Designer protein assemblies with tunable phase diagrams in living cells

Protein self-organization is a hallmark of biological systems. Although the physicochemical principles governing protein–protein interactions have long been known, the principles by which such nanoscale interactions generate diverse phenotypes of mesoscale assemblies, including phase-separated compartments, remain challenging to characterize. To illuminate such principles, we create a system of two proteins designed to interact and form mesh-like assemblies. We devise a new strategy to map high-resolution phase diagrams in living cells, which provide self-assembly signatures of this system. The structural modularity of the two protein components allows straightforward modification of their molecular properties, enabling us to characterize how interaction affinity impacts the phase diagram and material state of the assemblies in vivo. The phase diagrams and their dependence on interaction affinity were captured by theory and simulations, including out-of-equilibrium effects seen in growing cells. Finally, we find that cotranslational protein binding suffices to recruit a messenger RNA to the designed micron-scale structures

Inferring Structural Ensembles of Flexible and Dynamic Macromolecules Using Bayesian, Maximum Entropy, and Minimal-Ensemble Refinement Methods

The flexible and dynamic nature of biomolecules and biomolecular complexes is essential for many cellularfunctions in living organisms but poses a challenge for experimental methods to determine high-resolutionstructural models. To meet this challenge, experiments are combined with molecular simulations. The latterpropose models for structural ensembles, and the experimental data can be used to steer these simulationsand to select ensembles that most likely underlie the experimental data. Here, we explain in detail how the“Bayesian Inference Of ENsembles” (BioEn) method can be used to refine such ensembles using a widerange of experimental data. The “Ensemble Refinement of SAXS” (EROS) method is a special case ofBioEn, inspired by the Gull-Daniell formulation of maximum entropy image processing and focusedoriginally on X-ray solution scattering experiments (SAXS) and then extended to integrative structuralmodeling. We also briefly sketch the “minimum ensemble method,” a maximum-parsimony refinementmethod that seeks to represent an ensemble with a minimal number of representative structures