Using a Coarse-Grained Modeling Framework to Identify Oligomeric Motifs with Tunable Secondary Structure

Driving Forces of RNA Condensation Revealed through Coarse-Grained Modeling with Explicit Mg2

RNAs are major drivers of phase separation in the formation of biomolecular condensates, and can undergo protein-free phase separation in the presence of divalent ions or crowding agents. Much remains to be understood regarding how the complex interplay of base stacking, base pairing, electrostatics, ion interactions, and particularly structural propensities governs RNA phase behavior. Here we develop an intermediate resolution model for condensates of RNAs (iConRNA) that can capture key local and long-range structure features of dynamic RNAs and simulate their spontaneous phase transitions with Mg2+. Representing each nucleotide using 6-7 beads, iConRNA accurately captures base stacking and pairing and includes explicit Mg2+. The model does not only reproduce major conformational properties of poly(rA) and poly(rU), but also correctly folds small structured RNAs and predicts their melting temperatures. With an effective model of explicit Mg2+, iConRNA successfully recapitulates experimentally observed lower critical solution temperature phase separation of poly(rA) and triplet repeats, and critically, the nontrivial dependence of phase transitions on RNA sequence, length, concentration, and Mg2+ level. Further mechanistic analysis reveals a key role of RNA folding in modulating phase separation as well as its temperature and ion dependence, besides other driving forces such as Mg2+-phosphate interactions, base stacking, and base pairing. These studies also support iConRNA as a powerful tool for direct simulation of RNA-driven phase transitions, enabling molecular studies of how RNA conformational dynamics and its response to complex condensate environment control the phase behavior and condensate material properties.

SIGNIFICANCE STATEMENT

Dynamic RNAs and proteins are major drivers of biomolecular phase separation that has been recently discovered to underlie numerous biological processes and be involved in many human diseases. Molecular simulation has an indispensable role to play in dissecting the driving forces and regulation of biomolecular phase separation. The current work describes a high-resolution coarse-grained RNA model that is capable of describing the structure dynamics and complex sequence, concentration, temperature and ion dependent phase transitions of flexible RNAs. The study further reveals a central role of RNA folding in coordinating Mg2+-phosphate interactions, base stacking, and base pairing to drive phase separation, paving the road for studies of RNA-mediated phase separation in relevant biological contexts.

Folding Coarse-Grained Oligomer Models with PyRosetta

Non-biological foldamers are a promising class of macromolecules that share similarities to classical biopolymers such as proteins and nucleic acids. Currently, designing novel foldamers is a non-trivial process, often involving many iterations of trial synthesis and characterization until folded structures are observed. In this work, we aim to tackle these foldamer design challenges using computational modeling techniques. We developed CG PyRosetta, an extension to the popular protein folding python package, PyRosetta, which introduces coarse-grained (CG) residues into PyRosetta, enabling the folding of toy CG foldamer models. Although these models are simplified, they can help explore overarching physical hypotheses about how oligomers can form. Through systematic variation of CG parameters in these models, we can investigate various folding hypotheses at the CG scale to inform the design process of new foldamer chemistries. In this study, we demonstrate CG PyRosetta's ability to identify minimum energy structures with a diverse structural search over a range of simple models, as well as two hypothesis-driven parameter scans investigating the effects of side-chain size and internal backbone angle on secondary structures. We are able to identify several types of secondary structures from single- and double-helices to sheet-like and knot-like structures. We show how side-chain size and backbone bond angle both play an important role in the structure and energetics of these toy models. Optimal side-chain sizes promote favorable packing of side chains, while specific backbone bond angles influence the specific helix type found in folded structures.