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Abstract The Nucleocapsid protein (N) of SARS-CoV-2 plays a critical role in the viral lifecycle by regulating RNA replication and by packaging the viral genome. N and RNA phase separate to form condensates that may be important for these functions. Both functions occur at membrane surfaces, but how N toggles between these two membrane-associated functional states is unclear. Here, we reveal that phosphorylation switches how N condensates interact with membranes, in part by modulating condensate material properties. Our studies also show that phosphorylation alters N’s interaction with viral membrane proteins. We gain mechanistic insight through structural analysis and molecular simulations, which suggest phosphorylation induces a conformational change in N that softens condensate material properties. Together, our findings identify membrane association as a key feature of N condensates and provide mechanistic insights into the regulatory role of phosphorylation. Understanding this mechanism suggests potential therapeutic targets for COVID infection. 

Recent experiments have shown that complexation with a stabilizing compound can preserve enzyme activity in harsh environments. Such complexation is believed to be driven by noncovalent interactions at the enzyme surface, including hydrophobicity and electrostatics. Molecular modeling of these interactions is costly at the all-atom scale due to the long time scales and large particle counts needed to characterize binding. Protein structure at the scale of amino acid residues is parsimoniously represented by a coarse-grained model in which one particle represents several atoms, significantly reducing the cost of simulation. Coarse-grained models may then be used to generate reduced surface descriptions to underlie detailed theories of surface adhesion. In this study, we present two coarse-grained enzyme models—lipase and dehalogenase—that have been prepared using the Martini 3 top-down modeling framework. We simulate each enzyme in aqueous solution and calculate the statistics of protein surface features and shape descriptors. The values from the coarse-grained data are compared with the same calculations performed on all-atom reference systems, revealing key similarities of surface chemistry at the two scales. Structural measures are calculated from the all-atom reference systems and compared with estimates from small-angle x-ray scattering experiments, with good agreement between the two. The described procedures of modeling and analysis comprise a framework for the development of coarse-grained models of protein surfaces with validation to experiment. 

Oxygen tolerant polymerizations including Photoinduced Electron/Energy Transfer-Reversible Addition–Fragmentation Chain-Transfer (PET-RAFT) polymerization allow for high-throughput synthesis of diverse polymer architectures on the benchtop in parallel. Recent developments have further increased throughput using liquid handling robotics to automate reagent handling and dispensing into well plates thus enabling the combinatorial synthesis of large polymer libraries. Although liquid handling robotics can enable automated polymer reagent dispensing in well plates, photoinitiation and reaction monitoring require automation to provide a platform that enables the reliable and robust synthesis of various polymer compositions in high-throughput where polymers with desired molecular weights and low dispersity are obtained. Here, we describe the development of a robotic platform to fully automate PET-RAFT polymerizations and provide individual control of reactions performed in well plates. On our platform, reagents are automatically dispensed in well plates, photoinitiated in individual wells with a custom-designed lightbox until the polymerizations are complete, and monitored online in real-time by tracking fluorescence intensities on a fluorescence plate reader, with well plate transfers between instruments occurring via a robotic arm. We found that this platform enabled robust parallel polymer synthesis of both acrylate and acrylamide homopolymers and copolymers, with high monomer conversions and low dispersity. The successful polymerizations obtained on this platform make it an efficient tool for combinatorial polymer chemistry. In addition, with the inclusion of machine learning protocols to help navigate the polymer space towards specific properties of interest, this robotic platform can ultimately become a self-driving lab that can dispense, synthesize, and monitor large polymer libraries. 

The functional structure of proteins is heavily influenced by their folding behavior. AlphaFold, a powerful artificial intelligence (AI) program trained on information from the Protein Data Bank (PDB), was developed to predict the 3D structure of proteins from its amino acid sequence. Inspired by this, we aim to elucidate structural features of synthetic single-chain polymer nanoparticles (SCNPs) based on compositional information (monomers, chain length, molecular weight, charge, and valency) by machine learning (ML). Specifically, we demonstrate the effectiveness of ML to improve the efficiency of SCNP design and uncover important polymer design attributes to mimic protein-like structural features. To start, we randomly screened over 1000 synthesized SCNPs through a combination of high-throughput dynamic light scattering (DLS) and small-angle X-ray scattering (SAXS) and compared these results to simulated protein data from the PDB. Then, utilizing evidential neural networks (ENets), we predicted, synthesized, and characterized 30 novel compact SCNPs. Incredibly, this data-driven approach yielded 58% of the predicted SCNPs with Porod exponent ≥ 3.5 as opposed to 5% of SCNPs from the random screen. Using Shapely additive explanation (SHAP) values, we further uncovered interesting contributions of monomer content on Porod exponent and radius of gyration. From this work, we have shown that an ML-guided approach proves effective for the challenging, unintuitive problem of nanoparticle design. 

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.