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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. 

To contribute towards the democratization of self-driving laboratories, we created a foundation-level guide focusing on Bayesian optimization driven experimentation and low-cost open-source automated liquid handlers.