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We develop a computational method integrating a genetic algorithm with a residue-level coarse-grained model of intrinsically disordered proteins in order to uncover the molecular origins of multiphase condensates and enable their controlled design.

PRC1 (Polycomb repressive complex 1) plays a significant role in cellular differentiation and development by repressing lineage-inappropriate genes. PRC1 proteins phase separate to form Polycomb condensates (bodies) that are multi-component hubs for silencing Polycomb target genes; however, the molecular principles that underpin the condensate assembly and biophysical properties remain unknown. Here, by using biochemical reconstitution, cellular imaging, and multiscale molecular simulations, we show that PRC1 condensates are assembled via a scaffold-client liquid-liquid phase separation (LLPS) model by which Chromobox 2 (CBX2) is the scaffold and other subunits of the CBX2-PRC1 complex act as clients. The clients induce a reentrant phase transition of CBX2 condensates in a concentration-dependent manner. The composition of the multi-component, heterotypic LLPS systems directs the assembly and biophysical properties of CBX2-PRC1 condensates and selectively promotes the formation of CBX4-PRC1 condensates, but specifically dissolves condensates of CBX6-, CBX7-, and CBX8-PRC1. Additionally, the composition of CBX2-PRC1 condensates controls the enrichment of CBX4-, CBX7-, and CBX8-PRC1 into condensates but the exclusion of CBX6-PRC1 from condensates. Our results show the composition- and stoichiometry-dependent scaffold-client assembly of multi-component PRC1 condensates and supply a conceptual framework underlying the molecular basis and dynamics of Polycomb condensate assembly.

Understanding the thermodynamic stability and metastability of materials can help us to gauge for example whether crystalline polymorphs in pharmaceutical formulations are likely to be durable. It can also help us to design experimental routes to novel phases with potentially interesting properties. In this article, we provide an overview of how thermodynamic phase behaviour can be quantified both in computer simulations and using machine-learning approaches to determining phase diagrams, as well as combinations of the two. We review the basic workflow of free-energy computations for condensed phases, including some practical implementation advice, ranging from the Frenkel-Ladd approach to thermodynamic integration and to direct-coexistence simulations. We illustrate the applications of such methods on a range of systems from materials chemistry to biological phase separation. Finally, we outline some challenges, questions and practical applications of phase-diagram determination which we believe are likely to be possible to address in the near future using such state-of-the-art free-energy calculations, which may provide fundamental insight into separation processes using multicomponent solvents.