be time-consuming, preventing its use in rapid diagnostics. We present a time-saving and more sensitive ELISA without changing the standard setup and protocol, using surface acoustic waves (SAWs) to enhance performance. Each step of the assay, from the initial antibody binding onto the walls of the well plate to the target analyte molecules' binding for detection─except, notably, for the blocking step─is improved principally via acoustic streaming-driven advection. Using SAWs, the time required for one step of an example ELISA is reduced from 60 to 15 min to achieve the same binding amount. By extending the duration of SAW exposure to 20 min, the sensitivity can be significantly improved over the 60 min, 35 °C ELISA without SAWs. It is also possible to confer beneficial improvements to bead-based ELISA by combining it with SAWs to further reduce the time required for binding to 2 min. By significantly increasing the speed of ELISA, its utility may be improved for a wide range of point-of-care diagnostics applications.

Antibody design using deep learning: from sequence and structure design to affinity maturation.

Deep learning has achieved impressive results in various fields such as computer vision and natural language processing, making it a powerful tool in biology. Its applications now encompass cellular image classification, genomic studies and drug discovery. While drug development traditionally focused deep learning applications on small molecules, recent innovations have incorporated it in the discovery and development of biological molecules, particularly antibodies. Researchers have devised novel techniques to streamline antibody development, combining in vitro and in silico methods. In particular, computational power expedites lead candidate generation, scaling and potential antibody development against complex antigens. This survey highlights significant advancements in protein design and optimization, specifically focusing on antibodies. This includes various aspects such as design, folding, antibody-antigen interactions docking and affinity maturation.

Exploring the ability of the MD+FoldX method to predict SARS-CoV-2 antibody escape mutations using large-scale data.

Antibody escape mutations pose a significant challenge to the effectiveness of vaccines and antibody-based therapies. The ability to predict these escape mutations with computer simulations would allow us to detect threats early and develop effective countermeasures, but a lack of large-scale experimental data has hampered the validation of these calculations. In this study, we evaluate the ability of the MD+FoldX molecular modeling method to predict escape mutations by leveraging a large deep mutational scanning dataset, focusing on the SARS-CoV-2 receptor binding domain. Our results show a positive correlation between predicted and experimental data, indicating that mutations with reduced predicted binding affinity correlate moderately with higher experimental escape fractions. We also demonstrate that better performance can be achieved using affinity cutoffs tailored to distinct antibody-antigen interactions rather than a one-size-fits-all approach. We find that 70% of the systems surpass the 50% precision mark, and demonstrate success in identifying mutations present in significant variants of concern and variants of interest. Despite promising results for some systems, our study highlights the challenges in comparing predicted and experimental values. It also emphasizes the need for new binding affinity methods with improved accuracy that are fast enough to estimate hundreds to thousands of antibody-antigen binding affinities.

Fluorescence immunosensor based on a specific monoclonal antibody for highly sensitive and rapid detection of gizzerosine in feed

Gizzerosine is a biogenic amine produced in fish meal drying process and posted higher mortality due to gizzard erosion in poultry than histamine. However, it is difficult to obtain gizzerosine and achieve sensitive practical detection due to its simple structure. Herein, a monoclonal antibody (mAb) specific to gizzerosine was generated based on the new structural design and a fluorescence immunosensor for sensitive and on-site detection of gizzerosine in feed was first established. Molecular modeling of the three-dimensional (3D) structure and surface electrostatic potential of gizzerosine indicated that the carbonyl group of gizzerosine hapten might affect the important sites of antigen-antibody interactions. The proposed structure was used to obtain the sensitive and specific mAb with IC50 of 3.88 ng/mL in indirect competitive ELISA which was approximately 100-fold lower than that of direct competitive ELISA. Considering the practical application scenarios, a fluorescence immunosensor based on microporous dry method integrated with independent quality control line was established to improve detection stability. Under the optimum conditions, the proposed immunosensor showed a good linear relationship from 1.10 to 19.78 ng/mL and provided a low detection limit of 50 ng/g which was approximately 80-fold lower than the maximum recommended amount (0.4 mg/kg) of gizzerosine in feed. The recoveries of 6 kinds of feed ranged from 83.1 % to 114.3 %, which was in good consistence with that of UHPLC–MS/MS. Overall, this work provides a fast, cost-effective and reliable on-site tool for rapid screening of gizzerosine residues in feed samples.

Pt/Zn-TCPP Nanozyme-Based Flexible Immunoassay for Dual-Mode Pressure-Temperature Monitoring of Low-Abundance Proteins.

Pressure and temperature, as common physical parameters, are important for monitoring human health. In contrast, single-mode monitoring is prone to causing experimental errors. Herein, we innovatively designed a dual-mode flexible sensing platform based on a platinum/zinc-meso-tetrakis(4-carboxyphenyl)porphyrin (Pt/Zn-TCPP) nanozyme for the quantitative monitoring of carcinoembryonic antigen (CEA) in biological fluids with pressure and temperature readouts. The Pt/Zn-TCPP nanozyme with catalytic and photothermal efficiencies was synthesized by means of integrating photosensitizers into porous materials. The flexible sensing system after the antigen-antibody reaction recognized the pressure using a flexible skin-like pressure sensor with a digital multimeter readout, whereas the temperature was acquired via the photoheat conversion system of the Pt/Zn-TCPP nanozyme under 808 nm near-infrared (NIR) irradiation using a portable NIR imaging camera on a smartphone. Meanwhile, the dual-mode flexible sensing system was carried out on a homemade three-dimensional (3D)-printed device. Results revealed that the developed dual-mode immunosensing platform could exhibit good pressure and temperature responses within the dynamic range of 0.5-100 ng mL-1 CEA with the detection limits of 0.24 and 0.13 ng mL-1, respectively. In addition, the pressure and temperature were sensed simultaneously without crosstalk interference. Importantly, the dual-mode flexible immunosensing system can effectively avoid false alarms during the measurement, thus providing great potential for simple and low-cost development for point-of-care testing.

Electrochemical Immunoassay for Capturing Capsular Polysaccharide of Burkholderia pseudomallei: Early Onsite Detection of Melioidosis.

This study presented the detection and quantification of capsular polysaccharide (CPS) as a biomarker for the diagnosis of melioidosis. After successfully screening four monoclonal antibodies (mAbs) previously determined to bind CPS molecules, the team developed a portable electrochemical immunosensor based on antibody-antigen interactions. The biosensor was able to detect CPS with a wide detection range from 0.1pg/mL to 1 μg/mL. The developed biosensor achieved high sensitivity for the detection of CPS spiked into both urine and serum. The developed assay platform was successfully programmed into a Windows app, and the sensor performance was evaluated with different spiked concentrations. The rapid electro-analytical device (READ) sensor showed great unprecedented sensitivity for the detection of CPS molecules in both serum and urine, and results were cross-validated with ELISA methods.

A benchmark for evaluation of structure-based online tools for antibody-antigen binding affinity

The prediction of binding affinity changes caused by missense mutations can elucidate antigen-antibody interactions. A few accessible structure-based online computational tools have been proposed. However, selecting suitable software for particular research is challenging, especially research on the SARS-CoV-2 spike protein with antibodies. Therefore, benchmarking of the mutation-diverse SARS-CoV-2 datasets is critical. Here, we collected the datasets including 1216 variants about the changes in binding affinity of antigens from 22 complexes for SARS-CoV-2 S proteins and 22 monoclonal antibodies as well as applied them to evaluate the performance of seven binding affinity prediction tools. The tested tools' Pearson correlations between predicted and measured changes in binding affinity were between −0.158 and 0.657, while accuracy in classification tasks on predicting increasing or decreasing affinity ranged from 0.444 to 0.834. These tools performed relatively better on predicting single mutations, especially at epitope sites, whereas poor performance on extremely decreasing affinity. The tested tools were relatively insensitive to the experimental techniques used to obtain structures of complexes. In summary, we constructed a list of datasets and evaluated a range of structure-based online prediction tools that will explicate relevant processes of antigen-antibody interactions and enhance the computational design of therapeutic monoclonal antibodies.

Comparative Study of Cantilever Biosensor for Detection of Tuberculosis

Abstract: Now everywhere in the world sensors are used to solve daily human problem. In the medical field sensors are used to diagnose various diseases. In this article for detection of tuberculosis, micro cantilever biosensor has been design and simulate. Rectangular shape cantilever biosensors with silicon, poly-silicon and aluminium material were designed and analysed the displacement. The displacements of this biosensor are compared with the other available models. The MEMS based micro cantilever biosensor were design in COMSOL Multiphysics. 10N to 100N load force are applied on the surface of cantilever for deliberate the interaction of antigen antibody. Stress generated on the surface of cantilever when antigen antibody binds together and it get deflects. This deflection is measured for 10N to 100N load force which is compare with other modules. The displacement generated by cantilever was 1.71 x 1028 µm for 100N force which is higher than all the other models, thus the proposed cantilever model is the best model for detection of tuberculosis