Langmuir Binding Model Research Articles

Energetics of β-lactoglobulin–flavor compounds interactions

Interaction of bovine β-lactoglobulin (BLG) with several flavor compounds (FC) (2–methylpyrazine, vanillin, 2–acetylpyridine, 2– and 3–acetylthiophene, methyl isoamyl ketone, heptanone, octanone, and nonanone) was studied by high-sensitivity differential scanning calorimetry. The denaturation temperature, enthalpy, and heat capacity increment were determined at different FC concentrations. It was found that the denaturation temperature and heat capacity increment do not depend on the FC concentration, while the denaturation enthalpy decreases linearly with the FC concentration. These thermodynamic effects disclose the preferential FC binding to the unfolded form of BLG. By the obtained calorimetric data, the free energies of FC binding vs. the FC concentrations were calculated. These dependences were shown to be linear. Their slope relates closely to the overall FC affinity for the unfolded BLG in terms of the Langmuir binding model. The overall BLG affinity for FC varies from 20 M–1 (2-methylpyrazine) up to 360 M–1(nonanone). The maximal stoichiometry of the BLG–FC complexes was roughly estimated as a ratio of the length of the unfolded BLG to the molecular length of FC. Using these estimates, the apparent BLG–FC binding constants were determined. They are in the range of 0.3–8.0 M–1 and correlated strictly with the FC lipophilicity descriptor (logP).

Revealing the SARS-CoV-2 Spike Protein and Specific Antibody Immune Complex Formation Mechanism for Precise Evaluation of Antibody Affinity.

The profound understanding and detailed evaluation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (SCoV2-S) protein and specific antibody interaction mechanism is of high importance in the development of immunosensors for COVID-19. In the present work, we studied a model system of immobilized SCoV2-S protein and specific monoclonal antibodies by molecular dynamics of immune complex formation in real time. We simultaneously applied spectroscopic ellipsometry and quartz crystal microbalance with dissipation to reveal the features and steps of the immune complex formation. We showed direct experimental evidence based on acoustic and optical measurements that the immune complex between covalently immobilized SCoV2-S and specific monoclonal antibodies is formed in two stages. Based on these findings it was demonstrated that applying a two-step binding mathematical model for kinetics analysis leads to a more precise determination of interaction rate constants than that determined by the 1:1 Langmuir binding model. Our investigation showed that the equilibrium dissociation constants (KD) determined by a two-step binding model and the 1:1 Langmuir model could differ significantly. The reported findings can facilitate a deeper understanding of antigen-antibody immune complex formation steps and can open a new way for the evaluation of antibody affinity towards corresponding antigens.

Parameter estimation and identifiability analysis for a bivalent analyte model of monoclonal antibody-antigen binding

Surface plasmon resonance (SPR) is an extensively used technique to characterize antigen-antibody interactions. Affinity measurements by SPR typically involve testing the binding of antigen in solution to monoclonal antibodies (mAbs) immobilized on a chip and fitting the kinetics data using 1:1 Langmuir binding model to derive rate constants. However, when it is necessary to immobilize antigens instead of the mAbs, a bivalent analyte (1:2) binding model is required for kinetics analysis. This model is lacking in data analysis packages associated with high throughput SPR instruments and the packages containing this model do not explore multiple local minima and parameter identifiability issues that are common in non-linear optimization. Therefore, we developed a method to use a system of ordinary differential equations for analyzing 1:2 binding kinetics data. Salient features of this method include a grid search on parameter initialization and a profile likelihood approach to determine parameter identifiability. Using this method we found a non-identifiable parameter in data set collected under the standard experimental design. A simulation-guided improved experimental design led to reliable estimation of all rate constants. The method and approach developed here for analyzing 1:2 binding kinetics data will be valuable for expeditious therapeutic antibody discovery research.

On the Binding Mechanisms of Calcium Ions to Polycarboxylates: Effects of Molecular Weight, Side Chain, and Backbone Chemistry.

We experimentally determined the characteristics and Langmuir parameters of the binding of calcium ions to different polycarboxylates. By using potentiometric titrations and isothermal titration calorimetry, the effects of side chain chemistry, pH value, and chain length were systematically investigated using the linear polymers poly(aspartic acid), poly(glutamic acid), and poly(acrylic acid). We demonstrate that for polymers with high polymerization degrees, the binding process is governed by higher-order effects, such as the change of apparent pKa of carboxyl groups, and contributions arising from the whole polymer chain while the chemistry of the monomer unit constituting the polymer plays a subordinate role. In addition, primary binding sites need to be present in the polymer, thus rendering the abundance and sequential arrangement of protonated and deprotonated groups important. The detection of higher-order effects contradicts the assumptions posed by the Langmuir model of noninteracting binding sites and puts a question mark on whether ion binding to polycarboxylates can be described using solely a Langmuir binding model. No single uniform mechanism fits all investigated systems, and the whole polymer chain, including terminal groups, needs to be considered for the interpretation of binding data. Therefore, one needs to be careful when explaining ion binding to polymers solely based on studies on monomers or oligomers.

An accelerated approach for mechanistic model based prediction of linear gradient elution ion-exchange chromatography of proteins

With growing demands for therapeutic monoclonal antibodies, in silico downstream process development based on mechanistic modeling of chromatography separation process is being increasingly used for process optimization and process characterization. Application of mechanistic modeling in biopharmaceutical industry has been sparse due to the significant investment of time and resources that are required for performing model calibration. Mechanistic modeling of the chromatography process involves a large number of mass transport and binding parameters and their initial input values are required for simulations. These input values of column parameters can be easily obtained either from experiments or from empirical correlations available in literature. On the other hand, obtaining the model input valves for binding kinetic parameters is usually a cumbersome process as it involves performing batch experiments which are not only tedious but also require significant quantities of purely isolated main product and its related impurities, which is challenging as the product related impurities are typically present in smaller quantities and hence are difficult to obtain as pure species. In the present work, a mechanistic model that is based on the general rate model coupled with extended Langmuir binding model has been used for prediction of linear gradient elution peaks of monoclonal antibody on cation exchanger chromatography. The present work describes an accelerated approach for obtaining the input values for binding kinetic parameters in the extended Langmuir binding model from the two Yamamoto coefficient A and B values obtained by Yamamoto method directly from the model calibration linear gradient elution runs of different gradient slopes and at low to moderate protein loadings. The equations that can relate the two coefficients to the extended Langmuir model equation binding kinetic parameters were derived. Therefore, once A and B are determined, the binding kinetic parameter values were determined straightforward, thereby avoiding the problem of multiple solutions for the model parameters. The estimated binding parameters were successfully validated from isocratic elution experiments performed at low loading. What we demonstrate is that the proposed approach allows us to estimate binding kinetic parameters in a significantly more efficient and accelerated manner than presently used approaches, thereby accelerating development and implementation of mechanistic modeling for process chromatography.

Development of the Sequential Binding Model and Application for Anticooperative Protein Adsorption onto Charged Dendrimers.

The Langmuir binding model provides one of the simplest and elegant methods for characterizing an adsorption process. Despite its wide-ranging applications, enormous effort has been spent to further integrate complexity onto the standard Langmuir isotherm to incorporate a wide breadth of binding kinetics with the heterogeneity and cooperative effect among ligands and receptors. Here, we use statistical mechanics as a convenient theoretical framework to depict several adsorption processes on a Langmuir-like description. With regard to the system with a two-component mixture of macromolecular binders, we have derived the two-group sequential binding isotherm as an important extension of the original sequential model with more applications, including systems of non-identical binders. Via comparison of the Langmuir equilibrium with the Boltzmann equilibrium, for the first time the binding free energy defined in the Langmuir-like models can be meaningfully compared with simulations. In a practical example of the adsorption between the lysozyme protein and charged dendrimer, we have demonstrated how the calorimetry data of this system could be interpreted by the binding models described above, with an accurate description of the adsorption process, including the cooperative effect and dendrimer heterogeneity. Using the computer simulation as a benchmark, we also reveal and discuss the strengths and limitations of the proposed binding models. The entire analysis serves as a starting point for extending the standard Langmuir model to access more complicated binding processes.

Quantitative interpretation of protein breakthrough curves in small-scale depth filter modules for bioprocessing

Depth filters are used throughout downstream bioprocessing, with specialized filter chemistries developed for removal of host cell proteins, DNA, and protein aggregates. These filters often show very broad breakthrough curves, but there have not been any previous analyses of this phenomenon. Protein breakthrough curves were evaluated for Ribonuclease A and α-chymotrypsin in two small-scale depth filter modules using PDH4 media containing diatomaceous earth. Computational fluid dynamics (CFD) simulations were developed, with flow in the porous media described using the modified Navier-Stokes equations incorporating a Brinkman flow term and a Langmuir binding model. The breakthrough curve in the stainless-steel module was much sharper than that in the commercial Supracap™ 50 capsule due to the more uniform axisymmetric flow field. The behavior in the Supracap™ 50 capsule was more complex, with multiple inflection points observed due to the different flow paths and the time-dependent saturation of the two layers of the depth filter media. These results provide important insights into the factors controlling protein binding/breakthrough in commercial depth filter modules.

Quantifying Interferent Effects on Molecularly Imprinted Polymer Sensors for Per- and Polyfluoroalkyl Substances (PFAS).

Per- and polyfluoroalkyl substances (PFAS) are emerging as harmful environmental micropollutants. Generally, PFAS species are quantified by mass spectrometry, for which a collected sample is taken to a centralized facility. Robust techniques to quantify PFAS in the field are necessary to diagnose environmental contamination at the earliest onset of pollution. Here, we developed a molecularly imprinted polymer (MIP) electrode for the detection of perfluorooctanesulfonate (PFOS) and explored the MIP surface and the effects of interfering molecules. MIPs were formed by the anodic deposition of o-phenylenediamine (o-PD) in the presence of PFOS template molecules on a glassy carbon macroelectrode. The performance of the resulting MIP electrode was evaluated by the current obtained from the oxidation of ferrocene carboxylic acid as the electrochemical probe. The MIP electrode was able to detect PFOS with a detection limit of 0.05 nM, which is lower than the health advisory limit of 0.14 nM reported by the U.S. EPA. To better understand PFOS association into the MIP, a Langmuir binding model was developed based on the changes in electrochemical responses of the MIP. Fitting the model to the experimental data gave an association constant (KA) of 4.95 × 1012 over a PFOS concentration range of 0 to 0.05 nM. The binding isotherm of other commonly found substances in contaminated water sources such as chloride, humic acid, perfluorooctanoic acid (PFOA), and perfluorobutanesulfonate (PFBS) was also investigated. In the case of chloride and humic acid, the calculated KA values of 9.05 × 107 and 6.01 × 105, respectively, indicate relatively weak adsorption of these species on the MIP. However, PFOA, which is the carboxylate analog of PFOS, revealed a very close KA value (3.41 × 1012) to PFOS. A greater KA value (1.43 × 1013) was obtained for PFBS, which possesses the same functional group and a smaller molecular size compared to PFOS. The presented platform emphasizes the necessity to develop new strategies to make MIP sensors more specific if practical applications are to be pursued.

Exploration of synthesized quaternary ammonium ionic liquids as unharmful anti-corrosives for aluminium utilizing hydrochloric acid medium

1-(benzyloxy)-1-oxopropan-2-aminium 4-methylbenzenesulfonate (BOPAMS) and 4-(benzyloxy)-4-oxobutan-1-aminium 4-methylbenzenesulfonate (BOBAMS) were prepared and confirmed through spectroscopic methods (FT-Infrared, Carbon 13-NMR and Proton-NMR). BOPAMS and BOBAMS were evaluated as unharmful inhibitors for aluminium corrosion. The anti-corrosive properties, inhibition mechanism, inhibitor-metal adsorption behaviour and corrosion inhibition efficiency were evaluated by employing approaches like gravimetric, potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS). FT-IR demonstrated functional groups that are responsible for the binding of BOPAMS and BOBAMS with surfaces of aluminium. BOPAMS and BOBAMS resemble a semi-chemisorption/physisorption mechanism, obeying Langmuir binding model. The mechanism of binding adopted by BOPAMS and BOBAMS was proposed.

Revisiting the Interaction of Melittin with Phospholipid Bilayers: The Effects of Concentration and Ionic Strength.

Melittin is an anti-microbial peptide (AMP) and one of the most studied membrane-disrupting peptides. There is, however, a lack of accurate measurements of the concentration-dependent kinetics and affinity of binding of melittin to phospholipid membranes. In this study, we used surface plasmon resonance spectroscopy to determine the concentration-dependent effect on the binding of melittin to 1-palmitoyl-2-oleoyl-glycero-3-phosphocholine (POPC) bilayers in vesicles. Three concentration ranges were considered, and when combined, covered two orders of magnitudes (0.04 µM to 8 µM), corresponding to concentrations relevant to the membrane-disrupting and anti-microbial activities of melittin. Binding kinetics data were analysed using a 1:1 Langmuir-binding model and a two-state reaction model. Using in-depth quantitative analysis, we characterised the effect of peptide concentration, the addition of NaCl at physiological ionic strength and the choice of kinetic binding model on the reliability of the calculated kinetics and affinity of binding parameters. The apparent binding affinity of melittin for POPC bilayers was observed to decrease with increasing peptide/lipid (P/L) ratio, primarily due to the marked decrease in the association rate. At all concentration ranges, the two-state reaction model provided a better fit to the data and, thus, a more reliable estimate of binding affinity. Addition of NaCl significantly reduced the signal response during the association phase; however, no substantial effect on the binding affinity of melittin to the POPC bilayers was observed. These findings based on POPC bilayers could have important implications for our understanding of the mechanism of action of melittin on more complex model cell membranes of higher physiological relevance.

Analytical Model To Describe the Effect of Polyethylene Glycol on Ionic Screening of Analyte Charges in Transistor-Based Immunosensing.

Recently, the co-immobilization of polyethylene glycol has improved sensor responses of transistor-based immunosensing by approximately three times. However, there is currently no analytical model available to explain this empirical effect. The key parameters thought to affect the potential are the receptor density, the capacitance, the analyte charge, and the dissociation constant. Based on our experimental data, only the analyte charge can account for the signal enhancement. To capture the effect of PEG on the analyte charge, we introduce a prefactor, the detectable charge qdet, which represents the portion of analyte charges effectively detected by the sensor. This parameter can quantitatively describe the PEG-induced signal enhancement and can be used to recommend the choice of PEG size for immuno-field-effect transistors. Additionally, we include the competition between electrolyte ions and the analyte for binding to the recognition molecule to more accurately describe the concentration-dependent sensor responses than the traditional Langmuir binding model does.

Measurement of the interaction between mucin and oenological tannins by Surface Plasmon Resonance (SPR); relationship with astringency

The interaction between stomach porcine mucin and 3 oenological tannins (extract of ellagitannins from oak, extract of gallotannins from gall nuts and extract of proanthocyanidins from grape seeds) was measured by Surface Plasmon Resonance (SPR). These tannins were analysed and their astringency was determined using the Astringency Index method and by tasting. The interaction constants were determined using a Biacore SPR device (1:1 Langmuir binding model). The results indicate that the ellagitannins are more astringent than gallotannins and those, in turn, are more astringent than seed proanthocyanidins if the richness of the commercial extracts is considered. The astringency index of these tannins had high correlation and regression coefficients with their kinetic and thermodynamic dissociation constants. This data support a hypothesis that astringency depends not only on the thermodynamic tendency to form the complex between tannins and salivary proteins but also probably on the time required to dissociate the complex.

Real-time label-free detection and kinetic analysis of Etanercept—Protein A interactions using quartz crystal microbalance

A quartz crystal microbalance (QCM) was constructed to assess if such a biosensor has value as a complementary real-time label-free analysis platform for the biopharmaceutical industry. This was achieved through modifying QCM crystals with a low-fouling carboxymethyl-dextran layer bearing Protein A, and then injecting solutions containing Etanercept (i.e., Enbrel®) into the QCM chambers. The kinetics of Enbrel® – Protein A interactions was modeled using the Langmuir binding model and Enbrel® concentrations between 0.75–300ngmL−1. The resulting equilibrium dissociation and association constants (KD and KA) were 5.06×10−8M and 1.98×107M−1, respectively. The association and dissociation rate constants (kon and koff) decreased substantially as Enbrel® concentration, [C], increased, despite that the net binding rate, (kon[C]+koff), increased. The decrease in kon and koff was hypothesized to be a consequence of mass transport limitations. To verify this, QCM dissipation measurements were analyzed to provide insight on solution viscosity. As Enbrel® concentration increased, the net change in dissipation, ΔD, became larger. An augmentation of ΔD is associated with a higher solution viscosity, which would result in an increase in mass transport limitations. Therefore, the decrease in kon and koff for increasing Enbrel® concentration can be attributed to mass transport limitations. In conclusion, QCM is a valuable complementary real-time label-free biosensor analysis platform for the biopharmaceutical industry. Unlike the surface plasmon resonance (SPR) platform, QCM allows measuring dissipation, which can provide insight on how mass transport limitations impact interaction kinetics.

Computationally modelled receptors for drug monitoring using an optical based biomimetic SPR sensor

Molecularly imprinted polymers (MIPs) were designed using computational simulation for the synthesis of high affinity synthetic receptors to be used for drug recognition and detection in combination with optical based biosensors. The monomer library was screened for the target drug metoprolol based on binding energy between each monomer and the target for the selection of best monomers to develop a successful production recipe. Metoprolol-specific imprinted polymers were then produced using a solid phase synthesis method as nanoparticles. The quality and size of the produced receptors were characterized prior to the affinity testing through a surface plasmon resonance (SPR)-based optical biosensor. High quality synthetic receptors were obtained with the best monomer found using the molecular modelling program. The size of the receptors was found to be ∼169.4nm±3.5 with polydispersity index of 0.3. The target specific receptor was then immobilized on the sensor surface via covalent coupling. Metoprolol which is a β-blocker drug was successfully detected in the range of 1.9ngmL−1–1μgmL−1 with correlation coefficient of 0.97. A regeneration method was developed for the sensor reuse and cross-reactivity studies were conducted using the control drugs. Kinetic data analysis was performed through SPR-2 and Biacore 3000 analysers to determine the affinity between the recognition element and metoprolol. The dissociation constant was found to be 1.35×10−10M using Langmuir binding model. The size and quality of the synthetic receptor were periodically measured during six months to determine the stability. The size of the receptors was found nearly same (171.1nm±5.4) and they still showed high activity for sensor assays. The receptor affinity and capacity towards metoprolol were also confirmed by solid phase extraction (SPE) method coupled with LC–MS. The achieved results highlight the success of computationally modelled receptor with SPR biosensor for pharmaceuticals detection and monitoring. The application of this work can be extended to the separation and detection of drugs from water samples, providing useful information and separation techniques for ecotoxicity studies.

SPR detection of cardiac troponin T for acute myocardial infarction

A surface plasmon resonance (SPR) sensor developed for the rapid, sensitive and specific detection of cardiac troponin T (cTnT) in serum samples is reported in this work. An extensive optimisation of assay parameters was conducted to achieve optimal detection strategy. Both direct and sandwich immunoassay formats were investigated and optimised. The response obtained was enhanced further by the use of gold nanoparticles (AuNPs) conjugated to the anti-cTnT detection antibody. A regeneration method was developed to enable the reuse of the SPR sensor for multiple sample application. The SPR immunosensor showed good reproducibility for cTnT detection in the concentration range of 25–1000ngmL−1 and 5–400ngmL−1 for the direct and sandwich assays in buffer, respectively. The linear regression analysis was performed and R2 value was found as 0.99 for both assays. In order to optimise the sensor for serum analysis, nonspecific binding of serum proteins was reduced through the use of additives in the dilution buffer. To achieve greater sensitivity, the performance of the cTnT immunosensor sandwich assay in human serum was evaluated using non-modified and AuNP modified detector antibodies. A detection limit (LOD) for the immunosensor in 50% serum was assessed as 5ngmL−1 cTnT for the standard sandwich assay and 0.5ngmL−1 cTnT when using AuNP conjugated detector antibodies with a linear dynamic range of 0.5–40ngmL−1. The dissociation constant was found as 3.28 × 10−9M using Langmuir binding model which indicates high affinity between cTnT and its antibody. The proposed SPR immunosensor has a promising potential to be developed for point-of-care testing for the early diagnosis of acute myocardial infarction (AMI). This method can also be used for the rapid detection of biomarkers in central nervous system diseases.

Development of a Human IgG4 Bispecific Antibody for Dual Targeting of Interleukin-4 (IL-4) and Interleukin-13 (IL-13) Cytokines

Human bispecific antibodies have great potential for the treatment of human diseases. Although human IgG1 bispecific antibodies have been generated, few attempts have been reported in the scientific literature that extend bispecific antibodies to other human antibody isotypes. In this paper, we report our work expanding the knobs-into-holes bispecific antibody technology to the human IgG4 isotype. We apply this approach to generate a bispecific antibody that targets IL-4 and IL-13, two cytokines that play roles in type 2 inflammation. We show that IgG4 bispecific antibodies can be generated in large quantities with equivalent efficiency and quality and have comparable pharmacokinetic properties and lung partitioning, compared with the IgG1 isotype. This work broadens the range of published therapeutic bispecific antibodies with natural surface architecture and provides additional options for the generation of bispecific antibodies with differing effector functions through the use of different antibody isotypes.

Binding of the Anti-Cancer Drug Daunomycin to DNA Probed by Second Harmonic Generation

Second harmonic generation (SHG) was used to selectively probe DNA-drug interactions without the need for chemical labels or invasive detection methods. In particular, the binding constant of the anticancer drug daunomycin to a recognition triplet sequence in a 33-mer of double stranded DNA was determined. The SHG method, which is interface selective, probed the binding of daunomycin to DNA that was tethered to the surface of colloidal microparticles suspended in aqueous solution. Probing biomolecule coated colloids is expected to yield larger SH signals and provides experimental flexibility as compared to experiments performed at planar interfaces. The change in SHG intensity as daunomycin was added to the microparticle solution was fit to a Langmuir binding model, which yielded an equilibrium constant of 2.3 (±0.7) × 10(5) M(-1); the corresponding Gibbs free energy change at 20 °C is -7.2 ± 0.2 kcal/mol. Control experiments established that daunomycin preferentially binds to DNA at the recognition sequence. The equilibrium was found to be unaffected by the presence of free DNA in solution, and hyper-Rayleigh scattering from bulk molecules did not change with increasing daunomycin concentration. The extracted equilibrium constants are in agreement with the range of reported values found in the literature.

Integrated label-free silicon nanowire sensor arrays for (bio)chemical analysis

We present a label-free (bio)chemical analysis platform that uses all-electrical silicon nanowire sensor arrays integrated with a small volume microfluidic flow-cell for real-time (bio)chemical analysis and detection. The integrated sensing platform contains an automated multi-sample injection system that eliminates erroneous sensor responses from sample switching due to flow rate fluctuations and provides precise sample volumes down to 10 nl. Biochemical sensing is demonstrated with real-time 15-mer DNA-PNA (peptide nucleic acid) duplex hybridization measurements from different sample concentrations in a low ionic strength, and the equilibrium dissociation constant KD ≈ 140 nM has been extracted from the experimental data using the first order Langmuir binding model. Chemical sensing is demonstrated with pH measurements from different injected samples in flow that have sensitivities consistent with the gate-oxide materials. A differential sensor measurement configuration results in a 30× reduction in sensor drift. The integrated label-free analysis platform is suitable for a wide range of small volume chemical and biochemical analyses.

The Stalk Domain and the Glycosylation Status of the Activating Natural Killer Cell Receptor NKp30 Are Important for Ligand Binding

The natural cytotoxicity receptors are a unique set of activating proteins expressed mainly on the surface of natural killer (NK) cells. The human natural cytotoxicity receptor family comprises the three type I membrane proteins NKp30, NKp44, and NKp46. Especially NKp30 is critical for the cytotoxicity of NK cells against different targets including tumor, virus-infected, and immature dendritic cells. Although the crystal structure of NKp30 was recently solved (Li, Y., Wang, Q., and Mariuzza, R. A. (2011) J. Exp. Med. 208, 703-714; Joyce, M. G., Tran, P., Zhuravleva, M. A., Jaw, J., Colonna, M., and Sun, P. D. (2011) Proc. Natl. Acad. Sci. U.S.A. 108, 6223-6228), a key question, how NKp30 recognizes several non-related ligands, remains unclear. Therefore, we investigated the parameters that impact ligand recognition of NKp30. Based on various NKp30-hIgG1-Fc fusion proteins, which were optimized for minimal background binding to cellular Fcγ receptors, we identified the flexible stalk region of NKp30 as an important but so far neglected module for ligand recognition and related signaling of the corresponding full-length receptor proteins. Moreover, we found that the ectodomain of NKp30 is N-linked glycosylated at three different sites. Mutational analyses revealed differential binding affinities and signaling capacities of mono-, di-, or triglycosylated NKp30, suggesting that the degree of glycosylation could provide a switch to modulate the ligand binding properties of NKp30 and NK cell cytotoxicity.

Validation of Arrayed Imaging Reflectometry Biosensor Response for Protein–Antibody Interactions: Cross-Correlation of Theory, Experiment, and Complementary Techniques

One of the critical steps in the development of an analytical technique is to confirm that its experimental response correlates with predictions derived from the theoretical framework on which it is based. This validates the technique quantitatively and, in the case of a biosensor, facilitates a correlation of the sensor's output signal to the concentration of the analyte being tested. Herein we report studies demonstrating that the quantitative response of arrayed imaging reflectometry (AIR), a highly sensitive label-free biosensing method, is a predictable function of the probe and analyte properties. We first incorporated a standard one-site Langmuir binding model describing probe-analyte interactions at the surface into the theoretical model for thickness-dependent reflectance in AIR. This established a hypothetical correlation between the analyte concentration and the AIR response. Spectroscopic ellipsometry, surface plasmon resonance, and AIR were then used to validate this model for two biomedically important proteins, fibroblast growth factor-2 and vascular endothelial growth factor. While our studies demonstrated that the 1:1 one-site Langmuir model accurately described the observed response of macrospot AIR arrays, either a two-site Langmuir model or a Sips isotherm better described the behavior of AIR microarrays. These studies confirmed the quantitative performance of AIR across a range of probe-analyte affinities. Furthermore, the methodology developed here can be extended to other label-free biosensing platforms, thus facilitating a more accurate and quantitative interpretation of the sensor response.