A Potentially Versatile Enzyme Sensor Platform: Enzyme-Loaded, Tagged, Porous Polymeric Nanocapsules.

Enzymes are essential to life and indispensable in a wide range of industries (food, pharmaceutical, medical, biosensing, etc.); however, a significant shortcoming of these fragile biological catalysts is their poor stability. To address this challenge, a variety of immobilization methods have been described to enhance the enzyme's stability. These immobilization methods generally are specific to an individual enzyme or optimal for a particular application. The aim of this study is to explore the utility of porous, indicator moiety-tagged, polymeric nanocapsules (NCs) for the encapsulation of enzymes and measurement of the enzyme's substrate. As a model enzyme, glucose oxidase (GOx) is used. The GOx enzyme-loaded, fluorophore-tagged NCs were synthesized by using self-assembled surfactant vesicle templates. To show that the biological activity of GOx is preserved during entrapment, the rate of the GOx enzyme catalyzed reaction was measured. To evaluate the protective features of the porous NCs, the encapsulated GOx enzyme activity was followed in the presence of hydrolytic enzymes. During the encapsulation of GOx and the purification of the GOx-loaded NCs, the GOx activity decayed less than 10%, and up to 30% of the encapsulated GOx activity could be retained for 3-5 days in the presence of hydrolytic enzymes. In support of the potentially unique advantages of the enzyme-loaded NCs, as a proof-of-concept example, the fluorophore-tagged, GOx-loaded NCs were used for the determination of glucose in the concentration range between 18 and 162 mg/dL and for imaging the distribution of glucose concentration in imaging experiments.

Multilayer and Surface Immobilization of EDOT-Decorated Nanocapsules.

To assess the feasibility of utilizing reagent-loaded, porous polymeric nanocapsules (NCs) for chemical and biochemical sensor design, the surfaces of the NCs were decorated with 3,4-ethylenedioxythiophene (EDOT) moieties. The pores in the capsule wall allow unhindered bidirectional diffusion of molecules smaller than the programmed pore sizes, while larger molecules are either entrapped inside or blocked from entering the interior of the nanocapsules. Here, we investigate two electrochemical deposition methods to covalently attach acrylate-based porous nanocapsules with 3,4-ethylenedioxythiophene moieties on the nanocapsule surface, i.e., EDOT-decorated NCs to the surface of an existing PEDOT film: (1) galvanostatic or bilayer deposition with supporting EDOT in the deposition solution and (2) potentiostatic deposition without supporting EDOT in the deposition solution. The distribution of the covalently attached NCs in the PEDOT films was studied by variable angle FTIR-ATR and XPS depth profiling. The galvanostatic deposition of EDOT-decorated NCs over an existing PEDOT (tetrakis(pentafluorophenyl)borate) [PEDOT(TPFPhB)] film resulted in a bilayer structure, with an interface between the NC-free and NC-loaded layers, that could be traced with variable angle FTIR-ATR measurements. In contrast, the FTIR-ATR and XPS analyses of the films deposited potentiostatically from a solution without EDOT and containing only the EDOT-decorated NCs showed small amounts of NCs in the entire cross section of the films.

Benchmarking GPCR homology model template selection in combination with de novo loop generation.

G protein-coupled receptors (GPCR) comprise the largest family of membrane proteins and are of considerable interest as targets for drug development. However, many GPCR structures remain unsolved. To address the structural ambiguity of these receptors, computational tools such as homology modeling and loop modeling are often employed to generate predictive receptor structures. Here we combined both methods to benchmark a protocol incorporating homology modeling based on a locally selected template and extracellular loop modeling that additionally evaluates the presence of template ligands during these modeling steps. Ligands were also docked using three docking methods and two pose selection methods to elucidate an optimal ligand pose selection method. Results suggest that local template-based homology models followed by loop modeling produce more accurate and predictive receptor models than models produced without loop modeling, with decreases in average receptor and ligand RMSD of 0.54 Å and 2.91 Å, respectively. Ligand docking results showcased the ability of MOE induced fit docking to produce ligand poses with atom root-mean-square deviation (RMSD) values at least 0.20 Å lower (on average) than the other two methods benchmarked in this study. In addition, pose selection methods (software-based scoring, ligand complementation) selected lower RMSD poses with MOE induced fit docking than either of the other methods (averaging at least 1.57 Å lower), indicating that MOE induced fit docking is most suited for docking into GPCR homology models in our hands. In addition, target receptor models produced with a template ligand present throughout the modeling process most often produced target ligand poses with RMSD values ≤ 4.5 Å and Tanimoto coefficients > 0.6 after selection based on ligand complementation than target receptor models produced in the absence of template ligands. Overall, the findings produced by this study support the use of local template homology modeling in combination with de novo ECL2 modeling in the presence of a ligand from the template crystal structure to generate GPCR models intended to study ligand binding interactions.

GPCR homology model template selection benchmarking: Global versus local similarity measures.

G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for drug development. GPCR ligand interaction studies often have a starting point with either crystal structures or comparative models. The majority of GPCR do not have experimentally-characterized 3-dimensional structures, so comparative modeling, also called homology modeling, is a good structure-based starting point. Comparative modeling is a widely used method for generating models of proteins with unknown structures by analogy to crystallized proteins that are expected to exhibit structural conservation. Traditionally, comparative modeling template selection is based on global sequence identity and shared function. However high sequence identity localized to the ligand binding pocket may produce better models to examine protein-ligand interactions. This in silico benchmark study examined the performance of a global versus local similarity measure applied to comparative modeling template selection for 6 previously crystallized, class A GCPR (CXCR4, FFAR1, NOP, P2Y12, OPRK, and M1) with the long-term goal of optimizing GPCR ligand identification efforts. Comparative models were generated from templates selected using both global and local similarity measures. Similarity to reference crystal structures was reflected in RMSD values between atom positions throughout the structure or localized to the ligand binding pocket. Overall, models deviated from the reference crystal structure to a similar degree regardless of whether the template was selected using a global or local similarity measure. Ligand docking simulations were performed to assess relative performance in predicting protein-ligand complex structures and interaction networks. Calculated RMSD values between ligand poses from docking simulations and crystal structures indicate that models based on locally selected templates give docked poses that better mimic crystallographic ligand positions than those based on globally-selected templates in five of the six benchmark cases. However, protein model refinement strategies in advance of ligand docking applications are clearly essential as the average RMSD between crystallographic poses and poses docked into local template models was 9.7 Šand typically less than half of the ligand interaction sites are shared between the docked and crystallographic poses. These data support the utilization of local similarity measures to guide template selection in protocols using comparative models to investigate ligand-receptor interactions.