Salt-Dependent RNA Pseudoknot Stability: Effect of Spatial Confinement

Abstract

Macromolecules, such as RNAs, reside in crowded cell environments, which could strongly affect the folded structures and stability of RNAs. The emergence of RNA-driven phase separation in biology further stresses the potential functional roles of molecular crowding. In this work, we employed the coarse-grained model that was previously developed by us to predict 3D structures and stability of the mouse mammary tumor virus (MMTV) pseudoknot under different spatial confinements over a wide range of salt concentrations. The results show that spatial confinements can not only enhance the compactness and stability of MMTV pseudoknot structures but also weaken the dependence of the RNA structure compactness and stability on salt concentration. Based on our microscopic analyses, we found that the effect of spatial confinement on the salt-dependent RNA pseudoknot stability mainly comes through the spatial suppression of extended conformations, which are prevalent in the partially/fully unfolded states, especially at low ion concentrations. Furthermore, our comprehensive analyses revealed that the thermally unfolding pathway of the pseudoknot can be significantly modulated by spatial confinements, since the intermediate states with more extended conformations would loss favor when spatial confinements are introduced.

cgRNASP: coarse-grained statistical potentials with residue separation for RNA structure evaluation

“…HiRE-RNA [39,40], IsRNA [25,26], RACER [41,44], A k-state virtual bond model [79], A CG model with salt effect [32,33] C4' 1…”

“…HiRE-RNA [39,40], IsRNA [25,26], RACER [41,44], A k-state virtual bond model [79], A CG model with salt effect [32,33] a P, C4', and N1 are for pyrimidine, and P, C4', and N9 are for purine.…”

FebRNA: An automated fragment-ensemble-based model for building RNA 3D structures

“…In FebRNA, to identifying top-1 CG structure from a built 3D CG structure ensemble with a huge number of candidates, we developed a specific CG scoring function of cgRNASP-Feb, according to the newly released CG statistical potential of cgRNASP [62] and our CG model for RNA folding [23][24][25][26]51,52]. cgRNASP-Feb is composed of a pure cgRNASP-like statistical potential and a bonded potential from our CG RNA folding model [23][24][25][26]51,52].…”

“…In FebRNA, to identifying top-1 CG structure from a built 3D CG structure ensemble with a huge number of candidates, we developed a specific CG scoring function of cgRNASP-Feb, according to the newly released CG statistical potential of cgRNASP [62] and our CG model for RNA folding [23][24][25][26]51,52]. cgRNASP-Feb is composed of a pure cgRNASP-like statistical potential and a bonded potential from our CG RNA folding model [23][24][25][26]51,52]. The cgRNASP-like statistical potential was derived completely in the same way as that of cgRNASP with the same distance bin width and distance cutoffs, and the bonded potential was the same as that of our CG RNA folding model [23][24][25][26]51,52].…”

“…As complementary methods, various computational models have been developed to predict RNA 3D structures in silico. These models can be roughly classified into physics-based ones such as SimRNA [15,16], iFold [17], NAST [18], IsRNA [19,20], HireRNA [21], oxRNA [22], and our model with salt effect [23][24][25][26], and fragment-assembly-based ones such as MC-Fold [27], FARNA/FARFAR [28][29][30], Vfold3D [31][32][33][34], RNAComposer [35,36] and 3dRNA [37][38][39][40][41][42]. The physics-based models are generally based on various coarse-grained (CG) representations and specific force fields, and their predictions generally involve long computational time and are still only reliable for the RNAs of small size and simple topology [6,9].…”