Engineered synthetic antibodies as probes to quantify the energetic contributions of ligand binding to conformational changes in proteins.

Conformational changes in proteins due to ligand binding are ubiquitous in biological processes and are integral to many biological systems. However, it is often challenging to link ligand-induced conformational changes to a resulting biological function because it is difficult to distinguish between the energetic components associated with ligand binding and those due to structural rearrangements. Here, we used a unique approach exploiting conformation-specific and regio-specific synthetic antibodies (sABs) to probe the energetic contributions of ligand binding to conformation changes. Using maltose-binding protein (MBP) as a model system, customized phage-display selections were performed to generate sABs that stabilize MBP in different conformational states, modulating ligand-binding affinity in competitive, allosteric, or peristeric manners. We determined that the binding of a closed conformation-specific sAB (sAB-11M) to MBP in the absence of maltose is entropically driven, providing new insight into designing antibody-stabilized protein interactions. Crystal structures of sABs bound to MBP, together with biophysical data, delineate the basis of free energy differences between different conformational states and confirm the use of the sABs as energy probes for dissecting enthalpic and entropic contributions to conformational transitions. Our work provides a foundation for investigating the energetic contributions of distinct conformational dynamics to specific biological outputs. We anticipate that our approach also may be valuable for analyzing the energy landscapes of regulatory proteins controlling biological responses to environmental changes.

Deciphering complex protein interaction kinetics using Interaction Map.

Cellular receptor systems are expected to present complex ligand interaction patterns that cannot be evaluated assuming a simple one ligand:one receptor interaction model. We have previously evaluated heterogeneous interactions using an alternative method to regression analysis, called Interaction Map (IM). IM decomposes a time-resolved binding curve into its separate components. By replacing the reductionistic, scalar kinetic association rate constant k(a) and dissociation rate constant k(d) with a two-dimensional distribution of k(a) and k(d), it is possible to display heterogeneous data as a map where each peak corresponds to one of the components that contribute to the cumulative binding curve. Here we challenge the Interaction Map approach by artificially generating heterogeneous data from two known interactions, on either LigandTracer or Surface Plasmon Resonance devices. We prove the ability of IM to accurately decompose these man-made heterogeneous binding curves composed of two different interactions. We conclude that the Interaction Map approach is well suited for the analysis of complex binding data and forecast that it has a potential to resolve previously uninterpretable data, in particular those generated in cell-based assays.

Deciphering the stepwise binding mode of HRG1ß to HER3 by surface plasmon resonance and interaction map.

For the development of efficient anti-cancer therapeutics against the HER receptor family it is indispensable to understand the mechanistic model of the HER receptor activation upon ligand binding. Due to its high complexity the binding mode of Heregulin 1 beta (HRG1ß) with its receptor HER3 is so far not understood. Analysis of the interaction of HRG1ß with surface immobilized HER3 extracellular domain by time-resolved Surface Plasmon Resonance (SPR) was so far not interpretable using any regular analysis method as the interaction was highly complex. Here, we show that Interaction Map (IM) made it possible to shed light on this interaction. IM allowed deciphering the rate limiting kinetic contributions from complex SPR sensorgrams and thereby enabling the extraction of discrete kinetic rate components from the apparently heterogeneous interactions. We could resolve details from the complex avidity-driven binding mode of HRG1ß with HER3 by using a combination of SPR and IM data. Our findings contribute to the general understanding that a major conformational change of HER3 during its activation is induced by a complex sequential HRG1ß docking mode.

Automated functional characterization of radiolabeled antibodies: a time-resolved approach.

BACKGROUND: The number of radiolabeled monoclonal antibodies (mAbs) used for medical imaging and cancer therapy is increasing. The required chemical modification for attaching a radioactive label and all associated treatment may lead to a damaged mAb subpopulation. This paper describes a novel method, concentration through kinetics (CTK), for rapid assessment of the concentration of immunoreactive mAb and the specific radioactivity, based on monitoring binding kinetics. METHODS: The interaction of radiolabeled mAb with either the antigen or a general mAb binder such as Protein A was monitored in real time using the instrument LigandTracer. As the curvature of the binding trace has a distinct shape based on the interaction kinetics and concentration of the functional mAb, the immunoreactive mAb concentration could be calculated through reverse kinetic fitting of the binding curves, using software developed for this project. The specific activity, describing the degree of radioactive labeling, was determined through the use of calibrated signal intensities. RESULTS: The performance of the CTK assay was evaluated on the basis of various mAb-based interaction systems and assay formats, and it was shown that the assay can provide accurate and repeatable results for immunoreactive concentration and specific activity, with both accuracy and relative SD values below 15%. CONCLUSION: By applying reverse kinetics on real-time binding traces it is possible to estimate the functional concentration and specific activity of radiolabeled mAb. The CTK assay may in the future be included as a complement to current quality assessment methods of radiolabeled mAbs.

Resolving the EGF-EGFR interaction characteristics through a multiple-temperature, multiple-inhibitor, real-time interaction analysis approach.

Overexpression and aberrant activity of the epidermal growth factor (EGF) have been observed in various cancer types, rendering it an important target in oncology research. The interaction between EGF and its receptor (EGFR), as well as subsequent internalization, is complex and may be affected by various factors including tyrosine kinase inhibitors (TKIs). By combining real-time binding curves produced in LigandTracer® with internalization assays conducted at different temperatures and with different TKIs, the processes of ligand binding, internalization and excretion was visualized. SKOV3 cells had a slower excretion rate compared to A431 and U343 cells, and the tested TKIs (gefitinib, lapatinib, AG1478 and erlotinib) reduced the degree of internalization. The kinetic analysis of the binding curves further demonstrated TKI-dependent balances of EGFR monomer and dimer populations, where lapatinib promoted the monomeric form, while the other TKIs induced dimers. The dimer levels were found to be associated with the apparent affinity of the EGF-EGFR interaction, with EGF binding stronger to EGFR dimers compared to monomers. This study analyzed how real-time molecular interaction analysis may be utilized in combination with perturbations in order to understand the kinetics of a ligand-receptor interaction, as well as some of its associated intracellular processes. Our multiple-temperature and -inhibitor assay setup renders it possible to follow the EGFR monomer, dimer and internalized populations in a detailed manner, allowing for a new perspective of the EGFR biology.

Evaluation of real-time immunohistochemistry and interaction map as an alternative objective assessment of HER2 expression in human breast cancer tissue.

Immunohistochemical study (IHC) is a critical tool in the clinical diagnosis of breast cancer. One common assessment is the expression level of the HER2 receptor in breast cancer tissue samples with the aim of stratifying patients for applicability of the therapeutic antibody Herceptin. In this study, we aimed to investigate whether a novel assay, real-time IHC combined with Interaction Map analysis, offers the possibility of objective assessment of HER2 expression. Interaction Map presents real-time interaction data as a collection of peaks on a surface, and it was performed on 20 patient tissue samples previously scored for HER2 expression. The result shows that the relative weight of the peaks in the maps contains novel information that could discriminate between high and low HER2 expression in an operator-independent manner (P<0.001). We conclude that the real-time IHC assay has a promising potential to complement conventional IHC and may improve the precision in the future clinical diagnostics of breast cancer.

Gefitinib induces epidermal growth factor receptor dimers which alters the interaction characteristics with ¹²5I-EGF.

The tyrosine kinase inhibitor gefitinib inhibits growth in some tumor types by targeting the epidermal growth factor receptor (EGFR). Previous studies show that the affinity of the EGF-EGFR interaction varies between hosting cell line, and that gefitinib increases the affinity for some cell lines. In this paper, we investigate possible mechanisms behind these observations. Real-time interaction analysis in LigandTracer® Grey revealed that the HER2 dimerization preventing antibody pertuzumab clearly modified the binding of ¹²5I-EGF to EGFR on HER2 overexpressing SKOV3 cells in the presence of gefitinib. Pertuzumab did not affect the binding on A431 cells, which express low levels of HER2. Cross-linking measurements showed that gefitinib increased the amount of EGFR dimers 3.0-3.8 times in A431 cells in the absence of EGF. In EGF stimulated SKOV3 cells the amount of EGFR dimers increased 1.8-2.2 times by gefitinib, but this effect was cancelled by pertuzumab. Gefitinib treatment did not alter the number of EGFR or HER2 expressed in tumor cell lines A431, U343, SKOV3 and SKBR3. Real-time binding traces were further analyzed in a novel tool, Interaction Map, which deciphered the different components of the measured interaction and supports EGF binding to multiple binding sites. EGFR and HER2 expression affect the levels of EGFR monomers, homodimers and heterodimers and EGF binds to the various monomeric/dimeric forms of EGFR with unique binding properties. Taken together, we conclude that dimerization explains the varying affinity of EGF-EGFR in different cells, and we propose that gefitinib induces EGFR dimmers, which alters the interaction characteristics with ¹²5I-EGF.

QSAR studies applied to the prediction of antigen-antibody interaction kinetics as measured by BIACORE.

The objective of this work was to investigate the potential of the quantitative structure-activity relationships (QSAR) approach for predictive modulation of molecular interaction kinetics. A multivariate QSAR approach involving modifications in peptide sequence and buffer composition was recently used in an attempt to predict the kinetics of peptide-antibody interactions as measured by BIACORE. Quantitative buffer-kinetics relationships (QBKR) and quantitative sequence-kinetics relationships (QSKR) models were developed. Their predictive capacity was investigated in this study by comparing predicted and observed kinetic dissociation parameters (k(d)) for new antigenic peptides, or in new buffers. The range of experimentally measured k(d) variations was small (300-fold), limiting the practical value of the approach for this particular interaction. However, the models were validated from a statistical point of view. In QSKR, the leave-one-out cross validation gave Q(2) = 0.71 for 24 peptides (all but one outlier), compared to 0.81 for 17 training peptides. A more precise model (Q(2) = 0.92) could be developed when removing sets of peptides sharing distinctive structural features, suggesting that different peptides use slightly different binding modes. All models share the most important factor and are informative for structure-kinetics relationships. In QBKR, the measured effect on k(d) of individual additives in the buffers was consistent with the effect predicted from multivariate buffers. Our results open new perspectives for the predictive optimization of interaction kinetics, with important implications in pharmacology and biotechnology.