Condensates in RNA repeat sequences are heterogeneously organized and exhibit reptation dynamics

Modulation of α-synuclein aggregation amid diverse environmental perturbation

Intrinsically disordered protein α-synuclein (αS) is implicated in Parkinson's disease due to its aberrant aggregation propensity. In a bid to identify the traits of its aggregation, here we computationally simulate the multi-chain association process of αS in aqueous as well as under diverse environmental perturbations. In particular, the aggregation of αS in aqueous and varied environmental condition led to marked concentration differences within protein aggregates, resembling liquid-liquid phase separation (LLPS). Both saline and crowded settings enhanced the LLPS propensity. However, the surface tension of αS droplet responds differently to crowders (entropy-driven) and salt (enthalpy-driven). Conformational analysis reveals that the IDP chains would adopt extended conformations within aggregates and would maintain mutually perpendicular orientations to minimize inter-chain electrostatic repulsions. The droplet stability is found to stem from a diminished intra-chain interactions in the C-terminal regions of αS, fostering inter-chain residue-residue interactions. Intriguingly, a graph theory analysis identifies small-world-like networks within droplets across environmental conditions, suggesting the prevalence of a consensus interaction patterns among the chains. Together these findings suggest a delicate balance between molecular grammar and environment-dependent nuanced aggregation behavior of αS.

Constrained dynamics of DNA oligonucleotides in phase-separated droplets

Understanding the dynamics of biomolecules in complex environments is crucial for elucidating the effect of condensed and heterogeneous environments on their functional properties. A relevant environment-and one that can also be mimicked easily in vitro-is that of phase-separated droplets. While phase-separated droplet systems have been shown to compartmentalize a wide range of functional biomolecules, the effects of internal structuration of droplets on the dynamics and mobility of internalized molecules remain poorly understood. Here, we use fluorescence correlation spectroscopy to measure the dynamics of short oligonucleotides encapsulated within two representative kinds of uncharged and charged phase-separated droplets. We find that the internal structuration controls the oligonucleotide dynamics in these droplets, revealed by measuring physical parameters at high spatiotemporal resolution. By varying oligonucleotide length and salt concentrations (and thereby charge screening), we found that the dynamics are significantly affected in the noncharged droplets compared to the charged system. Our work lays the foundation for unraveling and quantifying the physical parameters governing biomolecular transport in the condensed environment.

Stick-slip unfolding favors self-association of expanded HTT mRNA

In Huntington's Disease (HD) and related disorders, expansion of CAG trinucleotide repeats produces a toxic gain of function in affected neurons. Expanded huntingtin (expHTT) mRNA forms aggregates that sequester essential RNA binding proteins, dysregulating mRNA processing and translation. The physical basis of RNA aggregation has been difficult to disentangle owing to the heterogeneous structure of the CAG repeats. Here, we probe the folding and unfolding pathways of expHTT mRNA using single-molecule force spectroscopy. Whereas normal HTT mRNAs unfold reversibly and cooperatively, expHTT mRNAs with 20 or 40 CAG repeats slip and unravel non-cooperatively at low tension. Slippage of CAG base pairs is punctuated by concerted rearrangement of adjacent CCG trinucleotides, trapping partially folded structures that readily base pair with another RNA strand. We suggest that the conformational entropy of the CAG repeats, combined with stable CCG base pairs, creates a stick-slip behavior that explains the aggregation propensity of expHTT mRNA.

Energy landscapes of homopolymeric RNAs revealed by deep unsupervised learning

Conformational dynamics of RNA plays important roles in a variety of cellular functions such as transcriptional regulation, catalysis, scaffolding, and sensing. Recently, RNAs with low-complexity sequences have been shown to phase separate and form condensate phases similar to lowcomplexity protein domains. The affinity for phase separation and the material characteristics of RNA condensates are strongly dependent on sequence composition and patterning. We hypothesize that differences in the affinities for RNA phase separation can be uncovered by studying sequence-dependent conformational dynamics of single RNA chains. To this end, we have employed atomistic simulations and deep dimensionality reduction techniques to map temperature-dependent conformational free energy landscapes for 20 base-long homopolymeric RNA sequences: poly(U), poly(G), poly(C), and poly(A). The energy landscapes of homopolymeric RNAs reveal a plethora of metastable states with qualitatively different populations stemming from differences in base chemistry. Through detailed analysis of base, phosphate, and sugar interactions, we show that experimentally observed temperature-driven shifts in metastable state populations align with experiments on RNA phase transitions. Specifically, we find that the thermodynamics of unfolding of homopolymeric RNA follows the poly(G) > poly(A) > poly(C) > poly(U) order of stability, mirroring the propensity of RNA to form condensates. To conclude, this work shows that at least for homopolymeric RNA sequences the single-chain conformational dynamics contains sufficient information for predicting and quantifying condensate forming affinities of RNAs. Thus, we anticipate that atomically detailed studies of temeprature -dependent energy landscapes of RNAs will be a useful guide for understanding the propensity of various RNA molecules to form condensates.

Kinetic trapping organizes actin filaments within liquid-like protein droplets

Several actin-binding proteins (ABPs) phase separate to form condensates capable of curating the actin network shapes. Here, we use computational modeling to understand the principles of actin network organization within VASP condensate droplets. Our simulations reveal that the different actin shapes, namely shells, rings, and mixture states are highly dependent on the kinetics of VASP-actin interactions, suggesting that they arise from kinetic trapping. Specifically, we show that reducing the residence time of VASP on actin filaments reduces degree of bundling, thereby promoting assembly of shells rather than rings. We validate the model predictions experimentally using a VASP-mutant with decreased bundling capability. Finally, we investigate the ring opening within deformed droplets and found that the sphere-to-ellipsoid transition is favored under a wide range of filament lengths while the ellipsoid-to-rod transition is only permitted when filaments have a specific range of lengths. Our findings highlight key mechanisms of actin organization within phase-separated ABPs.

Salt-Dependent Self-Association of Trinucleotide Repeat RNA Sequences

Repeat RNA sequences self-associate to form condensates. Simulations of a coarse-grained single-interaction site model for (CAG)n (n = 30 and 31) show that the salt-dependent free energy gap, ΔGS, between the ground (perfect hairpin) and the excited state (slipped hairpin (SH) with one CAG overhang) of the monomer for (n even) is the primary factor that determines the rates and yield of self-assembly. For odd n, the free energy (GS) of the ground state, which is an SH, is used to predict the self-association kinetics. As the monovalent salt concentration, CS, increases, ΔGS and GS increase, which decreases the rates of dimer formation. In contrast, ΔGS for shuffled sequences, with the same length and sequence composition as (CAG)31, is larger, which suppresses their propensities to aggregate. Although demonstrated explicitly for (CAG) polymers, the finding of inverse correlation between the free energy gap and RNA aggregation is general.

RNA Condensate as a Versatile Platform for Improving Fluorogenic RNA Aptamer Properties and Cell Imaging

Fluorogenic RNA aptamers are valuable tools for cell imaging, but they still suffer from shortcomings such as easy degradation, limited photostability, and low fluorescence enhancement. Molecular crowding conditions enable the stabilization of the structure, promotion of folding, and improvement of activity of functional RNA. Based on artificial RNA condensates, here we present a versatile platform to improve fluorogenic RNA aptamer properties and develop sensors for target analyte imaging in living cells. Using the CUG repeat as a general tag to drive phase separation, various fluorogenic aptamer-based RNA condensates (FLARE) were prepared. We show that the molecular crowding of FLARE can improve the enzymatic resistance, thermostability, photostability, and binding affinity of fluorogenic RNA aptamers. Moreover, the FLARE systems can be modularly engineered into sensors (FLARES), which demonstrate enhanced brightness and sensitivity compared to free sensors dispersed in homogeneous solution. This scalable design principle provides new insights into RNA aptamer property regulation and cellular imaging.

Quencher-free fluorescent assays by controlled DNA partitioning in the aqueous two-phase system with crowding-enhanced kinetics

Fluorescent DNA assays are promising in disease diagnosis, environmental monitoring, and drug screening, encompassing both heterogeneous and homogeneous assay types. Nevertheless, heterogeneous assays suffer from tedious washing steps and slow reaction kinetics, whereas homogenous assays require well-designed fluorophore pairs to modulate signal off/on. Herein, we developed a cost-effective and efficient quencher-free fluorescent DNA assay using an aqueous two-phase system (ATPS). Using a strand-displacement reaction, we showed that similar sensing performance could be achieved at a much lower cost. Furthermore, the unique crowding environment in ATPS accelerated strand-displacement reactions by up to six-fold and reduced DNA amplification time from 120 min to 30 min. Our assay demonstrated robust sensing in serum environments and successful detection of miRNA extracted from cells. This innovative assay format has the potential for biosensor development with both heterogeneous readout and rapid reaction kinetics in various applications.

Brewing COFFEE: A Sequence-Specific Coarse-Grained Energy Function for Simulations of DNA-Protein Complexes

DNA-protein interactions are pervasive in a number of biophysical processes ranging from transcription and gene expression to chromosome folding. To describe the structural and dynamic properties underlying these processes accurately, it is important to create transferable computational models. Toward this end, we introduce Coarse-grained Force Field for Energy Estimation, COFFEE, a robust framework for simulating DNA-protein complexes. To brew COFFEE, we integrated the energy function in the self-organized polymer model with side-chains for proteins and the three interaction site model for DNA in a modular fashion, without recalibrating any of the parameters in the original force-fields. A unique feature of COFFEE is that it describes sequence-specific DNA-protein interactions using a statistical potential (SP) derived from a data set of high-resolution crystal structures. The only parameter in COFFEE is the strength (λDNAPRO) of the DNA-protein contact potential. For an optimal choice of λDNAPRO, the crystallographic B-factors for DNA-protein complexes with varying sizes and topologies are quantitatively reproduced. Without any further readjustments to the force-field parameters, COFFEE predicts scattering profiles that are in quantitative agreement with small-angle X-ray scattering experiments, as well as chemical shifts that are consistent with NMR. We also show that COFFEE accurately describes the salt-induced unraveling of nucleosomes. Strikingly, our nucleosome simulations explain the destabilization effect of ARG to LYS mutations, which do not alter the balance of electrostatic interactions but affect chemical interactions in subtle ways. The range of applications attests to the transferability of COFFEE, and we anticipate that it would be a promising framework for simulating DNA-protein complexes at the molecular length-scale.

Interplay between self-assembly and phase separation in a polymer-complex model

We present a theoretical model for predicting the phase behavior of polymer solutions in which phase separation competes with oligomerization. Specifically, we consider scenarios in which the assembly of polymer chains into stoichiometric complexes prevents the chains from phase-separating via attractive polymer-polymer interactions. Combining statistical associating fluid theory with a two-state description of self-assembly, we find that this model exhibits rich phase behavior, including reentrance, and we show how system-specific phase diagrams can be derived graphically. Importantly, we discuss why these phase diagrams can resemble-and yet are qualitatively distinct from-phase diagrams of polymer solutions with lower critical solution temperatures.

RNAs undergo phase transitions with lower critical solution temperatures

Co-phase separation of RNAs and RNA-binding proteins drives the biogenesis of ribonucleoprotein granules. RNAs can also undergo phase transitions in the absence of proteins. However, the physicochemical driving forces of protein-free, RNA-driven phase transitions remain unclear. Here we report that various types of RNA undergo phase separation with system-specific lower critical solution temperatures. This entropically driven phase separation is an intrinsic feature of the phosphate backbone that requires Mg2+ ions and is modulated by RNA bases. RNA-only condensates can additionally undergo enthalpically favourable percolation transitions within dense phases. This is enabled by a combination of Mg2+-dependent bridging interactions between phosphate groups and RNA-specific base stacking and base pairing. Phase separation coupled to percolation can cause dynamic arrest of RNAs within condensates and suppress the catalytic activity of an RNase P ribozyme. Our work highlights the need to incorporate RNA-driven phase transitions into models for ribonucleoprotein granule biogenesis.

Formation, function, and pathology of RNP granules

Ribonucleoprotein (RNP) granules are diverse membrane-less organelles that form through multivalent RNA-RNA, RNA-protein, and protein-protein interactions between RNPs. RNP granules are implicated in many aspects of RNA physiology, but in most cases their functions are poorly understood. RNP granules can be described through four key principles. First, RNP granules often arise because of the large size, high localized concentrations, and multivalent interactions of RNPs. Second, cells regulate RNP granule formation by multiple mechanisms including posttranslational modifications, protein chaperones, and RNA chaperones. Third, RNP granules impact cell physiology in multiple manners. Finally, dysregulation of RNP granules contributes to human diseases. Outstanding issues in the field remain, including determining the scale and molecular mechanisms of RNP granule function and how granule dysfunction contributes to human disease.

Targeted RNA condensation in living cells via genetically encodable triplet repeat tags

Living systems contain various membraneless organelles that segregate proteins and RNAs via liquid-liquid phase separation. Inspired by nature, many protein-based synthetic compartments have been engineered in vitro and in living cells. Here, we introduce a genetically encoded CAG-repeat RNA tag to reprogram cellular condensate formation and recruit various non-phase-transition RNAs for cellular modulation. With the help of fluorogenic RNA aptamers, we have systematically studied the formation dynamics, spatial distributions, sizes and densities of these cellular RNA condensates. The cis- and trans-regulation functions of these CAG-repeat tags in cellular RNA localization, life time, RNA-protein interactions and gene expression have also been investigated. Considering the importance of RNA condensation in health and disease, we expect that these genetically encodable modular and self-assembled tags can be widely used for chemical biology and synthetic biology studies.

OpenABC enables flexible, simplified, and efficient GPU accelerated simulations of biomolecular condensates

Biomolecular condensates are important structures in various cellular processes but are challenging to study using traditional experimental techniques. In silico simulations with residue-level coarse-grained models strike a balance between computational efficiency and chemical accuracy. They could offer valuable insights by connecting the emergent properties of these complex systems with molecular sequences. However, existing coarse-grained models often lack easy-to-follow tutorials and are implemented in software that is not optimal for condensate simulations. To address these issues, we introduce OpenABC, a software package that greatly simplifies the setup and execution of coarse-grained condensate simulations with multiple force fields using Python scripting. OpenABC seamlessly integrates with the OpenMM molecular dynamics engine, enabling efficient simulations with performance on a single GPU that rivals the speed achieved by hundreds of CPUs. We also provide tools that convert coarse-grained configurations to all-atom structures for atomistic simulations. We anticipate that OpenABC will significantly facilitate the adoption of in silico simulations by a broader community to investigate the structural and dynamical properties of condensates.

Brewing COFFEE: A sequence-specific coarse-grained energy function for simulations of DNA-protein complexes

DNA-protein interactions are pervasive in a number of biophysical processes ranging from transcription, gene expression, to chromosome folding. To describe the structural and dynamic properties underlying these processes accurately, it is important to create transferable computational models. Toward this end, we introduce Coarse grained force field for energy estimation, COFFEE, a robust framework for simulating DNA-protein complexes. To brew COFFEE, we integrated the energy function in the Self-Organized Polymer model with Side Chains for proteins and the Three Interaction Site model for DNA in a modular fashion, without re-calibrating any of the parameters in the original force-fields. A unique feature of COFFEE is that it describes sequence-specific DNA-protein interactions using a statistical potential (SP) derived from a dataset of high-resolution crystal structures. The only parameter in COFFEE is the strength (λ D N A P R O ) of the DNA-protein contact potential. For an optimal choice of λ D N A P R O , the crystallographic B-factors for DNA-protein complexes, with varying sizes and topologies, are quantitatively reproduced. Without any further readjustments to the force-field parameters, COFFEE predicts the scattering profiles that are in quantitative agreement with SAXS experiments as well as chemical shifts that are consistent with NMR. We also show that COFFEE accurately describes the salt-induced unraveling of nucleosomes. Strikingly, our nucleosome simulations explain the destabilization effect of ARG to LYS mutations, which does not alter the balance of electrostatic interactions, but affects chemical interactions in subtle ways. The range of applications attests to the transferability of COFFEE, and we anticipate that it would be a promising framework for simulating DNA-protein complexes at the molecular length-scale.

Odd-even disparity in the population of slipped hairpins in RNA repeat sequences with implications for phase separation

Low-complexity nucleotide repeat sequences, which are implicated in several neurological disorders, undergo liquid-liquid phase separation (LLPS) provided the number of repeat units, n, exceeds a critical value. Here, we establish a link between the folding landscapes of the monomers of trinucleotide repeats and their propensity to self-associate. Simulations using a coarse-grained Self-Organized Polymer (SOP) model for (CAG)n repeats in monovalent salt solutions reproduce experimentally measured melting temperatures, which are available only for small n. By extending the simulations to large n, we show that the free-energy gap, ΔGS, between the ground state (GS) and slipped hairpin (SH) states is a predictor of aggregation propensity. The GS for even n is a perfect hairpin (PH), whereas it is a SH when n is odd. The value of ΔGS (zero for odd n) is larger for even n than for odd n. As a result, the rate of dimer formation is slower in (CAG)30 relative to (CAG)31, thus linking ΔGS to RNA-RNA association. The yield of the dimer decreases dramatically, compared to the wild type, in mutant sequences in which the population of the SH decreases substantially. Association between RNA chains is preceded by a transition to the SH even if the GS is a PH. The finding that the excitation spectrum-which depends on the exact sequence, n, and ionic conditions-is a predictor of self-association should also hold for other RNAs (mRNA for example) that undergo LLPS.

OpenABC Enables Flexible, Simplified, and Efficient GPU Accelerated Simulations of Biomolecular Condensates

Biomolecular condensates are important structures in various cellular processes but are challenging to study using traditional experimental techniques. In silico simulations with residue-level coarse-grained models strike a balance between computational efficiency and chemical accuracy. They could offer valuable insights by connecting the emergent properties of these complex systems with molecular sequences. However, existing coarse-grained models often lack easy-to-follow tutorials and are implemented in software that is not optimal for condensate simulations. To address these issues, we introduce OpenABC, a software package that greatly simplifies the setup and execution of coarse-grained condensate simulations with multiple force fields using Python scripting. OpenABC seamlessly integrates with the OpenMM molecular dynamics engine, enabling efficient simulations with performances on a single GPU that rival the speed achieved by hundreds of CPUs. We also provide tools that convert coarse-grained configurations to all-atom structures for atomistic simulations. We anticipate that Open-ABC will significantly facilitate the adoption of in silico simulations by a broader community to investigate the structural and dynamical properties of condensates. Open-ABC is available at https://github.com/ZhangGroup-MITChemistry/OpenABC.

Tuning Material States and Functionalities of G-Quadruplex-Modulated RNA-Peptide Condensates

RNA encodes sequence- and structure-dependent interactions to modulate the assembly and properties of biomolecular condensates. RNA G-quadruplexes (rG4s) formed by guanine-rich sequences can trigger the formation of liquid- or solid-like condensates that are involved in many aberrant phase transitions. However, exactly how rG4 motifs modulate different phase transitions and impart distinct material properties to condensates is unclear. Here, using RNA oligonucleotides and cationic peptides as model systems, we show that RNA-peptide condensates exhibit tunability in material properties over a wide spectrum via interactions arising from rG4 folding/unfolding kinetics. rG4-containing oligonucleotides formed strong pairwise attraction with peptides and tended to form solid-like condensates, while their less-structured non-G4 mutants formed liquid-like droplets. We find that the coupling between rG4 dissociation and RNA-peptide complex coacervation triggers solid-to-liquid transition of condensates prior to the complete unfolding of rG4s. This coupling points to a mechanism that material states of rG4-modulated condensates can be finely tuned from solid-like to liquid-like by the addition of less-structured RNA oligonucleotides, which have weak but dominant binding with peptides. We further show that the tunable material states of condensates can enhance RNA aptamer compartmentalization and RNA cleavage reactions. Our results suggest that condensates with complex properties can emerge from subtle changes in RNA oligonucleotides, contributing ways to treat dysfunctional condensates in diseases and insights into prebiotic compartmentalization.

Uncovering the mechanism for aggregation in repeat expanded RNA reveals a reentrant transition

RNA molecules aggregate under certain conditions. The resulting condensates are implicated in human neurological disorders, and can potentially be designed towards specified bulk properties in vitro. However, the mechanism for aggregation-including how aggregation properties change with sequence and environmental conditions-remains poorly understood. To address this challenge, we introduce an analytical framework based on multimer enumeration. Our approach reveals the driving force for aggregation to be the increased configurational entropy associated with the multiplicity of ways to form bonds in the aggregate. Our model uncovers rich phase behavior, including a sequence-dependent reentrant phase transition, and repeat parity-dependent aggregation. We validate our results by comparison to a complete computational enumeration of the landscape, and to previously published molecular dynamics simulations. Our work unifies and extends published results, both explaining the behavior of CAG-repeat RNA aggregates implicated in Huntington's disease, and enabling the rational design of programmable RNA condensates.

Topological Considerations in Biomolecular Condensation

Biomolecular condensation and phase separation are increasingly understood to play crucial roles in cellular compartmentalization and spatiotemporal regulation of cell machinery implicated in function and pathology. A key aspect of current research is to gain insight into the underlying physical mechanisms of these processes. Accordingly, concepts of soft matter and polymer physics, the thermodynamics of mixing, and material science have been utilized for understanding condensation mechanisms of multivalent macromolecules resulting in viscoelastic mesoscopic supramolecular assemblies. Here, we focus on two topological concepts that have recently been providing key mechanistic understanding in the field. First, we will discuss how percolation provides a network-topology-related framework that offers an interesting paradigm to understand the complex networking of dense 'connected' condensate structures and, therefore, their phase behavior. Second, we will discuss the idea of entanglement as another topological concept that has deep roots in polymer physics and important implications for biomolecular condensates. We will first review some historical developments and fundamentals of these concepts, then we will discuss current advancements and recent examples. Our discussion ends with a few open questions and the challenges to address them, hinting at unveiling fresh possibilities for the modification of existing knowledge as well as the development of new concepts relevant to condensate science.

Short PolyA RNA Homopolymers Undergo Mg2+-Mediated Kinetically Arrested Condensation

RNA-RNA interactions have increasingly been recognized for their potential to shape the mesoscale properties of biomolecular condensates, influencing morphology, organization, and material state through networking interactions. While most studies have focused on networking via Watson-Crick base pairing interactions, previous work has suggested a potential for noncanonical RNA-RNA interactions to also give rise to condensation and alter overall material state. Here, we test the phase separation of short polyA RNA (polyrA) homopolymers. We discover and characterize the potential for short polyrA sequences to form RNA condensates at lower Mg²⁺ concentrations than previously observed, which appear as internally arrested droplets with slow polyrA diffusion despite continued fusion. Our work also reveals a negative cooperativity effect between the effects of Mg²⁺ and Na⁺ on polyrA condensation. Finally, we observe that polyrA sequences can act as promoters of phase separation in mixed sequences. These results demonstrate the potential for noncanonical interactions to act as networking stickers, leading to specific condensation properties inherent to polyrA composition and structure, with implications for the fundamental physical chemistry of the system and function of polyA RNA in biology.

Systematic generation and imaging of tandem repeats reveal base-pairing properties that promote RNA aggregation

A common pathological feature of RNAs containing expanded repeat sequences is their propensity to aggregate in cells. While some repeat RNA aggregates have been shown to cause toxicity by sequestering RNA-binding proteins, the molecular mechanism of aggregation remains unclear. Here, we devised an efficient method to generate long tandem repeat DNAs de novo and applied it to systematically determine the sequence features underlying RNA aggregation. Live-cell imaging of repeat RNAs indicated that aggregation was promoted by multivalent RNA-RNA interactions via either canonical or noncanonical base pairs. While multiple runs of two consecutive base pairs were sufficient, longer runs of base pairs such as those formed by GGGGCC hexanucleotide repeats further enhanced aggregation. In summary, our study provides a unifying model for the molecular basis of repeat RNA aggregation and a generalizable approach for identifying the sequence and structural determinants underlying the distinct properties of repeat DNAs and RNAs.

Poly(ADP-ribose) in Condensates: The PARtnership of Phase Separation and Site-Specific Interactions

Biomolecular condensates are nonmembrane cellular compartments whose formation in many cases involves phase separation (PS). Despite much research interest in this mechanism of macromolecular self-organization, the concept of PS as applied to a live cell faces certain challenges. In this review, we discuss a basic model of PS and the role of site-specific interactions and percolation in cellular PS-related events. Using a multivalent poly(ADP-ribose) molecule as an example, which has high PS-driving potential due to its structural features, we consider how site-specific interactions and network formation are involved in the formation of phase-separated cellular condensates.