Liquid network connectivity regulates the stability and composition of biomolecular condensates with many components

Abstract

<jats:p>One of the key mechanisms used by cells to control the spatiotemporal organization of their many components is the formation and dissolution of biomolecular condensates through liquid–liquid phase separation (LLPS). Using a minimal coarse-grained model that allows us to simulate thousands of interacting multivalent proteins, we investigate the physical parameters dictating the stability and composition of multicomponent biomolecular condensates. We demonstrate that the molecular connectivity of the condensed-liquid network—i.e., the number of weak attractive protein–protein interactions per unit of volume—determines the stability (e.g., in temperature, pH, salt concentration) of multicomponent condensates, where stability is positively correlated with connectivity. While the connectivity of scaffolds (biomolecules essential for LLPS) dominates the phase landscape, introduction of clients (species recruited via scaffold–client interactions) fine-tunes it by transforming the scaffold–scaffold bond network. Whereas low-valency clients that compete for scaffold–scaffold binding sites decrease connectivity and stability, those that bind to alternate scaffold sites not required for LLPS or that have higher-than-scaffold valencies form additional scaffold–client–scaffold bridges increasing stability. Proteins that establish more connections (via increased valencies, promiscuous binding, and topologies that enable multivalent interactions) support the stability of and are enriched within multicomponent condensates. Importantly, proteins that increase the connectivity of multicomponent condensates have higher critical points as pure systems or, if pure LLPS is unfeasible, as binary scaffold–client mixtures. Hence, critical points of accessible systems (i.e., with just a few components) might serve as a unified thermodynamic parameter to predict the composition of multicomponent condensates.</jats:p>

Ligand Effects on Phase Separation of Multivalent Macromolecules

“…Here, the divalent ligand acts as a crosslinker that enables increased networking of the multivalent macromolecules. Increased networking of multivalent scaffolds by multivalent has been demonstrated for including patchy colloidal particles (36), which are facsimiles of folded domains with stickers on their surfaces (4). Divalent ligands that bind both sticker and spacer sites of scaffolds show an intermediate effect compared to the other two divalent ligands.…”

“…The stickers-and-spacers formalism (23)(24)(25)(26)36), which we model using coarse-grained simulations, allows us to uncover key features of ligands that destabilize or stabilize phase separation via preferential binding to scaffolds in the dilute versus dense phase, respectively. The features of ligands that contribute to their ability to modulate phase separation include the valence of scaffolding binding sites on ligands, the strengths of their interactions with scaffold sites, and the type of scaffold sites (stickers versus spacers) with which they interact.…”

“…Biomolecular condensates accommodate hundreds of distinct molecular components and considerable effort is being invested to understand the determinants of compositional control of distinct condensates (1, 9,15,17,19,34,36,[44][45][46][47][48][49][50]. Partition coefficients have become the workhorse quantities that are used to map the compositional biases of condensates (29,34,48,51).…”

Sequence dependent co-phase separation of RNA-protein mixtures elucidated using molecular simulations

“…Even with the current advances in simulation methodology and the CG nature of the models being used, it is nearly impossible to apply standard free-energy based techniques to sample the phase behavior of long-chain off-lattice polymers (20,(28)(29)(30)(31). We and others have been using co-existence simulation methodology (51) to sample the phase behavior of proteins undergoing LLPS successfully and efficiently (20,32). Here, we use the same strategy, as shown in Fig.…”

“…One can attempt to obtain such in-depth information from in silico atomic resolution simulation techniques (20,27,28) but studying a macroscopic phenomenon like phase separation would require considerable computational resources making this method quite expensive and prohibitive. This prompts us to look into computational approaches based on coarse-grained (CG) models which can allow investigations into the formation of biomolecular condensates and to provide molecular-level details necessary to develop theories of phase separation making CG models an integral part of the biophysical toolkit to study phase separation (29)(30)(31)(32). We have previously developed a CG modeling framework based on the amino acid hydropathy (HPS model) to study sequence determinants of protein phase separation that does not require input from experimental data (33,34).…”