Exploring the dynamic range of the kinetic exclusion assay in characterizing antigen-antibody interactions

Therapeutic antibodies are often engineered or selected to have high on-target binding affinities that can be challenging to determine precisely by most biophysical methods. Here, we explore the dynamic range of the kinetic exclusion assay (KinExA) by exploiting the interactions of an anti-DKK antibody with a panel of DKK antigens as a model system. By tailoring the KinExA to each studied antigen, we obtained apparent equilibrium dissociation constants (K(D) values) spanning six orders of magnitude, from approximately 100 fM to 100 nM. Using a previously calibrated antibody concentration and working in a suitable concentration range, we show that a single experiment can yield accurate and precise values for both the apparent K(D) and the apparent active concentration of the antigen, thereby increasing the information content of an assay and decreasing sample consumption. Orthogonal measurements obtained on Biacore and Octet label-free biosensor platforms further validated our KinExA-derived affinity and active concentration determinations. We obtained excellent agreement in the apparent affinities obtained across platforms and within the KinExA method irrespective of the assay orientation employed or the purity of the recombinant or native antigens.     

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

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

Quantitation of protein-protein interactions by thermal stability shift analysis

Thermal stability shift analysis is a powerful method for examining binding interactions in proteins. We demonstrate that under certain circumstances, protein-protein interactions can be quantitated by monitoring shifts in thermal stability using thermodynamic models and data analysis methods presented in this work. This method relies on the determination of protein stabilities from thermal unfolding experiments using fluorescent dyes such as SYPRO Orange that report on protein denaturation. Data collection is rapid and straightforward using readily available real-time polymerase chain reaction instrumentation. We present an approach for the analysis of the unfolding transitions corresponding to each partner to extract the affinity of the interaction between the proteins. This method does not require the construction of a titration series that brackets the dissociation constant. In thermal shift experiments, protein stability data are obtained at different temperatures according to the affinity- and concentration-dependent shifts in unfolding transition midpoints. Treatment of the temperature dependence of affinity is, therefore, intrinsic to this method and is developed in this study. We used the interaction between maltose-binding protein (MBP) and a thermostable synthetic ankyrin repeat protein (Off7) as an experimental test case because their unfolding transitions overlap minimally. We found that MBP is significantly stabilized by Off7. High experimental throughput is enabled by sample parallelization, and the ability to extract quantitative binding information at a single partner concentration. In a single experiment, we were able to quantify the affinities of a series of alanine mutants, covering a wide range of affinities (～ 100 nM to ～ 100 μM).     

Additive method for the prediction of protein-peptide binding affinity. Application to the MHC class I molecule HLA-A*0201

A method has been developed for prediction of binding affinities between proteins and peptides. We exemplify the method through its application to binding predictions of peptides with affinity to major histocompatibility complex class I molecule HLA-A*0201. The method is named "additive" because it is based on the assumption that the binding affinity of a peptide could be presented as a sum of the contributions of the amino acids at each position and the interactions between them. The amino acid contributions and the contributions of the interactions between adjacent side chains and every second side chain were derived using a partial least squares (PLS) statistical methodology using a training set of 420 experimental IC50 values. The predictive power of the method was assessed using rigorous cross-validation and using an independent test set of 89 peptides. The mean value of the residuals between the experimental and predicted pIC50 values was 0.508 for this test set. The additive method was implemented in a program for rapid T-cell epitope search. It is universal and can be applied to any peptide-protein interaction where binding data is known.     

Real-time analysis of antibody interactions with whole enveloped human cytomegalovirus using surface plasmon resonance

Human cytomegalovirus (CMV) is a large enveloped virus that encodes multiple glycoproteins required for virus-cell binding and fusion. To assess the binding properties of antibodies with target glycoprotein in a natural context of infection, we investigated the feasibility of using the surface plasmon resonance (SPR) technique for studying the direct binding of antibodies with CMV virions. Direct immobilization of whole virions to sensor surface and a surface regeneration procedure allowed for quantitative and reproducible measurements of binding affinity and binding kinetics of antibody–whole virion interactions. The conformational and functional integrity of viral particles was not compromised by the regeneration condition as evaluated with antibodies recognizing conformational epitopes and by electron microscopy. Binding of an irrelevant antibody was not observed, indicating the high specificity of the method. A panel of anti-gB antibodies was measured and the binding affinities correlated fairly well with those determined by ELISA. These data demonstrated that the interaction of anti-gB antibody with whole virion of large enveloped CMV can be quantitatively studied using SPR. This method has been successfully applied for screening and selection of anti-CMV antibodies and can be potentially extended to study antibody–glycoprotein interactions of other related herpesviruses.     

Directed evolution of an anti-prion protein scFv fragment to an affinity of 1 pM and its structural interpretation

Bovine spongiform encephalopathy (BSE) is a fatal neurodegenerative prion disease affecting cattle that is transmissible to humans, manifesting as a variant of Creutzfeldt-Jakob disease (vCJD) likely following the consumption of meat contaminated with BSE prions. High-affinity antibodies are a prerequisite for the development of simple, highly sensitive and non-invasive diagnostic tests that are able to detect even small amounts of the disease-associated PrP conformer (PrP(Sc)). We describe here the affinity maturation of a single-chain Fv antibody fragment with a binding affinity of 1 pM to a peptide derived from the unstructured region of bovine PrP (BoPrP (90-105)). This is the tightest peptide-binding antibody reported to date and may find useful application in diagnostics, especially when PrP(Sc) is pretreated by denaturation and/or proteolysis for peptide-like presentation. Several rounds of directed evolution and off-rate selection with ribosome display were performed using an antibody library generated from a single PrP binder with error-prone PCR and DNA-shuffling. As the correct determinations of affinities in this range are not straightforward, competition biosensor techniques and KinExA methods were both applied and compared. Structural interpretation of the affinity improvement was performed based on the crystal structure of the original prion binder in complex with the BoPrP (95-104) peptide by modeling the corresponding mutations.     

An efficient computational method for calculating ligand binding affinities

Virtual compound screening using molecular docking is widely used in the discovery of new lead compounds for drug design. However, the docking scores are not sufficiently precise to represent the protein-ligand binding affinity. Here, we developed an efficient computational method for calculating protein-ligand binding affinity, which is based on molecular mechanics generalized Born/surface area (MM-GBSA) calculations and Jarzynski identity. Jarzynski identity is an exact relation between free energy differences and the work done through non-equilibrium process, and MM-GBSA is a semimacroscopic approach to calculate the potential energy. To calculate the work distribution when a ligand is pulled out of its binding site, multiple protein-ligand conformations are randomly generated as an alternative to performing an explicit single-molecule pulling simulation. We assessed the new method, multiple random conformation/MM-GBSA (MRC-MMGBSA), by evaluating ligand-binding affinities (scores) for four target proteins, and comparing these scores with experimental data. The calculated scores were qualitatively in good agreement with the experimental binding affinities, and the optimal docking structure could be determined by ranking the scores of the multiple docking poses obtained by the molecular docking process. Furthermore, the scores showed a strong linear response to experimental binding free energies, so that the free energy difference of the ligand binding (ΔΔG) could be calculated by linear scaling of the scores. The error of calculated ΔΔG was within ≈±1.5 kcal•mol⁻¹ of the experimental values. Particularly, in the case of flexible target proteins, the MRC-MMGBSA scores were more effective in ranking ligands than those generated by the MM-GBSA method using a single protein-ligand conformation. The results suggest that, owing to its lower computational costs and greater accuracy, the MRC-MMGBSA offers efficient means to rank the ligands, in the post-docking process, according to their binding affinities, and to compare these directly with the experimental values.     

High throughput solution-based measurement of antibody-antigen affinity and epitope binning

Advances in human antibody discovery have allowed for the selection of hundreds of high affinity antibodies against many therapeutically relevant targets. This has necessitated the development of reproducible, high throughput analytical techniques to characterize the output from these selections. Among these characterizations, epitopic coverage and affinity are among the most critical properties for lead identification. Biolayer interferometry (BLI) is an attractive technique for epitope binning due to its speed and low antigen consumption. While surface-based methods such as BLI and surface plasmon resonance (SPR) are commonly used for affinity determinations, sensor chemistry and surface related artifacts can limit the accuracy of high affinity measurements. When comparing BLI and solution equilibrium based kinetic exclusion assays, significant differences in measured affinity (10-fold and above) were observed. KinExA direct association (k_a) rate constant measurements suggest that this is mainly caused by inaccurate k_a measurements associated with BLI related surface phenomena. Based on the kinetic exclusion assay principle used for KinExA, we developed a high throughput 96-well plate format assay, using a Meso Scale Discovery (MSD) instrument, to measure solution equilibrium affinity. This improved method combines the accuracy of solution-based methods with the throughput formerly only achievable with surface-based methods.     

Integration of cell-free protein coexpression with an enzyme-linked immunosorbent assay enables rapid analysis of protein-protein interactions directly from DNA

Assays that integrate detection of binding with cell-free protein expression directly from DNA can dramatically increase the pace at which protein-protein interactions (PPIs) can be analyzed by mutagenesis. In this study, we present a method that combines in vitro protein production with an enzyme-linked immunosorbent assay (ELISA) to measure PPIs. This method uses readily available commodity instrumentation and generic antibody-affinity tag interactions. It is straightforward and rapid to execute, enabling many interactions to be assessed in parallel. In traditional ELISAs, reporter complexes are assembled stepwise with one layer at a time. In the method presented here, all the members of the reporter complex are present and assembled together. The signal strength is dependent on all the intercomponent interaction affinities and concentrations. Although this assay is straightforward to execute, establishing proper conditions and analysis of the results require a thorough understanding of the processes that determine the signal strength. The formation of the fully assembled reporter sandwich can be modeled as a competition between Langmuir adsorption isotherms for the immobilized components and binding equilibria of the solution components. We have shown that modeling this process provides semiquantitative understanding of the effects of affinity and concentration and can guide strategies for the development of experimental protocols. We tested the method experimentally using the interaction between a synthetic ankyrin repeat protein (Off7) and maltose-binding protein. Measurements obtained for a collection of alanine mutations in the interface between these two proteins demonstrate that a range of affinities can be analyzed.     

Generation and iterative affinity maturation of antibodies in vitro using hypermutating B-cell lines

We show that iterative antigen-mediated selection of B-cell lines that constitutively hypermutate their immunoglobulin V genes during culture can be exploited to generate antibodies in vitro. From Ramos, a hypermutating human B-cell line expressing IgM of unknown specificity, we derived descendants that exhibit stepwise improved binding to streptavidin. Binding is initially conferred by mutations in complementarity-determining regions (CDRs), but maturation is due to strategic framework mutations. A more powerful system is provided by a hypermutating chicken B-lymphoma line, owing to its rapid proliferation, high rate of mutation accumulation, and genetic tractability. Starting from a single cell, we selected parallel lineages of derivatives, making mutated antibodies of increasing affinity to independent test antigens. Selection is initiated at an exceedingly low affinity threshold, but antibodies can be delivered with nanomolar affinities. The strategy could prove useful for in vitro generation of antigen-specific monoclonal antibodies and may be extendable to the maturation of other protein-ligand interactions.     

Kinetic analysis of unpurified native antigens available in very low quantities and concentrations

Affinity measurements of antigen–antibody interactions are generally performed using known concentrations of purified or recombinant materials. In addition, many technologies that measure affinity require the interacting components to be present in at least microgram quantities. Specifically, if the antigen is either available only in low quantities or unable to be purified, or if the quantity is unknown, then the measurement of affinity can be very difficult. Using the Kinetic Exclusion Assay (KinExA) technology, here we describe a method that overcomes the requirement for large amounts of purified and known quantities of antigen. We used this method to precisely measure the affinity of fully human anti-human interleukin 13 (IL13) monoclonal antibodies to IL13 produced in native form from primary T cells derived from a variety of species, including human. These antigens were available only in the limited quantities present in the conditioned cell culture medium, and the affinity was measured directly without further purification.     

Inhibitor affinity chromatography: profiling the specific reactivity of the proteome with immobilized molecules

An inhibitor affinity chromatography (IAC) method has been developed for the analysis of inhibitor-protein interactions as a complementary approach to two-dimensional electrophoresis for functional proteomics studies. The procedure was developed utilizing a cyclin-dependent kinase 2 (Cdk2) inhibitor coupled to a polymeric resin and validated using a number of proteins interacting with the inhibitor with different specificities. Cdk2 and the other kinases bound and eluted from the resin in accordance with the relative in vitro potency of the inhibitor for each enzyme. Molecular interactions with the Cdk2 inhibitor were compared for HCT116 cancer cells versus rat pancreatic acinar cells. Proteins interacting with the ligand on the IAC matrix were identified by mass spectrometry. Isothermal calorimetry was used to confirm and quantitatively evaluate the binding affinity of some of the interacting proteins. Heat-shock protein (Hsp) 70 and Hsp27 were the strongest interactors with the inhibitor, displaying binding affinities comparable to those of Cdk2. These results support the use of IAC as a general method for the rapid identification and qualitative evaluation of the in vivo targets and potential side effects of a given drug.     

Quantification of interactions between drug leads and serum proteins by use of "binding efficiency"

To develop efficient and reliable methods for prediction of serum protein binding of drug leads, the kinetic characteristics for the interactions between selected compounds and human serum albumin and α₁-acid glycoprotein have been explored using a surface plasmon resonance biosensor. Conventional methods for quantification of interactions (i.e., using rate constants or affinities determined on the basis of a reasonable mechanistic model) were applicable for only a few of the compounds. The affinity of a primary interaction and the contribution of lower affinity secondary interactions could be estimated for some compounds, but the affinity of many compounds could not be quantified by either of these methods. To have a quantification method that could be used for all compounds, independent of affinity and complexity of interaction mechanisms, the concept of “binding efficiency,” analogous to “catalytic efficiency” used for enzymes, was developed. It allowed the quantification of the binding of compounds interacting with weak affinity and for which saturation is not reached within a concentration range where the compound is soluble or when the influence of interactions with secondary sites makes interpretations difficult. In addition, compounds with large fractional binding can be identified by this strategy and simply quantified relative to reference compounds. This approach will enable ranking and identification of structure–activity relationships of compounds with respect to their serum protein binding profile.     

Feature selection and classification of protein–protein complexes based on their binding affinities using machine learning approaches

Protein–protein interactions are intrinsic to virtually every cellular process. Predicting the binding affinity of protein–protein complexes is one of the challenging problems in computational and molecular biology. In this work, we related sequence features of protein–protein complexes with their binding affinities using machine learning approaches. We set up a database of 185 protein–protein complexes for which the interacting pairs are heterodimers and their experimental binding affinities are available. On the other hand, we have developed a set of 610 features from the sequences of protein complexes and utilized Ranker search method, which is the combination of Attribute evaluator and Ranker method for selecting specific features. We have analyzed several machine learning algorithms to discriminate protein‐protein complexes into high and low affinity groups based on their K_d values. Our results showed a 10‐fold cross‐validation accuracy of 76.1% with the combination of nine features using support vector machines. Further, we observed accuracy of 83.3% on an independent test set of 30 complexes. We suggest that our method would serve as an effective tool for identifying the interacting partners in protein–protein interaction networks and human–pathogen interactions based on the strength of interactions. Proteins 2014; 82:2088–2096. © 2014 Wiley Periodicals, Inc.     

Epitope-mapping of transglutaminase with parallel label-free optical detection

The gastrointestinal disorder coeliac disease (CD) is induced by the ingestion of wheat gluten and is characterized by damage of the typical structure of the intestinal mucosa. The enzyme tissue transglutaminase (tTGase) was identified as the major target of disease-specific antibodies in-patients. We performed an epitope fine-mapping with a series of pentadecapeptides synthesized using parallel multiple peptide synthesis. For the detection of biomolecular interactions a label-free parallel method, reflectometric interference spectroscopy (RIfS), was used. This is the first optical label-free method adapted to a high throughput screening (HTS) format and the experimental results demonstrate its applicability as a biological screening device. A high titer of anti-tTGase antibodies is found in the serum of coeliac patients. We have taken the first step towards a fast non-surgical test for the detection of these antibodies. In order to identify and characterize a continuous epitope with high affinity against the anti-tTGase antibody a screening of 21 pentadecapeptides has been accomplished with the parallel RIfS system. A single channel RIfS-system with high resolution was used to determine binding constants of identified peptides with high affinity.     

Synthetic peptide vaccine development: measurement of polyclonal antibody affinity and cross-reactivity using a new peptide capture and release system for surface plasmon resonance spectroscopy

A method has been developed for measurement of antibody affinity and cross-reactivity by surface plasmon resonance spectroscopy using the EK-coil heterodimeric coiled-coil peptide capture system. This system allows for reversible capture of synthetic peptide ligands on a biosensor chip surface, with the advantage that multiple antibody-antigen interactions can be analyzed using a single biosensor chip. This method has proven useful in the development of a synthetic peptide anti-Pseudomonas aeruginosa (PA) vaccine. Synthetic peptide ligands corresponding to the receptor binding domains of pilin from four strains of PA were conjugated to the E-coil strand of the heterodimeric coiled-coil domain and individually captured on the biosensor chip through dimerization with the immobilized K-coil strand. Polyclonal rabbit IgG raised against pilin epitopes was injected over the sensor chip surface for kinetic analysis of the antigen-antibody interaction. The kinetic rate constants, k(on) and k(off), and equilibrium association and dissociation constants, KA and KD, were calculated. Antibody affinities ranged from 1.14 x 10(-9) to 1.60 x 10(-5) M. The results suggest that the carrier protein and adjuvant used during immunization make a dramatic difference in antibody affinity and cross-reactivity. Antibodies raised against the PA strain K pilin epitope conjugated to keyhole limpet haemocyanin using Freund's adjuvant system were more broadly cross-reactive than antibodies raised against the same epitope conjugated to tetanus toxoid using Adjuvax adjuvant. The method described here is useful for detailed characterization of the interaction of polyclonal antibodies with a panel of synthetic peptide ligands with the objective of obtaining high affinity and cross-reactive antibodies in vaccine development.     

Three-dimensional structure-activity relationship modeling of cocaine binding to two monoclonal antibodies by comparative molecular field analysis

Successful immunotherapy of cocaine addiction and overdoses requires cocaine-binding antibodies with specific properties, such as high affinity and selectivity for cocaine. We have determined the affinities of two cocaine-binding murine monoclonal antibodies (mAb: clones 3P1A6 and MM0240PA) for cocaine and its metabolites by [3H]-radioligand binding assays. mAb 3P1A6 (K(d) = 0.22 nM) displayed a 50-fold higher affinity for cocaine than mAb MM0240PA (K(d) = 11 nM) and also had a greater specificity for cocaine. For the systematic exploration of both antibodies' binding specificities, we used a set of approximately 35 cocaine analogues as structural probes by determining their relative binding affinities (RBAs) using an enzyme-linked immunosorbent competition assay. Three-dimensional quantitative structure-activity relationship (3D-QSAR) models on the basis of comparative molecular field analysis (CoMFA) techniques correlated the binding data with structural features of the ligands. The analysis indicated that despite the mAbs' differing specificities for cocaine, the relative contributions of the steric (approximately 80%) and electrostatic (approximately 20%) field interactions to ligand-binding were similar. Generated three-dimensional CoMFA contour plots then located the specific regions about cocaine where the ligand/receptor interactions occurred. While the overall binding patterns of the two mAbs had many features in common, distinct differences were observed about the phenyl ring and the methylester group of cocaine. Furthermore, using previously published data, a 3D-QSAR model was developed for cocaine binding to the dopamine reuptake transporter (DAT) that was compared to the mAb models. Although the relative steric and electrostatic field contributions were similar to those of the mAbs, the DAT cocaine-binding site showed a preference for negatively charged ligands. Besides establishing molecular level insight into the interactions that govern cocaine binding specificity by biopolymers, the three-dimensional images obtained reflect the properties of the mAbs binding pockets and provide the initial information needed for the possible design of novel antibodies with properties optimized for immunotherapy.     

How Structure Defines Affinity in Protein-Protein Interactions

Protein-protein interactions (PPI) in nature are conveyed by a multitude of binding modes involving various surfaces, secondary structure elements and intermolecular interactions. This diversity results in PPI binding affinities that span more than nine orders of magnitude. Several early studies attempted to correlate PPI binding affinities to various structure-derived features with limited success. The growing number of high-resolution structures, the appearance of more precise methods for measuring binding affinities and the development of new computational algorithms enable more thorough investigations in this direction. Here, we use a large dataset of PPI structures with the documented binding affinities to calculate a number of structure-based features that could potentially define binding energetics. We explore how well each calculated biophysical feature alone correlates with binding affinity and determine the features that could be used to distinguish between high-, medium- and low- affinity PPIs. Furthermore, we test how various combinations of features could be applied to predict binding affinity and observe a slow improvement in correlation as more features are incorporated into the equation. In addition, we observe a considerable improvement in predictions if we exclude from our analysis low-resolution and NMR structures, revealing the importance of capturing exact intermolecular interactions in our calculations. Our analysis should facilitate prediction of new interactions on the genome scale, better characterization of signaling networks and design of novel binding partners for various target proteins.     

Estrogen receptor-ligand complexes measured by chip-based nanoelectrospray mass spectrometry: an approach for the screening of endocrine disruptors

In the present report, a method based on chip-based nanoelectrospray mass spectrometry (nanoESI-MS) is described to detect noncovalent ligand binding to the human estrogen receptor alpha ligand-binding domain (hERalpha LBD). This system represents an important environmental interest, because a wide variety of molecules, known as endocrine disruptors, can bind to the estrogen receptor (ER) and induce adverse health effects in wildlife and humans. Using proper experimental conditions, the nanoESI-MS approach allowed for the detection of specific ligand interactions with hERalpha LBD. The relative gas-phase stability of selected hERalpha LBD-ligand complexes did not mirror the binding affinity in solution, a result that demonstrates the prominent role of hydrophobic contacts for stabilizing ER-ligand complexes in solution. The best approach to evaluate relative solution-binding affinity by nanoESI-MS was to perform competitive binding experiments with 17beta-estradiol (E2) used as a reference ligand. Among the ligands tested, the relative binding affinity for hERalpha LBD measured by nanoESI-MS was 4-hydroxtamoxifen approximately diethylstilbestrol > E2 > genistein > bisphenol A, consistent with the order of the binding affinities in solution. The limited reproducibility of the bound to free protein ratio measured by nanoESI-MS for this system only allowed the binding constants (K(d)) to be estimated (low nanomolar range for E2). The specificity of nanoESI-MS combined with its speed (1 min/ligand), low sample consumption (90 pmol protein/ligand), and its sensitivity for ligand (30 ng/mL) demonstrates that this technique is a promising method for screening suspected endocrine disrupting compounds and to qualitatively evaluate their binding affinity.