The roles of structural dynamics in the cellular functions of RNAs

Abstract

RNAs fold into 3D structures that range from simple helical elements to complex tertiary structures and quaternary ribonucleoprotein assemblies. The functions of many regulatory RNAs depend on how their 3D structure changes in response to a diverse array of cellular conditions. In this Review, we examine how the structural characterization of RNA as dynamic ensembles of conformations, which form with different probabilities and at different timescales, is improving our understanding of RNA function in cells. We discuss the mechanisms of gene regulation by microRNAs, riboswitches, ribozymes, post-transcriptional RNA modifications and RNA-binding proteins, and how the cellular environment and processes such as liquid-liquid phase separation may affect RNA folding and activity. The emerging RNA-ensemble-function paradigm is changing our perspective and understanding of RNA regulation, from in vitro to in vivo and from descriptive to predictive.

Fig. 1 |. RNA structural changes enable biological functions.

a | Riboswitches undergo metabolite- induced conformational changes to turn gene expression on or off. b | Conformational changes in the 5′ leader of the HIV-1 RNA genome drive genome dimerization and are proposed to regulate the switch between translation and packaging of the viral RNA genome. c | Alternative splicing (AS) factors bind cognate RNA-binding motifs as single-stranded RNA. Therefore, AS depends on the equilibrium between structured and unstructured conformations of the RNA at the binding site. d | Ribozymes undergo changes in tertiary structure during their catalytic cycles. e | Binding of ribosomal protein S15 induces a conformational change in ribosomal RNA to direct the ordered assembly of the ribosome. f | MicroRNAs interact with protein partners in specific conformations, which can be important for recognition, binding affinity and downstream activity. LIN28A binds to the primary let-7 microRNA or the precursor let-7 (pre-let-7) microRNA and induces a conformation that prevents binding of the microRNA processing factors Drosha and Dicer, respectively. g | Long non-coding RNAs have many cellular functions, including as scaffolds that direct RNA–protein, RNA–RNA and RNA–DNA interactions in epigenetic regulation. h | RNA can be found in phase-separated granules, in which it forms dynamic RNA–protein and RNA–RNA interactions. RNP, ribonucleoprotein.

Fig. 2 |. Dynamic ensembles describe the roles of RNA structural changes.

a | A representative RNA free energy landscape of the transactivation response element (TAR) of HIV-1 (REF.). The different conformations of TAR include a co-axially stacked conformation, a bent flexible conformation, three non-native secondary structures, including one with a base triple that represents a protein-bound conformation, and finally the unfolded conformation. The relative energetic stabilities (G⁰_i) of each conformation i are represented by the depth of the free energy minima and the corresponding abundance of each conformer is shown as the fractional population over the entire ensemble. TAR activates transcription elongation of the HIV-1 genome by forming a complex with the viral protein Tat and host factors including positive transcription elongation factor b (P-TEFb), which is part of the super elongation complex (SEC). Binding to partner molecules, changes in the cellular environment such as in the concentration of magnesium ions (Mg²⁺) and/or of crowding agents or mutations can remodel the free energy landscape and redistribute the ensemble of conformations, thereby altering RNA activity. The structural features of the TAR native structure include the lower helix (green), bulge (yellow) and upper helix and apical loop (blue). The dashed orange line represents tertiary interactions. b | Illustration of key aspects of RNA activity that can be modelled using RNA ensembles but not by static RNA structures. (Left) The strength of interactions between RNAs and ligands depends on the population of the bound conformation in the free ensemble, with a lower population corresponding to higher energy cost of redistribution (ΔG⁰_redist) and therefore lower binding affinity, and vice versa. The free RNA ensemble allowed by the long linker is broader and thus less likely to sample the bound conformation, resulting in weaker binding affinity (top). By contrast, a short linker results in a narrow ensemble centred around the bound conformation, which will result in tighter binding affinity (bottom). The free energy landscapes illustrate the extent of overlap between the free and bound ensembles for both examples. (Middle) Similarly, the degree of RNA activity correlates with the population of the active conformer in the ensemble. A small population of the active RNA conformation in the ligand-bound RNA ensemble will elicit low-level biological activity (top), whereas a large population of the active RNA conformation in the ligand-bound RNA ensemble will elicit high-level biological activity (bottom). The free energy landscapes illustrate the stability of the RNA in the active conformation for both examples. (Right) Selective pressure that favours dynamic ensembles (rather than a single conformation) can give rise to unique conservation patterns, which depend on the nature of the dynamics. In the example of sequence conservation, bases that form Watson–Crick pairs (paired red circles) in the native secondary structure form mismatches (paired red and yellow circles) in an alternative non-native secondary structure. The relative stabilities of these pairings determines the population of each secondary structure, and, thus, a mutation can affect this equilibrium by differently affecting the two structures. If the relative population of structures in the ensemble is important for function, there will be evolutionary pressure to maintain the relative stability of structures, rather than solely the stability of the native secondary structure. In the example of topological conservation, secondary-structure elements such as the length of junction linkers are important determinants of inter-helical dynamics. Thus, evolutionary pressure to maintain inter-helical dynamics can result in sequence-independent conservation of secondary structure.

Fig. 3 |. Organizing principles of RNA ensembles.

RNAs are composed of modular structural motifs with context-independent conformational preferences (ensemble modularity). Examples of RNA motifs are shown (ensemble modularity). The structural dynamics of each motif can be decomposed into a set of independent and reoccurring motional modes, which occur at different timescales and have different dependencies on sequence versus secondary structure and topology. The different modes represent transitions between conformations on different tiers in a hierarchically organized free energy landscape. Shown is an example RNA hairpin under ‘hierarchical landscapes’. In tier 0, formation and loss of tertiary interactions involving the tetraloop occurs on the slow millisecond (ms)–hour (h) timescales. In tier 1, the hairpin transitions between structures with alternative base pairing on the microsecond (μs)-ms timescale. Finally, in tier 2, the hairpin undergoes faster, inter-helical dynamics and local motions of the bases and sugars at picosecond (ps)-nanosecond (ns) timescales. Mutations (indicated with a red star) can affect different motional modes within individual motifs while minimally disrupting other motional modes or other motifs and therefore the core functionality of the RNA, thereby making RNA a highly evolvable molecule. The A-form helix represents the canonical RNA state, in which two strands of RNA are connected by Watson-Crick base pairing. A kink-turn (k-turn) is a special type of bulge that introduces a very tight kink into the backbone of the RNA. This comprises a 3-nucleotide bulge flanked by A–G and G–A base pairs.

Fig. 4 |. Ensemble-based modelling of RNA activity in vitro.

a | The RNA-binding affinity of small molecules can be calculated computationally by virtually docking small molecules to dynamic RNA ensembles. The affinity predictions deteriorate considerably when static structures are used for the screening^,. b | The tectoRNA host-guest system is a heterodimer of two structured RNAs (the host and the guest) connected by two tetraloop–tetraloop receptor tertiary contacts. The energetics of tertiary assembly of the tectoRNA host–guest system can vary considerably depending on the relative alignment of the tertiary receptors, which is dictated by helix-junction-helix (HJH) motifs such as mismatches, bulges and internal loops. The energetics of tertiary assembly is much better modelled by considering the guest HJH motifs as dynamic ensembles compared with static structures. c | G·T and G·U mismatches form wobble conformations that differ from the Watson-Crick geometry. On this basis, polymerases and ribosomes can discriminate against mispairing and reduce the error frequency during DNA replication, transcription and translation. However, the ensembles of G·T and G·U mismatches also include low-abundance, short-lived Watson-Crick-like conformations that are stabilized by rare tautomeric (blue) and anionic (green) bases. The population and lifetime of these rare species were integrated into a kinetic model, which could predict the probability of dGdT misincorporation over a wide range of conditions and for modified mutagenic bases^,. Part c is adapted from REF, Springer Nature Limited.

Fig. 5 |. The effect of cellular environments on RNA behaviour.

a | In the unbound, ground state of the fluoride riboswitch, an unstable RNA linchpin connects the upper and middle riboswitch helices. (Bottom) If fluoride is present, it binds and stabilizes the riboswitch, resulting in transcription. (Top) If fluoride is not present, the riboswitch can enter an unbound excited state, in which the unstable linchpin breaks. Linchpin breakage allows the invasion of the mRNA part of the RNA molecule and causes the riboswitch to refold and form a terminator helix, which terminates transcription. b | As mRNA is being transcribed by an RNA polymerase (RNAP), its free energy landscape changes. The elongating transcript may fold into an initial ensemble, which redistributes following the synthesis of additional nucleotides. c | The RNA post-transcriptional modification N⁶-methyladenosine (m⁶A) reduces the energetic cost (ΔG⁰_redist) of conformational changes, thereby enabling the opening of the RNA duplex structure and promoting the binding of heterogeneous nuclear ribonucleoprotein C (HNRNPC) to mRNAs at single-stranded RNA regions. Apo, ligand free; F⁻, fluoride ion. Part a is adapted from REF , Springer Nature Limited.

Fig. 6 |. RNA ensembles in cells.

a | Comparison of in vitro and in vivo ensembles described using selective 1′-hydroxyl acylation analysed by primer extension (SHAPE) data for a region of the human ACTB mRNA that contains two binding sites for zipcode binding protein 1 (ZBP1; also known as Insulin-like growth factor 2 mRNA-binding protein 1). Circle areas are proportional to the population of the conformation they represent. The data demonstrate a redistribution of the ensemble away from the dominant structure in vitro (green), in which a ZBP1 binding site is occluded towards another structure in vivo (purple), in which the ZBP1 binding site is exposed. b | RNA secondary structures can determine whether or not a recognition motif for an RNA-binding protein will be bound and active in vivo. For example, the splicing factor RNA binding fox-1 homologue 2 (RBFOX2) enhances splicing by preferentially binding its target RNAs in less structured regions. c | A new method identifies compounds that bind their target RNAs specifically within the cellular context by self-assembling into multivalent compounds using click chemistry. This has been applied to inhibit muscleblind-like 1 protein (MBNL1) binding to expanded CCUG repeats in the myotonic dystrophy type 2 mRNA. When the compound self-assembles along the repeats, the molecules link to form a single polymer (yellow lines zig-zag to indicate covalent binding). When this occurs, MBNL1 is not sequestered by the repeats and functions normally, thereby eliminating disease symptoms. Part a is adapted with permission from REF, Elsevier.