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Statement of Purpose Hybrid nanoparticles in which a polymer is used to stabilize the secondary structure of enzyme provide a means to preserve its activity in non-native environments. This approach is illustrated here with horseradish peroxidase (HRP), an important heme enzyme used in medical diagnostic, biosensing, and biotechnological applications. Polymer chaperones in these polymer-enzyme complex (PEC) nanoparticles can enhance the utility of enzymes in unfavorable environments. Structural analysis of the PECs is a crucial link in the machine-learning driven iterative optimization cycle of polymer synthesis and testing. Here, we discuss the utility of small-angle X-ray scattering (SAXS) and quartz crystal microbalance with dissipation (QCMD) for evaluating PECs. Materials and Methods Six polymers were synthesized by automated photoinduced electron/energy transfer-reversible addition-fragmentation chain-transfer (PET-RAFT) polymerization directly in 96-well plates.1 Multiple molar ratios of enzyme:polymer (1:1, 1:5, 1:10, and 1:50) were characterized. HRP was mixed with the polymer and heated to 65 °C for 1 hr to form PECs. Enzyme assay and circular dichroism measurements were performed along with SAXS and QCMD to understand polymer-protein interactions. SAXS data were obtained at NSLS-II beamline 16-ID. Results and Discussion SAXS data were analyzed to determine the radius of gyration (Rg), Porod exponent and pair distance distribution functions (P(r)) (Figure 1). Rg, which corresponds to the size of the PEC nanoparticles, is sensitive to the polydispersity of the solution and does not change significantly in the presence of the polymer GEP1. Notably, the maximal dimension does not change as significantly upon heating to denaturation in the case of the PEC as it does with HRP alone. The effect of denaturation induced by heating seems to depend on the molar ratio of the polymer to enzyme. The Porod exponent, which is related to roughness, decreased from about 4 to 3 upon complexation indicating polymer binding to the enzyme’s surface. These were confirmed by modeling the structures of the HRP, the polymer and the PEC were modeled using DAMMIF/DAMMIN and MONSA (ATSAS software). The changes observed in the structure could be correlated to the measured enzymatic activity. Figure 2 shows the evolution of the PEC when the polymer is deposited onto the enzyme immobilized on Figure 1. P(r) plots for PEC vs. HRP before and after heating, illustrating the increased enzymatic stability due to polymer additives. gold-coated QCM sensors. The plots show the changes in frequency (f) and dissipation (D) with time as HRP is first deposited and is followed by the adsorption of the polymer. Large f and D show that the polymer forms a complex with HRP. Such changes were not observed with negative controls, Pluronics and poly(ethylene glycol). Comparison of the data from free particles in solution with QCM data from immobilized enzymes, shows that the conformation of the complexes in solution and surface-bound HRP could be different. This way, we were able to explore the various states of complex formation under different conditions with different polymers. Figure 2. QCMD data showing the interaction between the immobilized HRP and the polymer. 3rd and 5th harmonics are plotted (blue -f; red-D). Conclusion SAXS and QCMD data show that stabilization of the enzyme activity by inhibiting the unraveling of the secondary structure as seen in size, surface roughness, pair distribution function and percent helicity. Acknowledgment This work was supported by NSF grant 2009942. References [1] Tamasi, M, et al. Adv Intell Syst 2020, 2(2): 1900126. 

Polymer‐protein hybrids can be deployed to improve protein solubility and stability in denaturing environments. While previous work used robotics and active machine learning to inform new designs, further biophysical information is required to ascertain structure–function behavior. Here, we show the value of tandem small‐angle x‐ray scattering (SAXS) and quartz crystal microbalance with dissipation (QCMD) experiments to reveal detailed polymer‐protein interactions with horseradish peroxidase (HRP) as a test case. Of particular interest was the process of polymer‐protein complex formation under thermal stress whereby SAXS monitors formation in solution while QCMD follows these dynamics at an interface. The radius of gyration (R_g) of the protein as measured by SAXS does not change significantly in the presence of polymer under denaturing conditions, but thickness and dissipation changes were observed in QCMD data. SAXS data with and without thermal stress were utilized to create bead models of the potential complexes and denatured enzyme, and each model fit provided insight into the degree of interactions. Additionally, QCMD data demonstrated that HRP deforms by spreading upon surface adsorption at low concentration as shown by longer adsorption times and smaller frequency shifts. In contrast, thermally stressed and highly inactive HRP had faster adsorption kinetics. The combination of SAXS and QCMD serves as a framework for biophysical characterization of interactions between proteins and polymers which could be useful in designing polymer‐protein hybrids.

 

Polymer–protein hybrids are intriguing materials that can bolster protein stability in non‐native environments, thereby enhancing their utility in diverse medicinal, commercial, and industrial applications. One stabilization strategy involves designing synthetic random copolymers with compositions attuned to the protein surface, but rational design is complicated by the vast chemical and composition space. Here, a strategy is reported to design protein‐stabilizing copolymers based on active machine learning, facilitated by automated material synthesis and characterization platforms. The versatility and robustness of the approach is demonstrated by the successful identification of copolymers that preserve, or even enhance, the activity of three chemically distinct enzymes following exposure to thermal denaturing conditions. Although systematic screening results in mixed success, active learning appropriately identifies unique and effective copolymer chemistries for the stabilization of each enzyme. Overall, this work broadens the capabilities to design fit‐for‐purpose synthetic copolymers that promote or otherwise manipulate protein activity, with extensions toward the design of robust polymer–protein hybrid materials.

 

Among the many molecules that contribute to glial scarring, chondroitin sulfate proteoglycans (CSPGs) are known to be potent inhibitors of neuronal regeneration. Chondroitinase ABC (ChABC), a bacterial lyase, degrades the glycosaminoglycan (GAG) side chains of CSPGs and promotes tissue regeneration. However, ChABC is thermally unstable and loses all activity within a few hours at 37 °C under dilute conditions. To overcome this limitation, the discovery of a diverse set of tailor‐made random copolymers that complex and stabilize ChABC at physiological temperature is reported. The copolymer designs, which are based on chain length and composition of the copolymers, are identified using an active machine learning paradigm, which involves iterative copolymer synthesis, testing for ChABC thermostability upon copolymer complexation, Gaussian process regression modeling, and Bayesian optimization. Copolymers are synthesized by automated PET‐RAFT and thermostability of ChABC is assessed by retained enzyme activity (REA) after 24 h at 37 °C. Significant improvements in REA in three iterations of active learning are demonstrated while identifying exceptionally high‐performing copolymers. Most remarkably, one designed copolymer promotes residual ChABC activity near 30%, even after one week and notably outperforms other common stabilization methods for ChABC. Together, these results highlight a promising pathway toward sustained tissue regeneration.