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Estimating the duration of natural immunity induced by SARS-CoV2 infection is crucial in health policy strategies. A patient infected by the SARS-CoV2 quickly produces three antibody isotypes IgM, IgG, and IgA that reveal an infection. In this paper, we use a Bayesian twocomponent mixture of random coefficient model to capture the longitudinal/temporal evolution of antibody levels, as well as viral neutralization on the dataset reported by Seow et al. in [1]. We observe that the more severe the symptoms, the more intense antibodies and immunity responses. And their decline is decelerated with the severity. Moreover, it appears that viral neutralization is best predicted by the level of IgM or IgA antibody, rather than by IgG level. Furthermore, our model is particularly suitable to estimate the Probability of being Out of Detection. Thus, we observe that although antibodies persist for up to 5 months in the plasma, the probability of becoming undetectable exceeds 50% after 3 months.

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.

Sensors capable of detecting different types of biomolecules have widespread applications in the field of biomedical research, but despite many years of research, the development of biosensors suitable for point-of-care (POC) applications in resource-limited areas is still extremely challenging. Sensors based on photonic crystal hydrogels (PCHs) hold much promise in this regard because of their numerous advantages over other existing bioanalytical methods. All current PCH biosensors are however restricted in the types of analytes they can detect sensitively with good selectivity. By taking advantage of the powerful and ubiquitous antibody-antigen interaction, we report herein the first-ever competition-based PCH biosensors capable of naked-eye detection of various biomolecules (e.g., proteins, peptides, and small molecules) with high sensitivity and selectivity and minimal background and excellent reversibility. We showed such PCH designs could be extended to the fabrication of different enzyme-detecting biosensors. The universal feature of these novel biosensors thus enables future development of POC biosensors in disease diagnostics for other bioanalytes.

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

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.

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.

The article has been withdrawn at the request of the author and the editor of the journal Coronaviruses, due to incoherent language. Bentham Science apologizes to the readers of the journal for any inconvenience this may have caused. The Bentham Editorial Policy on Article Withdrawal can be found at https://benthamscience.com/editorial-policies-main.php Bentham Science Disclaimer: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submit- ting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript, the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.

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.

We studied the molecular details of the recognition of antigens by the variable domain of their cognate antibodies in as well as those elicited by the constant domains, which do not directly interact with antigens. Such effects are difficult to study experimentally; however, molecular dynamics simulations and subsequent residue interaction network analysis provide insight into the allosteric communication between the antigen-binding CDR region and the constant domain. We performed MD simulations of the complex of Fab and prion-associated peptide in the apo and bound forms and follow the conformational changes in the antibody and cross-talk between its subunits and with antigens. These protocols could be generally applied for studies of other antigens-antibody recognition systems.

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.

Antibody-antigen interactions are complex events central to immune response, in vivo and in vitro diagnostics, and development of therapeutic substances. We developed an ultrastable single-molecule localized surface plasmon resonance (LSPR) sensing platform optimized for studying antibody-antigen interaction kinetics over very long time scales. The setup allowed us to perform equilibrium fluctuations analysis of the PEG/anti-PEG interaction. By time and frequency domain analysis, we demonstrate that reversible adsorption of monovalently bound anti-PEG antibodies is the dominant factor affecting the LSPR fluctuations. The results suggest that equilibrium fluctuation analysis can be an alternative to established methods for determination of interaction rates. In particular, the methodology is suited to analyze molecular systems whose properties change during the initial interaction phases, for example, due to mass transport limitations or, as demonstrated here, because the effective association rate constant varies with surface concentration of adsorbed molecules.

We present a case series of seven patients with suspected cold agglutinin antibodies, discovered after initiation of bypass. Laboratory analysis of blood samples intraoperatively determined the cause of the aggregation to be rouleaux formation in three of the patients and cold agglutinins in the other four.

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.

Artificial receptor-like structures such as molecular imprinted polymers (MIPs) are biomimetic molecules are used to replicate target specific antibody-antigen mechanism. In MIPs, selective binding of template molecule can be significantly correlated with lock and key mechanism, which play a major role in the drug delivery mechanism. The MIPs are biocompatible with high efficiency and are considered in several drug delivery and biosensor applications besides continuous and controlled drug release leading to better therapeutics. There is a need to explore the potential synthetic methods to improve MIPs with respect to the imprinting capacity in cancer therapeutics. In this review, we focus on MIPs as drug delivery mechanism in cancer and the challenges related to their synthesis and applications.

Surface Second Harmonic Generation (SSHG) is an optical non-linear analytical technique that has been applied to various biological and non-biological systems. Its application to biological systems has been limited to imaging and studies of molecular structures. However, its potential as a label-free method to detect bio-molecular interactions was not thoroughly investigated. This study explored, probed and evaluated the potential of SSHG as a viable biosensing technique.

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.

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 to improve our ability of designing efficient biological drugs. Computational approaches such as molecular docking are providing a valuable and fast alternative to experimental structural characterization for those complexes. We investigate here how information about complementary determining regions and binding epitopes can be used to drive the modelling process and present a comparative study of four different docking software (ClusPro, LightDock, ZDOCK and HADDOCK) providing specific options for antibody-antigen modelling. 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 both in the presence and absence of information about the epitope on the antigen.

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.

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.

Understanding the single molecular protein-protein interaction has great significance in evaluating the affinity of a specific antibody. Herein, the interaction between single α-fetal protein (AFP) and its antibody was monitored via transient ionic current recording by using the antibody functionalized nanopore sensors. More importantly, the kinetic evaluation was performed at the single molecule level to determine the dissociation constant of this interaction. This method enables the monitoring of the kinetic antigen-antibody interaction in their heterogenetic state without any labelling. Our results provided new insights into the evaluation of the antibody's binding affinity and more into the development of immunoassays for diagnostics.

Structure prediction of antibody-antigen complexes, which involves molecular docking to generate decoys that are ranked using a scoring function, is an important approach in the design of antibody drugs and biosensors. However, it is not easy to evaluate the stability of protein-protein complexes, using a single scoring function. Here, we developed a prediction method of antibody-antigen complex structures using the docking engine "surFit" and a scoring function (GBSA score) that combined the generalized Born (GB) energy and the hydration energy based on the solvent-accessible surface area (SA). We chose 95 antibody-antigen structural datasets for self-docking and generated many decoy structures using the surFit program. The GBSA scores were computed for all of the decoys, and the area under the curve (AUC) of the GBSA scores yielded a higher value (0.972) than the values obtained by the original surFit scores (0.873) and the ZRANK scores (0.953). To improve the accuracy of prediction, molecular dynamics (MD) simulations were performed for several decoy structures that had good GBSA scores. Consequently, average GBSA scores from MD trajectories can reduce the number of non-native decoys that have GBSA scores competitive with the near-native ones.

Measurement in liquid media is a major challenge in real-time detection using resonant cantilevers. This is addressed in the present study by fabricating sub-micron thick cantilevers followed by functionalization for biomolecule detection. The fabricated cantilever resonator beams of thickness 165 nm were used for measurements in two systems: (i) human immunoglobulin (HIgG) as the antibody on the cantilever sensing mouse immunoglobulin (MIgG) as corresponding antigen, and (ii) detection of triglyceride (TG) based on the enzymatic hydrolysis with lipase, using tributyrin as a model. In both cases, the beams were functionalized for covalent bonding of the protein receptor. The label-free detection was carried out by measuring the shift in resonance frequency at higher modes, using a laser Doppler vibrometer in liquid and in air. The calibration showed a linear correlation between the bioanalyte concentration and change in the resonance frequency. Notably, detection of antigen mass as low as 434 ± 59fg and triglyceride concentration in the nM range with limit of detection as 7 nM in liquid interface was achieved, greatly improving the sensitivity of bioanalyte detection in liquid samples. Although frequency-based methods are highly sensitive, the issues with measurement liquid medium limit their application. In the present report, these issues were addressed by fabricating sub-micron thick cantilever beam, choosing an appropriate functionalization method without affecting the sensitivity, and measurement at higher modes. These have resulted in circumventing issues like damping and hydrodynamic loading thus improving its potential as real-time sensor.

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.

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.

Antibodies are medically and experimentally important immune system proteins. By binding to foreign molecules with high specificity and affinity, they act as flags that guide the immune response. The high-quality binding characteristics of antibodies have led to their widespread use as both experimental reagents and therapeutic molecules. Due to their importance, the interactions between antibodies and antigens has been an ongoing area of research. Here, a non-redundant database of 492 antibody – protein complexes was studied. Using the CHARMM and Amber force fields, the energies of the complexes were minimized and the interaction energies of individual residues were determined. This revealed that the percent contributions to binding energy of amino acids follows an exponential decay, with only a few residues being responsible for most of the energy.

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.

Antibody-antigen interactions are vital in immunoassay development and can determine detection limits and analysis times. Capillary electrophoresis (CE) is a powerful technique that can be used to quantify antibody-antigen interactions. These CE methods range from simple separations of a premixed antibody and antigen sample applied as a short plug to allow for separation of complex, free antibody, and free antigen to more complex systems which inject complexed samples in the presence of antibody or antigen; or even injections of antibody and antigen sequentially. The objective of this review is to identify and describe various CE techniques which have been used to study antibody-antigen interactions. A brief discussion of linear and nonlinear curve fitting is also included.

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’.

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.

Hand, Foot and mouth disease (HFMD) is a common disease with high infectivity for children, and enterovirus 71 (EV71) is one of the main pathogens to cause the type of illness. Therefore, the aim of this study was to propose a rapid and effective technique for detecting EV71 directly based on the mechanism of biological intermolecular force by using atomic force microscopy (AFM). At first, we coated EV71 particles on the mica surface and made the EV71 antibodies (anti-EV71) fixed on the AFM tip by means of several chemical procedures. Then, AFM chemically modified tip was applied to measure the unbinding forces between EV71 and anti-EV71 by contact mode. Finally, by using AFM imaging calculating software, the EV71 particle size (mean±SD) was 31.36±3.87 nm (n = 200) and this result was concordance with previous literature. Besides, the force (mean±SD) between EV71 antigen and antibody complex was 336.9±64.7 pN. The force (mean±SD) between anti-EV71 and non-specific specimens was 47.1±15.1 pN and was significantly smaller (P < 0.05). Apparently, the results show that we can precisely identify EV71 infection among the samples by measuring the force magnitude and observing the occurrence of EV71/anti-EV71 unbinding events. Therefore, the combination of AFM system and the chemically modified tip has the potential to be a rapid and effective method for detecting EV71 directly.

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.

Antibody–antigen complexes have a natural protein-protein interface whose study illuminates how one molecule can be designed to bind to another. Moreover, antibodies evolve naturally to improve specificity and affinity, so we can observe the effect of different choices in protein sequence. We will see that we must use all of the ideas developed so far to assess such interfaces completely.

Immunohistochemistry (IHC) is a method of routine use in histopathology for the diagnosis and differentiated diagnosis of liver diseases. IHC staining in human hepatic tissue is often obstructed by the presence of non-specific antibody interactions, the cause of high background staining. Cognizant of previous encounters with high levels of background in human hepatic tissue when using polyclonal antibodies, the potential causes were systemattically investigated by testing the impact of different antibody variants and assay conditions. Firstly, IHC was performed on hepatic tissue with immunized and non-immunized polyclonal antibodies and it was demonstrated that immunized antibodies pervaded the samples with non-specific staining, while non-immunized antibodies produced insignificant background. Following, the primary antibody was co-incubated with different concentrations of protein blocks, acid/base solutions, crystalline ionic compounds, and combinations of these individual components in order t...