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Identification of critical quality attributes (CQAs) is an important step for development of biopharmaceuticals with intended performance. An accurate CQA assessment is needed to ensure product quality and focusing on development efforts where control is needed. The assignment of criticality is based on safety and efficacy. Efficacy is related to PK and bioactivity. Here, we developed a novel approach based on antibody-antigen complex structure and modeling as a complementary method for bioactivity assessment. To validate this approach, common product related quality attributes and mutagenesis data from several IgGs were assessed using available antibody-antigen complex structures, and results were compared with experimental data from bioactivity or binding affinity measurements. A stepwise evaluation scheme for structural based analysis is proposed; based on systematic assessment following the scheme, good correlation has been observed between structural analysis and experimental data. This demonstrates that such an approach can be applied as a complementary tool for bioactivity assessment. Main applications are 1) To decouple multiple attributes to achieve amino acid resolution for bioactivity assessment, 2) To assess bioactivity of attributes that cannot be experimentally generated, 3) To provide molecular mechanism for experimental observation and understand structure function relationship. Examples are provided to illustrate these applications.

Summary This protocol describes the use of a biolayer interferometry platform for assessing antibody-antigen interactions. The protocol focuses on affinity determination and epitope binning, although the system can be utilized for measuring any protein-protein interaction. Readings are collected in real time, allowing the use of unlabeled molecules, and data can thus be obtained in a fast and easy manner. Experiments should be carefully designed, taking into consideration the tested interaction, available sensors, and suitable controls. For complete details on the use and execution of this protocol, please refer to Noy-Porat et al. (2021) .

We recently demonstrated that capturing human monoclonal antibodies (hmAbs) using high affinity anti-human Fc (AHC) antibodies allows reliable characterization of antibody-antigen interactions. Here, we characterized six human Fc specific mouse monoclonal antibodies (mAbs) and compared their binding profiles with three previously characterized goat AHC polyclonal antibodies (pAbs), exhibiting properties of a good capture reagent. All six mouse AHC mAbs specifically bound with high affinity to the Fc region of hIgG1, hIgG2, hIgG4 and to 43 different hIgG variants, containing substitutions and/or mutations in the hinge and/or Fc region, that have been reported to exhibit modified antibody effector function and/or pharmacokinetics. Biacore sensor surfaces individually derivatized with mouse AHC mAbs exhibited2.5-fold higher hIgG binding capacity compared to the three goat AHC pAb surfaces and reproducibly captured hIgG over 300 capture-regeneration cycles. The results of the capture kinetic analyses performed on 31 antibody-antigen interactions using surfaces derivatized with either of the two highest affinity AHC mAbs (REGN7942 or REGN7943) were in concordance with those performed using goat AHC pAb surfaces. Our data demonstrate that AHC mAbs such as REGN7942 and REGN7943 that have properties superior than the three goat AHC pAbs are highly valuable research reagents, especially to perform capture kinetic analyses of antibody-antigen interactions on optical biosensors.

Different diagnostic tests were evaluated in a cluster of six European travellers with acute schistosomiasis (AS) acquired in Mozambique. Adult worm and soluble egg antibodies, circulating anodic antigen (CAA) and DNA detection allowed early confirmation of AS. CAA permitted monitoring treatment response and Sm1-7 polymerase chain reaction allowed identification of Schistosoma mansoni complex.

هدفت الدراسة الحالية الى تقييم الانتشار المصلي لداء المقوسات القندية في مرضى الفشل الكلوي المزمن الذين خضعوا للغسيل الكلوي في مستشفيات ديالى, استعمل اختبار معقد الانتاجين والجسم المضاد للكشف عن حدوث داء المقوسات في مرضى غسيل الكلية ,وقد اظهرت النتائج لمرضى الغسيل الكلوي للمرضى المصابين ب Toxoplasma والحاملين للجسم المضاد IgG اذا كانت النسبة المئوية 12.6% . واظهرت النتائج ان الفئة العمرية (41-50) والفئة العمرية (51-60) هم النسبة الاعلى بالاجسام المضادة . كما تم الكشف عن زيادة في معدل ايجابية المصل مع زيادة طول وقت العلاج بغسيل الكلى, واظهرت الدراسة عدم وجود ارتباط معنوي احصائي بمستوى P

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

Antibody-antigen (Ab-Ag) interactions are canonically described by a model which exclusively accommodates non-interaction (0) or reproducible-interaction (RI) states, yet this model is inadequate to explain often-encountered non-reproducible signals. Here, by monitoring diverse experimental systems and confirmed COVID-19 clinical sera using a peptide microarray, we observed that non-specific interactions (NSI) comprise a substantial proportion of non-reproducible antibody-based results. This enabled our discovery and capacity to reliably identify non-reproducible Ab-Ag interactions (NRI), as well as our development of a powerful explanatory model (“0-RI-NRI-Hook four-state model”) that is [mAb]-dependent, regardless of specificity, which ultimately shows that both NSI and NRI are not predictable yet certain-to-happen. In experiments using seven FDA-approved mAb drugs, we demonstrated the use of NSI counts in predicting epitope type. Beyond challenging the centrality of Ab-Ag interaction specificity data in serology and immunology, our discoveries also facilitated the rapid development of a serological test with uniquely informative COVID-19 diagnosis performance.

Coronavirus disease 2019 (COVID-19) is a newly emerging human infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Early diagnosis is essential to reducing the transmission rate and mortality of COVID-19. PCR-based tests are the gold standard for the confirmation of COVID-19, but immunological tests for SARS-CoV-2 detection are widely available and play an increasingly important role in the diagnosis of COVID-19. Nanomechanical sensors are biosensors that work based on a change in the mechanical response of the system when a foreign object is added. In this paper, a graphene-based nanoresonator sensor for SARS-CoV-2 detection was introduced and analyzed by using the finite element method (FEM). The sensor was simulated by coating a single-layer graphene sheet (SLGS) with a specific antibody against SARS-CoV-2 Spike S1 antigen. In the following, the SARS-CoV-2 viruses were randomly distributed on the SLGSs, and essential design parameters of the nanoresonator, including frequency shift and relative frequency shift, were evaluated. The effect of the SLGS size, aspect ratio and boundary conditions, antibody concentration, and the number of viruses variation on the frequency shift and relative frequency shift were investigated. The results revealed that, by proper selection of the nanoresonator design variables, a good sensitivity index is achievable for identifying the SARS-CoV-2 virus even when the number of the viruses are less than 10 per test. Eventually, according to the simulation results, by using SLGS geometry determination, an analytical relationship is presented to predict the limit of detection (LOD) of the sensor with the required sensitivity index. The results can be applied in designing and fabricating specific graphene-based nanoresonator sensors for SARS-CoV-2.

Motivation Understanding antibody–antigen interactions is key to improving their binding affinities and specificities. While experimental approaches are fundamental for developing new therapeutics, computational methods can provide quick assessment of binding landscapes, guiding experimental design. Despite this, little effort has been devoted to accurately predicting the binding affinity between antibodies and antigens and to develop tailored docking scoring functions for this type of interaction. Here, we developed CSM-AB, a machine learning method capable of predicting antibody–antigen binding affinity by modelling interaction interfaces as graph-based signatures. Results CSM-AB outperformed alternative methods achieving a Pearson's correlation of up to 0.64 on blind tests. We also show CSM-AB can accurately rank near-native poses, working effectively as a docking scoring function. We believe CSM-AB will be an invaluable tool to assist in the development of new immunotherapies. Availability and implementation CSM-AB is freely available as a user-friendly web interface and API at http://biosig.unimelb.edu.au/csm_ab/datasets. Supplementary information Supplementary data are available at Bioinformatics online.

DNA sequencing technologies are providing new insights into the immune response by allowing the large scale sequencing of rearranged immunoglobulin gene present in an individual, however the applications of this approach are limited by the lack of methods for determining the antigen(s) that an immunoglobulin encoded by a given sequence binds to. Computational methods for predicting antibody-antigen interactions that leverage structure prediction and docking have been proposed, however these methods require knowledge of the 3D structures.As a step towards the development of a machine learning method suitable for predicting antibody-antigen binding affinities from sequence data, a weighted nearest neighbor machine learning approach was applied to the problem. A prediction program was coded in Python and evaluated using cross-validation on a dataset of 600 antibodies interacting with 50 antigens. The classification predicting accuracy was around 76% for this dataset. These results provide a useful frame of reference as well as protocols and considerations for machine learning and dataset creation in this area.Both the dataset (in csv format) and the machine learning program (coded in python) are freely available for download.

Accurate predictive modeling of antibody-antigen complex structures and structure-based antibody design remain major challenges in computational biology, with implications for biotherapeutics, immunity, and vaccines. Through a systematic search for high-resolution structures of antibody-antigen complexes and unbound antibody and antigen structures, in conjunction with identification of experimentally determined binding affinities, we have assembled a non-redundant set of test cases for antibody-antigen docking and affinity prediction. This benchmark more than doubles the number of antibody-antigen complexes and corresponding affinities available in our previous benchmarks, providing an unprecedented view of the determinants of antibody recognition and insights into molecular flexibility. Initial assessments of docking and affinity prediction tools highlight the challenges posed by this diverse set of cases, which includes camelid nanobodies, therapeutic monoclonal antibodies, and broadly neutralizing antibodies targeting viral glycoproteins. This dataset will enable development of advanced predictive modeling and design methods for this therapeutically relevant class of protein-protein interactions.

Although in situ atomic force microscopy (AFM) can allow single-molecule detection of antibody-antigen binding, the practical applications of in situ AFM for disease diagnosis are greatly limited, due to its operational complexity and long operational times, including the execution time for the surface chemical/biological treatments in the equipped glass liquid cell. In this report, we present a method of graphically superimposed alignment that enables ex situ AFM analysis of an immobilized antibody at the same location on a semiconductor chip surface before and after incubation with its antigen. All of the required chemical/biological treatments can be executed feasibly using standard laboratory containers, allowing single-molecule ex situ AFM detection to be performed with great practicality, flexibility, and versatility. As an example, we describe the analysis of hepatitis B virus X protein (HBx) and its IgG antibody. Using ex situ AFM, we extracted individual information about the topographical characteristics of the immobilized single and aggregated IgG antibodies on the chip surface and analyzed the data statistically. Furthermore, we investigated, in a statistical manner, the changes in AFM-measured heights of the individual and aggregated IgG antibodies that occurred as a result of changes in conformation upon formation of IgG-HBx complexes. This article is protected by copyright. All rights reserved.

Protein biomolecules are used as markers for various diseases and infections and are targets in biosensing research and development. One of the highest-sensitivity biosensing techniques is considered to be fluorescence (FL) sensing. However, to our knowledge, no study has shown that all-dielectric metasurfaces can contribute to highly sensitive FL sensing. Here, we introduce an efficient type of FL-sensing platforms and show that all-dielectric metasurface FL biosensors are able to directly detect a representative antibody, immunoglobulin G, at very small concentrations on the order of pg/mL or tens of femtomolars. Furthermore, it is shown that they work efficiently as an indirect detection platform for standard cancer marker antigens, such as carcinoembryonic antigens, at concentrations well below the medical diagnosis criterion at 5 ng/mL. Importantly, the metasurface biosensors simultaneously suppress inhomogeneous FL responses, exhibit high reproducibility, and retain sensitivity, even in human serum. These results indicate that the present metasurface FL biosensors provide a high-sensitivity practical platform, suggesting that they are a better option than the commercially standard of enzyme-linked immunosorbent assays.

In recent decades, antibodies (Abs) have attracted the attention of academia and the biopharmaceutical industry due to their therapeutic properties and versatility in binding a vast spectrum of antigens. Different engineering strategies have been developed for optimizing Ab specificity, efficacy, affinity, stability and production, enabling systematic screening and analysis procedures for selecting lead candidates. This quality assessment is critical but usually demands time-consuming and labor-intensive purification procedures. Here, we harnessed the direct-mass spectrometry (direct-MS) approach, in which the analysis is carried out directly from the crude growth media, for the rapid, structural characterization of designed Abs. We demonstrate that properties such as stability, specificity and interactions with antigens can be defined, without the need for prior purification. This ty.

Structure-based vaccine design depends on extensive structural analyses of antigen-antibody complexes.Single-particle electron cryomicroscopy (cryoEM) can circumvent some of the problems of x-ray crystallography as a pipeline for obtaining the required structures. We have examined the potential of single-particle cryoEM for determining the structure of influenza-virus hemagglutinin (HA):single-chain variable-domain fragment complexes, by studying a complex we failed to crystallize in pursuing an extended project on the human immune response to influenza vaccines.The result shows that a combination of cryoEM and molecular modeling can yield details of the antigen-antibody interface, although small variation in the twist of the rod-likeHA trimer limited the overall resolution to about 4.5Å.Comparison of principal 3D classes suggests ways to modify the HA trimer to overcome this limitation. A closely related antibody from the same donor did yield crystals when bound with the same HA, giving us an independent validation of the cryoEM results.The two structures also augment our understanding of receptor-binding site recognition by antibodies that neutralize a wide range of influenza-virus variants.

To investigate the dynamic interaction between antibody and antigen, Fan et al. rationally designed a triangular DNA origami framework to spatially organize the antigenic epitopes at the nanoscale and thus to monitor the transient binding kinetics of the dynamic antigen-antibody complexes at room temperature. This study provides a straightforward, designable and programmable strategy to investigate the transition kinetics of antibody-antigen interaction at a single-molecule level and improve the understanding toward the design of the next-generation antibodies and vaccines for various biomedical applications. This work has been published online in Nature Communications on June 19, 2020.

Mortalin is a widely studied stress chaperone that plays a significant role in diseases such as cancer, diabetes mellitus, liver cirrhosis, neurodegeneration and generalized aging. Based on these, the level of mortalin expression has been predicted to be an important and valuable diagnostic and prognostic marker. Conventional methods of protein analyses, such as Western blotting, immunohistochemistry or ELISA with antibodies provide specific, sensitive and useful outcomes. However, they are limited by lengthy and time-consuming protocols. Here, we present an upgrade to the existing ELISA techniques. We have prepared a conjugate of anti-mortalin antibody and luciferase enzyme that can be recruited for rapid (∼3 h) and quantitative detection of mortalin expression in a given biological sample.

Background: Multispecific antibodies are artificially engineered molecules designed to bind simultaneously to several (different) antigens. Potential advantages of generating viable multispecific antibodies include the identification of malignant cells coupled with the concurrent recruitment of immune cells and the blocking of complex viral escape mechanisms. The cross-over dual-variable immunoglobulin (CODV-Ig) has been proposed as a universal bispecific therapeutic format. Its unique antigen-binding fragment (Fab) architecture provides pM affinities for ligands, no positional effect in target binding and a stable self-supporting structure. However, the three-dimensional arrangement of the constant and antigen-binding fragments in the CODV-Ig format may play a role in its in vivo effects. To further understand the structure and function of multispecific antibodies based on the CODV-Ig format high-resolution structural information is required. Towards this, we use cryo-electron microscopy (cryo-EM). Methods: We purified CODV-Ig both in an unbound state and in complex with a single antigen and validated sample quality using SDS-PAGE, Small Angle X-Ray Scattering (SAXS) and negative-stain electron microscopy (NSEM; Tecnai T12 and F20 microscopes at IBS, Grenoble). Data of sufficient quality for image analysis was obtained using a Titan Krios microscope (ESRF, Grenoble) equipped with a Quantum LS energy filter and K2 Summit direct electron detector. Results: NSEM of CODV-Ig resulted in low-resolution structural models and suggested a preferential orientation of the antibody under negative-stain conditions. Close-to-optimal vitrification conditions for CODV-Ig and antibody-antigen complexes have been identified. Efforts are in progress to reduce the antibody’s propensity to aggregation and aversion to conventional cryo-EM supports. Nevertheless, image processing of both CODV-Ig alone and in complex with antigens suggests very high flexibility and conformational heterogeneity. Conclusion: Particle heterogeneity may require additional data to be collected, in order to have sufficient signal that will lead to well-defined classes. An additional strategy may involve efforts to immobilize the molecules as to obtain fewer and better-defined classes.

The natural immune repertoire can be a useful guide to antibody discovery against any given target. However, the large volume of immunoglobulin gene sequencing data necessitates the rational prioritisation of possible binders for experimental validation. Where other known binders exist, sequence similarity is used to infer binding, but this neglects alternative binding modes to the same epitope, and cannot identify antibodies that bind to different epitopes. In this review, we summarise the state-of-the-art of high-throughput antibody–antigen complex modelling. Given the millions of natural antibody sequences now available, this pipeline attempts to predict whether, and if so how, each antibody binds to a particular antigen's surface. We cover the current paradigm (antibody and antigen structural modelling, followed by binding site prediction, followed by molecular docking), discussing how existing algorithms can deal with this magnitude of data by balancing accuracy with computational efficiency, and identifying areas where further developments are required to improve performance.

B-cells can neutralize pathogenic molecules by targeting them with extreme specificity using receptors secreted or expressed on their surface (antibodies). This is achieved via molecular interactions between the paratope (i.e., the antibody residues involved in the binding) and the interacting region (epitope) of its target molecule (antigen). Discerning the rules that define this specificity would have profound implications for our understanding of humoral immunogenicity and its applications. The aim of this work is to produce improved, antibody-specific epitope predictions by exploiting features derived from the antigens and their cognate antibodies structures, and combining them using statistical and machine learning algorithms. We have identified several geometric and physicochemical features that are correlated in interacting paratopes and epitopes, used them to develop a Monte Carlo algorithm to generate putative epitopes-paratope pairs, and train a machine-learning model to score them. We show that, by including the structural and physicochemical properties of the paratope, we improve the prediction of the target of a given B-cell receptor. Moreover, we demonstrate a gain in predictive power both in terms of identifying the cognate antigen target for a given antibody and the antibody target for a given antigen, exceeding the results of other available tools. Fil: Jespersen, Martin Closter. Technical University of Denmark; Dinamarca Fil: Mahajan, Swapnil. La Jolla Institute for Allergy and Immunology; Estados Unidos Fil: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados Unidos Fil: Nielsen, Morten. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina. Technical University of Denmark; Dinamarca Fil: Marcatili, Paolo. Technical University of Denmark; Dinamarca

Antibodies provide a key mode of defense employed by the immune system to fight disease, so eliciting potent antibodies is one of the main goals in vaccine development. Antibodies are also being conceived as powerful therapeutic agents, so engineering potent antibodies is one of the main goals in biologic drug development. The power of antibodies lies in their affinity and specificity in recognizing their cognate antigens. Unfortunately, experimental techniques to determine antibody-antigen binding affinities are difficult to scale up to large sets of new antibodies and antigens. Though computational methods are suitable for large-scale prediction, current methods lack sufficient accuracy. Here, we address the problem of predicting the binding affinity of an antibody against an antigen variant based on the amino acid sequence of that antigen variant. We develop a mixture of experts approach that learns models for individual antibodies against some antigen variants, and then combines information across the antibodies in order to make accurate predictions for a wide range of new variants. In evaluation on a dataset consisting of 52 antibodies and 608 strains of HIV, the predictive accuracy of our approach is demonstrated to be significantly better than that of an existing approach. Our method provides a fast and accurate way to predict antibody-antigen binding affinity, which has the potential to expedite the study of antibody-antigen interactions for vaccine design and therapy.

The development of the immunoblot to detect and characterize a protein with an antisera, even in a crude mixture, was a breakthrough with wide-ranging and unpredictable applications across physiology and medicine. Initially, this technique was viewed as a tool for qualitative, not quantitative, analyses of proteins because of the high number of variables between sample preparation and detection with antibodies. Nonetheless, as the immunoblot method was streamlined and improved, investigators pushed it to quantitate protein abundance in unpurified samples as a function of treatment, genotype, or pathology. This short review, geared at investigators, reviewers, and critical readers, presents a set of issues that are of critical importance for quantitative analysis of protein abundance: 1) Consider whether tissue samples are of equivalent integrity and assess how handling between collection and assay influences the apparent relative abundance. 2) Establish the specificity of the antiserum for the protein of interest by providing clear images, molecular weight markers, positive and negative controls, and vendor details. 3) Provide convincing evidence for linearity of the detection system by assessing signal density as a function of sample loaded. 4) Recognize that loading control proteins are rarely in the same linear range of detection as the protein of interest; consider protein staining of the gel or blot. In summary, with careful attention to sample integrity, antibody specificity, linearity of the detection system, and acceptable loading controls, investigators can implement quantitative immunoblots to convincingly assess protein abundance in their samples.

Therapeutic antibodies constitute one of the fastest areas of growth in the field of biologic drugs. A molecular understanding of how antibodies interact with their target antigens is known as epitope mapping. The data provided by epitope mapping is extremely valuable in the process of antibody humanization, as well as in vaccine design. In many cases the epitope recognized by the antibody is a complex, discontinuous 3D conformational epitope. Mapping the interactions of an antibody to a conformational epitope is difficult by many standard approaches. X-ray crystallography is considered to be the gold standard of epitope mapping as it can provide a near atomic resolution model of the antibody-antigen interaction. An X-ray structure allows for inspection of specific antibody-antigen interactions, even in the case of complex conformational epitopes. The method described here can be adapted for structure determination and epitope mapping of any antibody fragment to a simple or complex antigen.

Scientific Reports 6: Article number: 37457; published online: 24 November 2016; updated: 06 April 2017 This Article contains a typographical error in the Methods section under the subheading ‘Crystallization’, where the Protein Data Bank accession code ‘5LDN’ was incorrectly given as ‘5LDV’.

Antibody–antigen interactions are critical to our immune response, and understanding the structure-based biophysical determinants for their binding specificity and affinity is of fundamental importance. We present a computational structure-based cross-docking study to test the identification of native antibody–antigen interaction pairs among cognate and non-cognate complexes. We picked a dataset of 17 antibody–antigen complexes of which 11 have both bound and unbound structures available, and we generated a representative ensemble of cognate and non-cognate complexes. Using the Rosetta interface score as a classifier, the cognate pair was the top-ranked model in 80% (14/17) of the antigen targets using bound monomer structures in docking, 35% (6/17) when using unbound, and 12% (2/17) when using the homology-modeled backbones to generate the complexes. Increasing rigid-body diversity of the models using RosettaDock’s local dock routine lowers the discrimination accuracy with the cognate antibody–antigen pair ranking in bound and unbound models but recovers additional top-ranked cognate complexes when using homology models. The study is the first structure-based cross-docking attempt aimed at distinguishing antibody–antigen binders from non-binders and demonstrates the challenges to address for the methods to be widely applicable to supplement high-throughput experimental antibody sequencing workflows.

Summary Antibodies are Y-shaped proteins essential for immune response. Their capability to recognize antigens with high specificity makes them excellent therapeutic targets. Understanding the structural basis of antibody-antigen interactions is therefore crucial for improving our ability to design efficient biological drugs. Computational approaches such as molecular docking are providing a valuable and fast alternative to experimental structural characterization for these complexes. We investigate here how information about complementarity-determining regions and binding epitopes can be used to drive the modeling process, and present a comparative study of four different docking software suites (ClusPro, LightDock, ZDOCK, and HADDOCK) providing specific options for antibody-antigen modeling. Their performance on a dataset of 16 complexes is reported. HADDOCK, which includes information to drive the docking, is shown to perform best in terms of both success rate and quality of the generated models in both the presence and absence of information about the epitope on the antigen.

The unique hypervariability of the immunoglobulin (Ig) superfamily provides a means to create both binding and catalytic antibodies with almost any desired specificity and activity. The diversity of antigens and concept of adaptive response suggest that it is possible to find an antigen pair to any raised Ig. In the current review we discuss combinatorial approaches, which makes it possible to obtain an antibody with predefined properties, followed by 3D structure-based rational design to enhance or dramatically change its characteristics. A similar strategy, but applied to the second partner of the antibody–antigen pair, may result in selection of complementary substrates to the chosen Ig. Finally, 2D screening may be performed solving the “Chicken and Egg” problem when neither antibody nor antigen is known.

This present study was designed to evaluate four different Blastomyces dermatitidis antibody-antigen combinations (B5896 and T-58 antibodies and B5896 and WI-R antigens) for the detection of antigen in 36 urine specimens from dogs with blastomycosis using a standard indirect ELISA (STD) and a biotin-streptavidin ELISA (B-SA). The antigen detection sensitivity values ranged from 81% (B-SA: T-58 Ab + WI-R Ag) to 100% (STD and B-SA: B5896 Ab + WI-R Ag; B5896 Ab + B5896 Ag) with the antibody-antigen combinations in the two assays. Optimal detection was evidenced when the B5896 Ab was allowed to react with the urine specimens for 30 min at 37?C and then placed in the B-SA ELISA plates containing the B5896 Ag. The greatest absorbance value obtained with this antibody-antigen com-bination was 0.903 (range of 0.596 - 0.903) as compared to the control value of 1.246. The difference between the control absorbance and the test absorbance values was 0.343 which was considerably greater than the control-test values with the other combinations. This study thus showed that the results obtained in antigen detection assays are dependent upon the antibody used to react with the urine specimens as well as the antigen used in the enzyme immunoassay.

Epistasis is the phenomenon by which the effect of a mutation depends on its genetic background. While it is usually defined in terms of organismal fitness, for single proteins, it must reflect physical interactions among residues. Here, we systematically extract the specific contribution pairwise epistasis makes to the physical affinity of antibody-antigen binding relevant to affinity maturation, a process of accelerated Darwinian evolution. We find that, among competing definitions of affinity, the binding free energy is the most appropriate to describe epistasis. We show that epistasis is pervasive, accounting for 25%-35% of variability, of which a large fraction is beneficial. This work suggests that epistasis both constrains, through negative epistasis, and enlarges, through positive epistasis, the set of possible evolutionary paths that can produce high-affinity sequences during repeated rounds of mutation and selection.

The application of microseed matrix screening to the crystallization of related antibodies in complex with IL-13 is described. Both self-seeding or cross-seeding helped promote nucleation and increase the hit rate., The application of microseed matrix screening to the crystallization of antibody–antigen complexes is described for a set of antibodies that include mouse anti-IL-13 antibody C836, its humanized version H2L6 and an affinity-matured variant of H2L6, M1295. The Fab fragments of these antibodies were crystallized in complex with the antigen human IL-13. The initial crystallization screening for each of the three complexes included 192 conditions. Only one hit was observed for H2L6 and none were observed for the other two complexes. Matrix self-microseeding using these microcrystals yielded multiple hits under various conditions that were further optimized to grow diffraction-quality H2L6 crystals. The same H2L6 seeds were also successfully used to promote crystallization of the other two complexes. The M1295 crystals appeared to be isomorphous to those of H2L6, whereas the C836 crystals were in a different crystal form. These results are consistent with the concept that the conditions that are best for crystal growth may be different from those that favor nucleation. Microseed matrix screening using either a self-seeding or cross-seeding approach proved to be a fast, robust and reliable method not only for the refinement of crystallization conditions but also to promote crystal nucleation and increase the hit rate.

The effects of internal dynamics in diffusion-driven encounters between macro-molecules represent a problem of broad relevance in molecular biology. In this view, we investigate a typical antigen-antibody reaction chain, based on a coarse-grained mechanical model parameterized directly upon results from single-molecule experiments. We demonstrate that the internal dynamics is a crucial factor in the encounter process. To describe our numerical results, we formulate a simple, intuitive theoretical framework, and we develop it analytically. This enables us to show that the inner dynamics of antibody molecules results in a cooperative behavior of their individual sub-units. Along the same lines, we also investigate the case of double binding to multi-valent antigens. Our results quantify the enhancement of avidity afforded by the double binding in excellent agreement with the available experimental data.

The use of the atomic force microscope to measure antibody and antigen interactions is not new and, in theory, allows the detection of single antibody–antigen interactions with nanometre resolution. However the application of this technique has been limited to ‘ideal’ situations and not been applied as a general laboratory technique. Previous studies on more realistic samples have been hampered by non-specific interactions. The technique has been modified with a double attachment spacer molecule between tip and antibody in order to successfully reduce the number of non-specific events seen and improving the sensitivity of the technique. This technique has been applied to an identical sample used for immunofluorencese and electron microcopy investigations. Antibody–antigen interactions were readily detected on the sample with a resolution of 50–100 nm. The presence of specific interactions was confirmed by blocking using excess antibodies.

We present an updated and integrated version of our widely used protein–protein docking and binding affinity benchmarks. The benchmarks consist of non-redundant, high-quality structures of protein–protein complexes along with the unbound structures of their components. Fifty-five new complexes were added to the docking benchmark, 35 of which have experimentally measured binding affinities. These updated docking and affinity benchmarks now contain 230 and 179 entries, respectively. In particular, the number of antibody–antigen complexes has increased significantly, by 67% and 74% in the docking and affinity benchmarks, respectively. We tested previously developed docking and affinity prediction algorithms on the new cases. Considering only the top 10 docking predictions per benchmark case, a prediction accuracy of 38% is achieved on all 55 cases and up to 50% for the 32 rigid-body cases only. Predicted affinity scores are found to correlate with experimental binding energies up to r = 0.52 overall and r = 0.72 for the rigid complexes.

A surface plasmon resonance (SPR) biosensor technology has recently been applied biochemically and clinically to the study of immunologic recognition and the evaluation of binding parameters for various interactions between antibodies (Abs) and antigens (Ags) at liquid-solid interface. The simple interaction between hapten and Ab fragment, e.g., variable single-chain fragment and antigen- binding fragment, can be described sufficiently by a 1:1 stoichiometry in SPR. However, the determination of the binding constant of an anti-protein Ab is usually complicated by the multivalence of the protein Ag. The SPR-based method enables direct determination of binding constants for a variety of specific Ab-Ag interactions in real-time. It also allows estimation of the binding stoichiometry and binding ratio for low-, intermediate-, and high-affinity Ab-Ag interaction systems. The present review is designed to indicate the theo- retical background of SPR-based biosensor technology as well as to present the great variety of measurement modes of interaction kinet- ics that can be performed with these techniques. Quantitative aspects of the Ab-Ag interaction kinetics are reviewed, focusing especially on mono- and multi-valent Ab-Ag interaction modes using a SPR biosensor. Four model binding systems developed recently for use with SPR biosenser are described with principles and examples: (i) one to one interaction mode, (ii) nonequivalent two-site interaction mode, (iii) multiple equivalent-site interaction mode and (iv) multisite interaction mode. This article closes with two descriptions of the deter- minations of the binding stoichiometry and maximum binding ratio of Ab-Ag interactions.

In clinical practices, the examination of pentamer C-reactive protein (pCRP) is commonly used as a prognostic indicator of the risk of a patient developing cardiovascular disease (CVD). Structural modification of pCRP produces a modified CRP (mCRP) which exhibits different biological activities in the body. In recent years, mCRP has come to be regarded as a more powerful inducer than pCRP, and hence mCRP measurement has emerged as an important indicator for assessing the risk of developing CVD. The surface plasmon resonance (SPR) biosensing technique can be employed to increase the detection accuracy and real-time response when sensing pCRP or mCRP. In this study, three monoclonal antibodies (Mabs), C8, 8D8, and 9C9, are immobilized on a protein G layer for subsequent CRP detection. The experimental results reveal that the Mab C8 reacts with both pCRP and mCRP, the Mab 8D8 with pCRP, and the Mab 9C9 with mCRP. No false signals caused by non-specific binding are observed. When detecting pCRP using Mab C8, the SPR bioassay provides sufficient sensitivity to evaluate whether or not a patient is at risk of developing CVD. SPR biosensing provides a viable and accurate approach for the real-time evaluation of pCRP and mCRP levels, and is therefore of considerable benefit in clinical examinations of CPR.

It is established that the use of modern technologies allowed not only to establish the nature of complications of liver echinococcosis, but also to clarify the relation of the cyst to the anatomical formations of the liver and the functional reserve capabilities of the liver. The clinical significance of serological studies based on the phenomenon of antibody antigen reaction in recurrent echinococcosis was studied. Differential radiation diagnosis of echinococcosis and other liquid focal liver lesions was developed and refined.

Self-assembled monolayer (SAM) of n-alkanethiols of different chain lengths (n = 2, 3, 6, 11, 16) on a gold surface are used to immobilize antibodies which in turn bind to a antigen. The antibody and antigen used in this study have similar molecular weights i.e. ?150 kDa. The antibody [1.5 ?g cm-2] immobilized varied with the surface packing density of the SAM of carboxylic acid-terminated n-alkanethiols of different lengths. In comparison, the efficiency of antibody immobilization was lowest on the loosely packed SAM of n-alkanethiols (n ? 3) and the highest on the densely packed SAM of n-alkanethiols (n ? 11). However, increased immobilization of antibodies with increasing chain length of the n-alkanethiols [n > 11], did not result in a corresponding increase in antigen binding. An attempt to explain this phenomenon based on packing density and an orientation of the captured antibody is presented. � 2015 The Royal Society of Chemistry.

s should provide enough information to initiate discussion of the application. It is important to note, however, that great care must be exercised when using other Internet resources which may not be directly associated with the primary literature.

Modelled structures of the acetylcholine receptor-mimicking antibody, Malpha2-3, both free and bound to its antigen, toxin alpha, are assessed in the light of new experimental mutational data from functional mapping of the paratopic region of Malpha2-3. The experimental results are consistent with the previously-predicted structure of the free antibody, and also demonstrate that structural particularities of the Malpha2-3 combining site that were identified in the models play a role in the protein association. The modelled conformations of the hypervariable loops are discussed in the context of recent new data and analyses. The new mutational data allow several previously-considered modelled structures of the complex to be rejected. Two quite similar models now remain.

Empirical residue–residue pair potentials are used to screen possible complexes for protein–protein dockings. A correct docking is defined as a complex with not more than 2.5 A root-mean-square distance from the known experimental structure. The complexes were generated by “ftdock” (Gabb et al. J Mol Biol 1997;272:106–120) that ranks using shape complementarity. The complexes studied were 5 enzyme-inhibitors and 2 antibody-antigens, starting from the unbound crystallographic coordinates, with a further 2 antibody-antigens where the antibody was from the bound crystallographic complex. The pair potential functions tested were derived both from observed intramolecular pairings in a database of nonhomologous protein domains, and from observed intermolecular pairings across the interfaces in sets of nonhomologous heterodimers and homodimers. Out of various alternate strategies, we found the optimal method used a mole-fraction calculated random model from the intramolecular pairings. For all the systems, a correct docking was placed within the top 12% of the pair potential score ranked complexes. A combined strategy was developed that incorporated “multidock,” a side-chain refinement algorithm (Jackson et al. J Mol Biol 1998;276:265–285). This placed a correct docking within the top 5 complexes for enzyme-inhibitor systems, and within the top 40 complexes for antibody–antigen systems. Proteins 1999;35:364–373. © 1999 Wiley-Liss, Inc.

Antibodies are a class of proteins indispensable for the vertebrate immune system. The general architecture of all antibodies is very similar, but they contain a hypervariable region which allows millions of antibody variants to exist, each of which can bind to different molecules. This binding malleability means that antibodies are an increasingly important category of biopharmaceuticals and biomarkers. We present Antibody i-Patch, a method that annotates the most likely antibody residues to be in contact with the antigen. We show that our predictions correlate with energetic importance and thus we argue that they may be useful in guiding mutations in the artificial affinity maturation process. Using our predictions as constraints for a rigid-body docking algorithm, we are able to obtain high-quality results in minutes. Our annotation method and re-scoring system for docking achieve their predictive power by using antibody-specific statistics. Antibody i-Patch is available from http://www.stats.ox.ac.uk/research/proteins/resources.

Recently there has been a great develop of new antibodies immobilization procedures, that keep antibodies to retain their orientation and functionality after the binding to a solid support. This allows the formation of immune-complexes useful for the detection of biomarkers from biological samples. We have developed a new method of functionalization for solid substrates that involves an initial surface activation, then a functionalization by means of 3-aminopropyltriethoxysilane-followed by another functionalization step with a layer of very small peptides, which have a high affinity to the Antibody Fc portion, acting as antibody linkers. These antibody binding peptides can immobilize the antibodies with a proper configuration that allows an unambiguous detection of antibody-antigen complexes by means of atomic force microscopy (AFM). The AFM can act as a powerful label free detection technique, which allows to detect, in principle, single molecule interactions, with the only limitation to use substrate with low-roughness surfaces; in this case, the roughness can be interpreted as background noise in the AFM analysis. Moreover, our functionalization method can be used to obtain bioactive surfaces on a wide range of solid supports, making them capable to suitably immobilize the antibodies for the antigenic binding.

Motivation: Antibodies or immunoglobulins are proteins of paramount importance in the immune system. They are extremely relevant as diagnostic, biotechnological and therapeutic tools. Their modular structure makes it easy to re-engineer them for specific purposes. Short of undergoing a trial and error process, these experiments, as well as others, need to rely on an understanding of the specific determinants of the antibody binding mode. Results: In this article, we present a method to identify, on the basis of the antibody sequence alone, which residues of an antibody directly interact with its cognate antigen. The method, based on the random forest automatic learning techniques, reaches a recall and specificity as high as 80% and is implemented as a free and easy-to-use server, named prediction of Antibody Contacts. We believe that it can be of great help in re-design experiments as well as a guide for molecular docking experiments. The results that we obtained also allowed us to dissect which features of the antibody sequence contribute most to the involvement of specific residues in binding to the antigen. Availability: http://www.biocomputing.it/proABC. Contact: anna.tramontano@uniroma1.it or paolo.marcatili@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.