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

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

Confirmation of the validity of the current characterization of immunochemical reactions by kinetic exclusion assay.

Prior observations that questioned the validity of kinetic exclusion assays were based on the mistaken assumption that the assays quantified the fraction of those antibody molecules that had unoccupied binding sites. Instead, the standard KinExA assay quantifies the fraction of total antibody binding sites that are unoccupied, regardless of the number of unoccupied sites on each antibody molecule. Although the standard KinExA analysis assumes that there is only a small probability of antibody-site capture by the affinity matrix, the results of numerical simulations demonstrate the reliability of dissociation constants obtained by the standard KinExA analysis for capture probabilities as high as 30%. This finding further strengthens the potential of kinetic exclusion assays as the procedure of choice for the rapid and accurate characterization of immunochemical reactions that forms part of screening processes in the search for therapeutic antibodies.

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.

Monoclonal antibodies to lipoarabinomannan/arabinomannan - characteristics and implications for tuberculosis research and diagnostics.

Antibodies to the mycobacterial surface lipoglycan lipoarabinomannan (LAM) and its related capsular polysaccharide arabinomannan (AM) are increasingly important for investigations focused on both understanding mechanisms of protection against Mycobacterium tuberculosis (Mtb) and developing next-generation point-of-care tuberculosis (TB) diagnostics. We provide here an overview of the growing pipeline of monoclonal antibodies (mAbs) to LAM/AM. Old and new methodologies for their generation are reviewed and we outline and discuss their glycan epitope specificity and other features with implications for the TB field.

Improvements in skills and knowledge after a comprehensive ELISA teaching course for biotechnology undergraduates.

As a universal and extensively adopted technique, enzyme-linked immunosorbent assay (ELISA) can be used to detect and quantify small molecules in many applications both clinical and analytical. However, generally, students experiment mechanically using commercial ELISA kits according to the instructions and eventually produce a standard curve to calculate the concentration of the sample to be measured, cannot understand the critical factors and process of method establishment. This study systematically introduced undergraduates to using the pathogen-specific antigen and establishing an indirect ELISA method to detect the diagnostic target pathogen Burkholderia pseudomallei. This course aimed to develop the experimental skills of the students and improve their scientific research knowledge, which fully embody the organic combination of scientific research and teaching. Students independently selected the diagnostic antigen target of interest, obtained the antigen proteins using genetic engineering techniques, and established an ELISA method through a series of conditional optimization experiments. In addition, typical student-generated data, experimental methods, and a student feedback interpretation are presented in this study. Overall, the students were able to combine abstract knowledge with practice and understand the principles and applications of antigen-antibody interactions, thus enabling them to gain practical experience in molecular biology techniques, and learn how to use this principle to establish an ELISA method for detecting infectious diseases.

Detection of culture-negative sepsis in clinical blood samples using a microfluidic assay for combined CD64 and CD69 cell capture.

Sepsis is a life-threatening disease that affects millions of people every year. Rapid detection of sepsis assists clinicians to initiate timely antibiotic therapy and to reduce mortality. At the same time, accurate point-of-care detection is needed to reduce unnecessary use of antibiotics. One of the principal challenges in sepsis diagnosis is that many sepsis cases do not result in positive blood cultures. These so-called culture-negative cases present a significant health threat. In this work, we present a microfluidic cells separation system for the detection of sepsis in both culture-positive and culture-negative cases. Leukocytes were captured in several affinity separation zones of a microchip based on CD64, CD69, and CD25 expression. To validate this assay 40 septic patients and 10 healthy volunteers were enrolled in this study. Septic patients were divided into culture-positive (n = 12) and culture-negative cases (n = 21). CD64 + cell capture demonstrated excellent accuracy for sepsis detection with an area under the receiver operating characteristic curves (AUC) of 0.962. A combined panel of CD64 + and CD69 + cell counts was constructed, and the new panel outperformed each of these two biomarkers alone with the AUC of 0.978. Our affinity microfluidic devices were validated by conventional flow cytometry analysis. Results showed that the cell capture number of specific affinity region increased along with the increase of its corresponding antigen expression. This clinical validation confirms that CD64 and CD69 cell separations are a powerful sepsis assay with the potential for point-of-care analysis in culture-positive and culture-negative cases.

Structural basis for neutralization of cytotoxic abrin by monoclonal antibody D6F10.

Abrin, an extremely cytotoxic Type II ribosome-inactivating protein (RIP), is a potential bio-warfare agent. Abrin A-chain (ABA) depurinates an adenosine of sarcin-ricin loop (SRL) from eukaryotic 28S rRNA, thereby arresting protein synthesis and leading to cell death. Monoclonal antibody (mAb) D6F10 is the only known antibody that neutralizes ABA's activity in cell-free systems as well as abrin's toxicity in vitro and in vivo. However, how binding of mAb D6F10 to abrin interferes with abrin's catalytic activity at ribosome is still poorly understood. To provide structural basis for mAb D6F10-mediated rescue of ribosome inactivation by abrin, we determined crystal structures of ABA with and without substrate analogs. The structures of ABA-substrate analogs and ribosome were used in an experiment-guided computational protocol, to construct the ABA-Ribosome complex. A homology model of the variable region (Fv ) of mAb D6F10 was generated and docked with the apo-ABA structure to construct the ABA-D6F10 Fv complex. Structural superposition of ABA common to ABA-D6F10 Fv and ABA-Ribosome complexes reveals steric hindrance as the primary mechanism by which mAb D6F10 neutralizes abrin. In contrast to ABA alone, ABA bound to mAb D6F10 is unable to access the SRL on the ribosome owing to steric clashes of mAb D6F10 with the ribosome. Crystal structures of ABA also reveal a catalytic water molecule implicated in hydrolyzing N-glycosidic bond of the susceptible adenosine by RIPs. Furthermore, our strategy provides structural details of steric hindrance important for neutralization of ricin, another RIP, by mAb 6C2 and hence is of wide applicability. ENZYME: EC3.2.2.22. DATABASE: Structural data have been deposited in the Protein Data Bank (PDB) under the accession numbers 5Z37, 5Z3I, and 5Z3J.

Extending rule-based methods to model molecular geometry and 3D model resolution.

BACKGROUND: Computational modeling is an important tool for the study of complex biochemical processes associated with cell signaling networks. However, it is challenging to simulate processes that involve hundreds of large molecules due to the high computational cost of such simulations. Rule-based modeling is a method that can be used to simulate these processes with reasonably low computational cost, but traditional rule-based modeling approaches do not include details of molecular geometry. The incorporation of geometry into biochemical models can more accurately capture details of these processes, and may lead to insights into how geometry affects the products that form. Furthermore, geometric rule-based modeling can be used to complement other computational methods that explicitly represent molecular geometry in order to quantify binding site accessibility and steric effects. RESULTS: We propose a novel implementation of rule-based modeling that encodes details of molecular geometry into the rules and binding rates. We demonstrate how rules are constructed according to the molecular curvature. We then perform a study of antigen-antibody aggregation using our proposed method. We simulate the binding of antibody complexes to binding regions of the shrimp allergen Pen a 1 using a previously developed 3D rigid-body Monte Carlo simulation, and we analyze the aggregate sizes. Then, using our novel approach, we optimize a rule-based model according to the geometry of the Pen a 1 molecule and the data from the Monte Carlo simulation. We use the distances between the binding regions of Pen a 1 to optimize the rules and binding rates. We perform this procedure for multiple conformations of Pen a 1 and analyze the impact of conformation and resolution on the optimal rule-based model. CONCLUSIONS: We find that the optimized rule-based models provide information about the average steric hindrance between binding regions and the probability that antibodies will bind to these regions. These optimized models quantify the variation in aggregate size that results from differences in molecular geometry and from model resolution.