Energy landscapes of homopolymeric RNAs revealed by deep unsupervised learning

Abstract

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.

Graphical abstract

Figure 1

(A) Zoomed-in view of the RNA’s backbone and uracil base. We labeled dihedral angles, which are used in featurization and dimensionality reduction of sampled conformational ensembles. (B) Schematic representation of the VAE, with the energy landscape plotted from reduced dimensions x and y of the dihedral angle from the latent space of the VAE. To see this figure in color, go online.

Figure 2

Comparative conformational energy landscapes of RNAs generated by PCA, TICA, and VAEs. All three techniques are trained on a combined data set of all the dihedral angles of four RNAs across all temperatures. Poly(G) is denoted in black, poly(A) in blue, poly(C) in red, and poly(U) in green. Shown are the projections at a T of ∼300 K from (A) PCA, (B) TICA, and (C) VAE. To see this figure in color, go online.

Figure 3

The projection of the reduced representation of dihedral angles of poly(A) is presented as (A) PCA as a function of pc1 and pc2, (B) TICA as a function of t1 and t2, and (C) VAE as a function of x and y. (D) Representative structures correspond to the different parts of the three landscapes shown in (A)–(C). To see this figure in color, go online.

Figure 4

Temperature-dependent energy landscapes generated from VAE- based projection of trajectories onto two main coordinates, x and y. (A–D) Guanine at temperatures 300, 384, 420, and 480 K. (E–H) Adenine at temperatures 300, 352, 384, and 420 K. (I–L) Cytosine at temperatures 300, 326, 350, and 381 K. (M–P) Uracil at temperatures 306, 326, 353, and 382 K. To see this figure in color, go online.

Figure 5

Structural diversity seen in VAE-based free energy landscapes of RNA at a temperature of ∼300 K. (A) Poly(G) landscape, (B) poly(G)’s major structures, (C) poly(A) landscape, (D) poly(A)’s major structures, (E) poly(C) landscape, (F) poly(C)’s major structures, (G) poly(U) landscape, and (H) poly(U)’s major structures. To see this figure in color, go online.

Figure 6

Quantifying microscopic drivers of RNA (un)folding as a function of temperature. Guanine is denoted in black, adenine in blue, cytosine in red, and uracil in green. (A) Average radius of gyration versus temperature. (B) Average number of hydrogen bonds versus temperature. (C) Stacking interactions versus temperature. (D) Average number of basepairs versus temperature. To see this figure in color, go online.

Figure 7

Correlation of hydrogen-bonding patterns with conformational dynamics of RNA as a function of temperature. (A) Pearson correlation between radius of gyration and hydrogen bond count. (B) Correlation between radius of gyration and stacking interactions in RNA. (C) Illustration of the hairpin loop structure in poly(G), highlighting stacking interactions between bases and hydrogen bonds between bases and phosphate backbone on the right side. (D) The pseudoknot structure in poly(A) is represented with base pairing shown on the right side. (E) Depiction of the knot structure in poly(C), emphasizing the hydrogen bond between the base and phosphate backbone on the right side. To see this figure in color, go online.

Figure 8

Quantifying the role of water on the conformational dynamics of RNAs. (A) Depiction of water expulsion as RNA folds into a knot structure. (B) Distribution of the amount of water within a 4 Å of RNA for all four RNAs at a T of ∼300 K. (C) Average amount of water within 4 Å across temperatures for all four RNAs. (D) Pearson correlation between radius of gyration and number of water molecules within 4 Å of RNA. (E) Correlation between the number of water molecules within 4 Å of RNA and the number of hydrogen bonds. To see this figure in color, go online.