bioRxivBiophysics
Dual RNA modulation of protein mobility and stability within phase-separated condensates
Abstract
One of the key mechanisms employed by cells to control their spatiotemporal organization is the formation and dissolution of phase-separated condensates. The balance between condensate assembly and disassembly can be critically regulated by the presence of RNA. In this work, we use a novel sequence-dependent coarse-grained model for proteins and RNA to unravel the impact of RNA in modulating the transport properties and stability of biomolecular condensates. We explore the phase behavior of several RNA-binding proteins such as FUS, hnRNPA1 and TDP-43 proteins along with that of their corresponding prion-like domains and RNA-recognition motifs, from absence to moderately high RNA concentration. By characterising the phase diagram, key molecular interactions, surface tension and transport properties of the condensates, we report a dual RNA-induced behavior: On the one hand, RNA enhances phase separation at low concentration as long as the RNA radius of gyration is comparable to that of the proteins, whilst at high concentration it inhibits the ability of proteins to self-assemble independently of its length. On the other hand, along with the stability modulation, the viscosity of the condensates can be considerably reduced at high RNA concentration as long as the length of the RNA chains is shorter than that of the proteins. Conversely, long RNA strands increase viscosity, even at high concentration, but barely modify protein self-diffusion, which mainly depends on RNA concentration and on its own effect on droplet density. On the whole, our work rationalizes the different routes by which RNA can regulate phase separation and condensate dynamics, as well as the subsequent aberrant rigidification implicated in the emergence of various neuropathologies and age-related diseases.
- assay: protein structure
- research focus: chromatin & epigenetic regulation
- research focus: cell signaling & trafficking
- approach: deep learning
- approach: statistical model
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