A conceptual framework for understanding phase separation and addressing open questions and challenges

Micellization: A new principle in the formation of biomolecular condensates

Phase separation is a fundamental mechanism for compartmentalization in cells and leads to the formation of biomolecular condensates, generally containing various RNA molecules. RNAs are biomolecules that can serve as suitable scaffolds for biomolecular condensates and determine their forms and functions. Many studies have focused on biomolecular condensates formed by liquid-liquid phase separation (LLPS), one type of intracellular phase separation mechanism. We recently identified that paraspeckle nuclear bodies use an intracellular phase separation mechanism called micellization of block copolymers in their formation. The paraspeckles are scaffolded by NEAT1_2 long non-coding RNAs (lncRNAs) and their partner RNA-binding proteins (NEAT1_2 RNA-protein complexes [RNPs]). The NEAT1_2 RNPs act as block copolymers and the paraspeckles assemble through micellization. In LLPS, condensates grow without bound as long as components are available and typically have spherical shapes to minimize surface tension. In contrast, the size, shape, and internal morphology of the condensates are more strictly controlled in micellization. Here, we discuss the potential importance and future perspectives of micellization of block copolymers of RNPs in cells, including the construction of designer condensates with optimal internal organization, shape, and size according to design guidelines of block copolymers.

An Interpretable Machine-Learning Algorithm to Predict Disordered Protein Phase Separation Based on Biophysical Interactions

Protein phase separation is increasingly understood to be an important mechanism of biological organization and biomaterial formation. Intrinsically disordered protein regions (IDRs) are often significant drivers of protein phase separation. A number of protein phase-separation-prediction algorithms are available, with many being specific for particular classes of proteins and others providing results that are not amenable to the interpretation of the contributing biophysical interactions. Here, we describe LLPhyScore, a new predictor of IDR-driven phase separation, based on a broad set of physical interactions or features. LLPhyScore uses sequence-based statistics from the RCSB PDB database of folded structures for these interactions, and is trained on a manually curated set of phase-separation-driving proteins with different negative training sets including the PDB and human proteome. Competitive training for a variety of physical chemical interactions shows the greatest contribution of solvent contacts, disorder, hydrogen bonds, pi–pi contacts, and kinked beta-structures to the score, with electrostatics, cation–pi contacts, and the absence of a helical secondary structure also contributing. LLPhyScore has strong phase-separation-prediction recall statistics and enables a breakdown of the contribution from each physical feature to a sequence’s phase-separation propensity, while recognizing the interdependence of many of these features. The tool should be a valuable resource for guiding experiments and providing hypotheses for protein function in normal and pathological states, as well as for understanding how specificity emerges in defining individual biomolecular condensates.

Double-stranded RNA drives SARS-CoV-2 nucleocapsid protein to undergo phase separation at specific temperatures

Nucleocapsid protein (N-protein) is required for multiple steps in betacoronaviruses replication. SARS-CoV-2-N-protein condenses with specific viral RNAs at particular temperatures making it a powerful model for deciphering RNA sequence specificity in condensates. We identify two separate and distinct double-stranded, RNA motifs (dsRNA stickers) that promote N-protein condensation. These dsRNA stickers are separately recognized by N-protein's two RNA binding domains (RBDs). RBD1 prefers structured RNA with sequences like the transcription-regulatory sequence (TRS). RBD2 prefers long stretches of dsRNA, independent of sequence. Thus, the two N-protein RBDs interact with distinct dsRNA stickers, and these interactions impart specific droplet physical properties that could support varied viral functions. Specifically, we find that addition of dsRNA lowers the condensation temperature dependent on RBD2 interactions and tunes translational repression. In contrast RBD1 sites are sequences critical for sub-genomic (sg) RNA generation and promote gRNA compression. The density of RBD1 binding motifs in proximity to TRS-L/B sequences is associated with levels of sub-genomic RNA generation. The switch to packaging is likely mediated by RBD1 interactions which generate particles that recapitulate the packaging unit of the virion. Thus, SARS-CoV-2 can achieve biochemical complexity, performing multiple functions in the same cytoplasm, with minimal protein components based on utilizing multiple distinct RNA motifs that control N-protein interactions.