Antibody–enzyme complexes (AECs) are ideal molecular recognition elements for immunosensing applications. One molecule possesses both a binding ability to specific targets and catalytic activity to gain signals, particularly oxidoreductases, which can be integrated into rapid and sensitive electrochemical measurements. The development of AECs using fragment antibodies rather than intact antibodies, such as immunoglobulin G (IgG), has attracted attention for overcoming the ethical and cost issues associated with the production of intact antibodies. Conventionally, chemical conjugation has been used to fabricate AECs; however, controlling stoichiometric conjugation using this method is difficult. To prepare homogeneous AECs, methods based on direct fusion and enzymatic conjugation have been developed, and more convenient methods using Catcher/Tag systems as coupling modules have been reported. In this review, we summarize the methods for fabricating AECs using fragment antibodies developed for sensing applications and discuss the advantages and disadvantages of each method.

One challenge in the continuous monitoring of insulin lies with the unavailability of a high-affinity biological recognition element (BRE) with kinetic parameters sufficient to track fluctuating concentrations of insulin in vivo. An approach to overcome this limitation is to engineer a high-affinity BRE to selectively modulate its binding kinetics in response to an external signal. Herein, we design and evaluate changes in the pH-dependent binding kinetics of an anti-insulin single chain variable fragment (scFv) we previously employed in a point-of-care insulin sensor. We predicted the scFv structure in complex with human insulin and selected scFv residues directly involved in insulin binding for histidine substitution. We identify one mutation, T32H, that improves the pH-sensitivity of the wild-type (WT); the K_D of the T32H mutant is calculated to be 145.5 ± 83.1 nM at pH 7.4 and 17.4 ± 5.1 nM at pH 6.0 – an average of an 8.4× difference between the two conditions and a 3.8× increase in pH-sensitivity from the WT. We design a bio-layer interferometry (BLI) assay to interrogate the improved pH-sensitivity of the T32H mutant in tracking fluctuating insulin concentrations in dynamic pH conditions and find that improved pH-sensitivity can be leveraged to improve biosensor regeneration. These results suggest the potential for pH-sensitive antibodies to improve the development of in vivo continuous monitoring systems.

Class 1 phytoglobins (Pgbs) are known for their multifunctional roles in plant stress responses, with recent studies suggesting broader interactions involving metabolic and transcriptional regulation. Interestingly, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) moonlights in many roles in colocalized spaces during cellular stress that are strikingly suitable for supporting Pgb function. This study investigates the molecular interactions of class 1 Pgb from sugar beet (Beta vulgaris), BvPgb 1.2, and an alanine-substituted mutant (C86A), focusing on their ability to bind GAPDH and DNA. Using dual-emission isothermal spectral shift (SpS) analysis, we report strong binding interactions with GAPDH, with dissociation constants (K_D) of 260 ± 50 nM for the recombinant wild-type protein (rWT) and a significantly stronger affinity for C86A (120 ± 40 nM), suggesting that the cysteine residue limits the interaction. Remarkably strong DNA-binding affinities were also observed for both variants, displaying biphasic binding. This behavior is characteristic of hexacoordinated globins and reflects the presence of two distinct species: a fast-reacting open pentacoordinated form and a slow-reacting closed hexacoordinated form with high apparent affinity. Here, the K_D in the open configuration was 120 ± 50 nm and 50 ± 20 nM for rWT and C86A, respectively. In the closed configuration, however, the cysteine appears to support the interaction, as the K_D was measured at 100 ± 10 pM and 230 ± 60 pM for rWT and C86A, respectively. Protein–protein docking studies reinforced these findings, revealing electrostatically driven interactions between BvPgb 1.2 and GAPDH, characterized by a substantial buried surface area indicative of a stable, biologically relevant complex. Protein–DNA docking similarly confirmed energetically favorable binding near the heme pocket without obstructing ligand accessibility. Together, these findings indicate a potential regulatory role for BvPgb 1.2 through its interaction with GAPDH and DNA.

Introduction: Many disease states are characterized by the presence, absence, or change of molecular biological markers, or biomarkers. Common affinity-type recognition elements used in the detection of biomarkers are antibodies, either Immunoglobulins (IgGs) or recombinant fragments. While highly specific, the dissociation kinetics of antibodies highly favors the antibody-antigen complex, resulting in single, endpoint measurements [1]. Endpoint measurements are useful for ex vivo assays, but further antibody engineering is required for in vivo continuous monitoring. A key burden to overcome for using antibodies in continuous monitoring applications is the lack of binding site regeneration [2]. Thus, we aim to create pH-modulable single chain fragment variables (scFvs), in which the K_D of the scFv can be controlled to favor dissociation. We posit that through mutagenesis of residues within the complementarity-determining regions (CDRs) to histidine, we are able to modulate antibody binding kinetics through a change in pH [3,4]. There is a basis in the literature that supports this antibody design approach [5]. Those working with therapeutic antibodies currently employ similar techniques to effectively “recycle,” or regenerate the binding site of the antibody in vivo [6]. We seek to expand these techniques into the biosensing space to develop continuous, antibody-based biosensors. Methods: ClusPro was utilized to predict the antigen-binding residues in the CDRs of an anti-insulin scFv. From the predicted residues, we selected three amino acid residues to mutate to histidine with the intent of generating pH-modulable mutants. Three single mutants and a triple mutant containing all mutations were designed. The wild-type (WT) scFv, as well as the four mutants were all expressed as a soluble protein using Escherichia coli (E. coli) as a host microorganism. The binding ability of the WT and four mutants was assessed via two assays in differing pH with Bio-Layer Interferometry (BLI). The BLI assay workflow is as follows: biotinylated insulin was immobilized to Octet Streptavidin tips in buffers of either pH 7.4 or 6.0. Following immobilization, either the WT or mutants were introduced to bind to insulin. The resulting association and dissociation sensorgrams were recorded, and nonlinear fitting of the data was performed in GraphPad Prism 10.0. Results and Discussion: Compared to the WT, the triple histidine mutations in the CDR did not eliminate the scFv’s ability to bind to insulin. For the WT, the apparent K_D decreased slightly between pH 7.4 and pH 6.0. This could be attributed to the change in pH affecting the streptavidin-biotin solution, or could be due to an artifact of the assay. Further studies would be needed to discern between the two hypotheses. However, there was a pronounced increase in the apparent K_D of the triple mutant in a solution of pH 6.0 compared to pH 7.4. These results indicate the combination of mutations led to an observed pH-dependent binding. Further experimentation is needed with the single mutants to elucidate which residue is responsible for pH dependence, or if it is a combination of the three. Conclusions: We employed a rational mutation strategy to generate pH-dependent binding of an anti-insulin scFv. Upon a change in solution pH from 7.4 to 6.0, the triple mutant’s binding equilibrium is shifted, leading to an increase in K_D. Further studies are needed to understand the reversibility of this interaction as well as to elucidate what specific residue interactions are contributing to this change. References: [1]J. P. Landry, Y. Ke, G.-L. Yu, and X. D. Zhu, J. Immunol. Methods, 417, 86–96 (2015) [2] E. Wilson, D. Probst, and K. Sode, Appl. Phys. Rev., 10 (2023). [3] M. L. Murtaugh, S. W. Fanning, T. M. Sharma, A. M. Terry, and J. R. Horn, Protein Sci., 20, 1619–1631 (2011). [4] C. Fercher, M. L. Jones, S. M. Mahler, and S. R. Corrie, ACS Sensors, 6, 764–776 (2021). [5] T. Klaus and S. Deshmukh, J. Biomed. Sci., 28 (2021). [6] T. Igawa et al., Nat. Biotechnol., 28, 1203–1207 (2010).