Programmable viscoelasticity in protein-RNA condensates with disordered sticker-spacer polypeptides

Abstract

Liquid-liquid phase separation of multivalent proteins and RNAs drives the formation of biomolecular condensates that facilitate membrane-free compartmentalization of subcellular processes. With recent advances, it is becoming increasingly clear that biomolecular condensates are network fluids with time-dependent material properties. Here, employing microrheology with optical tweezers, we reveal molecular determinants that govern the viscoelastic behavior of condensates formed by multivalent Arg/Gly-rich sticker-spacer polypeptides and RNA. These condensates behave as Maxwell fluids with an elastically-dominant rheological response at shorter timescales and a liquid-like behavior at longer timescales. The viscous and elastic regimes of these condensates can be tuned by the polypeptide and RNA sequences as well as their mixture compositions. Our results establish a quantitative link between the sequence- and structure-encoded biomolecular interactions at the microscopic scale and the rheological properties of the resulting condensates at the mesoscale, enabling a route to systematically probe and rationally engineer biomolecular condensates with programmable mechanics.

RNA chain length and stoichiometry govern surface tension and stability of protein-RNA condensates

“…Previously, we have reported that mixtures of RNA and Arg-rich proteins/peptides can undergo a reentrant liquid-liquid phase separation with respect to RNA:protein mixture stoichiometry ( Alshareedah et al., 2019 , 2021a , 2020 , 2021b ; Banerjee et al., 2017 ; Kaur et al., 2021 ). Other reports have indicated that such reentrant behavior is observed for several protein-RNA and protein-protein heterotypic phase separating systems ( Babinchak and Surewicz, 2020 ; Iserman et al., 2020 ; Maharana et al., 2018 ).…”

“…Subsequently, more complex topologies including multilayered, micellar, and hollow vesicles were revealed by exploring the non-stoichiometric protein-RNA ratios ( Kaur et al., 2021 ; Alshareedah et al., 2020 ). The protein sequence-dependent material properties ( Alshareedah et al., 2021a ) were recently explained utilizing the stickers-and-spacers framework. The thermodynamics of protein-RNA phase separation was further elucidated using minimalist patchy particle and residue-level models, showing that fine-tuning the interaction strengths and stoichiometries of components can indeed enhance or downregulate condensation ( Joseph et al., 2021 ).…”

“…It is important to note that most of these pioneering computational studies of biomolecular phase transitions, with a few exceptions ( Mazarakos and Zhou, 2021 ), have largely focused on computing sequence-dependent phase-coexistence diagrams ( Choi et al., 2020a ; Regy et al., 2020 ) and have not investigated the sequence and composition dependence of interfacial properties. However, it is expected that interfacial tension and viscosity of condensates govern the dynamics of droplet fusion, extent of wetting, and topology with respect to other coexisting condensates ( Kaur et al., 2021 ; Ghosh et al., 2021 ; Alshareedah et al., 2021a ). The manner in which molecular interactions, sequence patterning, and stoichiometric composition of mixed chain condensates contribute to their interfacial properties is still not well understood.…”

Motif-pattern dependence of biomolecular phase separation driven by specific interactions

“…Because the viscoelastic properties of our system depend strongly on self-binding entropy, the density and viscosity of the dense phase are not necessarily correlated. This is intriguing in light of recent experiments showing that changes in motif identity drive density and viscosity in the same direction [ 32 , 33 ], because it suggests that the specific sequence of motifs could provide an orthogonal mechanism of control that decouples density and viscosity. For our simulated polymers, all sequences expand in the dense phase to form more trans-bonds, and small-block sequences are the most compact.…”

i-RheoFT: Fourier transforming sampled functions without artefacts

“…These correlation functions are commonly evaluated only for a relatively ‘small’ number of lag-times within a finite time window, often spanning several decades (e.g., from 10 −7 sec to 10 2 sec in the case of DLS measurements); thus avoiding the risk of clogging the machines’ internal random-access memory (RAM) after a few seconds of measurement duration. The investigation of the second point has been driven by the fact that a few research groups have implemented the oversampling procedure by using different interpolation functions 20 , 21 than the one employed in the original work 19 . Therefore, here we have compared the effectiveness of the following three interpolation functions already built-in MATLAB: a cubic spline data interpolation (Spline) 22 (as the one used in 19 ), a modified Akima piecewise cubic Hermite interpolation (Makima) 23 and Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) 24 .…”