Structure of a low-population binding intermediate in protein-RNA recognition

A DNP-Supported Solid-State NMR Approach to Study Nucleic Acids In Situ Reveals Berberine-Stabilized Hoogsteen Structures in Mitochondria

Mitochondria are central to cellular bioenergetics, with the unique ability to translate and transcribe a subset of their own proteome. Given the critical importance of energy production, mitochondria seem to utilize higher-order nucleic acid structures to regulate gene expression, much like nuclei. Herein, we introduce a tailored approach to probe the formation of such structures, specifically G-quadruplexes, within intact mitochondria by using sensitivity-enhanced dynamic nuclear polarization-supported solid-state NMR (DNP-ssNMR). We acquired NMR spectra on isolated intact isotopically labeled mitochondria treated with berberine, a known high-affinity G-quadruplex stabilizer. The DNP-ssNMR data revealed spectral changes in nucleic acid sugar correlations, increased signal intensity for guanosine carbons, and enhanced Hoogsteen hydrogen bond formation, providing evidence of in vivo G-quadruplex formation in mitochondria. Together, our workflow enables the study of mitochondrial nucleic acid-ligand interactions at endogenous concentrations within biologically relevant environments by DNP-ssNMR, thus paving the way for future research into mitochondrial diseases and their potential treatments.

RNA binding tunes the conformational plasticity and intradomain stability of TDP-43 tandem RNA recognition motifs

TAR DNA binding protein 43 (TDP-43) is a nuclear RNA/DNA-binding protein with pivotal roles in RNA-related processes such as splicing, transcription, transport, and stability. The high binding affinity and specificity of TDP-43 toward its cognate RNA sequences (GU-rich) is mediated by highly conserved residues in its tandem RNA recognition motif (RRM) domains (aa: 104-263). Importantly, the loss of RNA binding to the tandem RRMs caused by physiological stressors and chemical modifications promotes cytoplasmic mislocalization and pathological aggregation of TDP-43. Despite the substantial implications of RNA binding in TDP-43 function and pathology, its precise effects on the intradomain stability, and conformational dynamics of the tandem RRMs is not properly understood. Here, we employed all-atom molecular dynamics (MD) simulations to assess the effect of RNA binding on the conformational landscape and intradomain stability of TDP-43 tandem RRMs. RNA limits the overall conformational space of the tandem RRMs and promotes intradomain stability through a combination of specific base stacking interactions and transient electrostatic interactions. In contrast, tandem RRMs exhibit a high intrinsic conformational plasticity in the absence of RNA, which, surprisingly, is accompanied by a tendency of RRM1 to adopt partially unfolded conformations. Overall, our simulations reveal how RNA binding dynamically tunes the structural and conformational landscape of TDP-43 tandem RRMs, contributing to physiological function and mitigating pathological aggregation.

Deciphering the Mystery in p300 Taz2-p53 TAD2 Recognition

Intrinsically disordered proteins (IDPs) engage in various fundamental biological activities, and their behavior is of particular importance for a better understanding of the verbose but well-organized signal transduction in cells. IDPs exhibit uniquely paradoxical features with low affinity but simultaneously high specificity in recognizing their binding targets. The transcription factor p53 plays a crucial role in cancer suppression, carrying out some of its biological functions using its disordered regions, such as N-terminal transactivation domain 2 (TAD2). Exploration of the binding and unbinding processes between proteins is challenging, and the inherently disordered properties of these regions further complicate the issue. Computer simulations are a powerful tool to complement the experiments to fill gaps to explore the binding/unbinding processes between proteins. Here, we investigated the binding mechanism between p300 Taz2 and p53 TAD2 through extensive molecular dynamics (MD) simulations using the physics-based UNited RESidue (UNRES) force field with additional Go̅-like potentials. Distance restraints extracted from the NMR-resolved structures were imposed on intermolecular residue pairs to accelerate binding simulations, in which Taz2 was immobilized in a native-like conformation and disordered TAD2 was fully free. Starting from six structures with TAD2 placed at different positions around Taz2, we observed a metastable intermediate state in which the middle helical segment of TAD2 is anchored in the binding pocket, highlighting the significance of the TAD2 helix in directing protein recognition. Physics-based binding simulations show that successful binding is achieved after a series of stages, including (1) protein collisions to initiate the formation of encounter complexes, (2) partial attachment of TAD2, and finally (3) full attachment of TAD2 to the correct binding pocket of Taz2. Furthermore, machine-learning-based PathDetect-SOM was used to identify two binding pathways, the encounter complexes, and the intermediate states.

From Intercalation to External Binding: Ru(II) Complexes with a Spiro Ligand for TAR RNA Selective Binding and HIV-1 Reverse Transcriptase Inhibition

As a typical RNA virus, the genetic information on HIV-1 is entirely stored in RNA. The reverse transcription activity of HIV-1 reverse transcriptase (RT) plays a crucial role in the replication and transmission of the virus. Non-nucleoside RT inhibitors (NNRTIs) block the function of RT by binding to the RNA binding site on RT, with very few targeting viral RNA. In this study, by transforming planar conjugated ligands into a spiro structure, we convert classical Ru(II) DNA intercalators into a nonintercalator. This enables selective binding to HIV-1 transactivation response (TAR) RNA on the outer side of nucleic acids through dual interactions involving hydrogen bonds and electrostatic attraction, effectively inhibiting HIV-1 RT and serving as a selective fluorescence probe for TAR RNA.

Impact of subtype C-specific amino acid variants on HIV-1 Tat-TAR interaction: insights from molecular modelling and dynamics

BACKGROUND

HIV-1 produces Tat, a crucial protein for transcription, viral replication, and CNS neurotoxicity. Tat interacts with TAR, enhancing HIV reverse transcription. Subtype C Tat variants (C31S, R57S, Q63E) are associated with reduced transactivation and neurovirulence compared to subtype B. However, their precise impact on Tat-TAR binding is unclear. This study investigates how these substitutions affect Tat-TAR interaction.

METHODS

We utilized molecular modelling techniques, including MODELLER, to produce precise three-dimensional structures of HIV-1 Tat protein variants. We utilized Tat subtype B as the reference or wild type, and generated Tat variants to mirror those amino acid variants found in Tat subtype C. Subtype C-specific amino acid substitutions were selected based on their role in the neuropathogenesis of HIV-1. Subsequently, we conducted molecular docking of each Tat protein variant to TAR using HDOCK, followed by molecular dynamic simulations.

RESULTS

Molecular docking results indicated that Tat subtype B (TatWt) showed the highest affinity for the TAR element (-262.07), followed by TatC31S (-261.61), TatQ63E (-256.43), TatC31S/R57S/Q63E (-238.92), and TatR57S (-222.24). However, binding free energy analysis showed higher affinities for single variants TatQ63E (-349.2 ± 10.4 kcal/mol) and TatR57S (-290.0 ± 9.6 kcal/mol) compared to TatWt (-247.9 ± 27.7 kcal/mol), while TatC31S and TatC31S/R57SQ/63E showed lower values. Interactions over the protein trajectory were also higher for TatQ63E and TatR57S compared to TatWt, TatC31S, and TatC31S/R57SQ/63E, suggesting that modifying amino acids within the Arginine/Glutamine-rich region notably affects TAR interaction. Single amino acid mutations TatR57S and TatQ63E had a significant impact, while TatC31S had minimal effect. Introducing single amino acid variants from TatWt to a more representative Tat subtype C (TatC31S/R57SQ/63E) resulted in lower predicted binding affinity, consistent with previous findings.

CONCLUSIONS

These identified amino acid positions likely contribute significantly to Tat-TAR interaction and the differential pathogenesis and neuropathogenesis observed between subtype B and subtype C. Additional experimental investigations should prioritize exploring the influence of these amino acid signatures on TAR binding to gain a comprehensive understanding of their impact on viral transactivation, potentially identifying them as therapeutic targets.

Advances in machine-learning approaches to RNA-targeted drug design

RNA molecules play multifaceted functional and regulatory roles within cells and have garnered significant attention in recent years as promising therapeutic targets. With remarkable successes achieved by artificial intelligence (AI) in different fields such as computer vision and natural language processing, there is a growing imperative to harness AI's potential in computer-aided drug design (CADD) to discover novel drug compounds that target RNA. Although machine-learning (ML) approaches have been widely adopted in the discovery of small molecules targeting proteins, the application of ML approaches to model interactions between RNA and small molecule is still in its infancy. Compared to protein-targeted drug discovery, the major challenges in ML-based RNA-targeted drug discovery stem from the scarcity of available data resources. With the growing interest and the development of curated databases focusing on interactions between RNA and small molecule, the field anticipates a rapid growth and the opening of a new avenue for disease treatment. In this review, we aim to provide an overview of recent advancements in computationally modeling RNA-small molecule interactions within the context of RNA-targeted drug discovery, with a particular emphasis on methodologies employing ML techniques.

Integrating cryo-OrbiSIMS with computational modelling and metadynamics simulations enhances RNA structure prediction at atomic resolution

The 3D architecture of RNAs governs their molecular interactions, chemical reactions, and biological functions. However, a large number of RNAs and their protein complexes remain poorly understood due to the limitations of conventional structural biology techniques in deciphering their complex structures and dynamic interactions. To address this limitation, we have benchmarked an integrated approach that combines cryogenic OrbiSIMS, a state-of-the-art solid-state mass spectrometry technique, with computational methods for modelling RNA structures at atomic resolution with enhanced precision. Furthermore, using 7SK RNP as a test case, we have successfully determined the full 3D structure of a native RNA in its apo, native and disease-remodelled states, which offers insights into the structural interactions and plasticity of the 7SK complex within these states. Overall, our study establishes cryo-OrbiSIMS as a valuable tool in the field of RNA structural biology as it enables the study of challenging, native RNA systems.

Structural and computational studies of HIV-1 RNA

Viruses remain a global threat to animals, plants, and humans. The type 1 human immunodeficiency virus (HIV-1) is a member of the retrovirus family and carries an RNA genome, which is reverse transcribed into viral DNA and further integrated into the host-cell DNA for viral replication and proliferation. The RNA structures from the HIV-1 genome provide valuable insights into the mechanisms underlying the viral replication cycle. Moreover, these structures serve as models for designing novel therapeutic approaches. Here, we review structural data on RNA from the HIV-1 genome as well as computational studies based on these structural data. The review is organized according to the type of structured RNA element which contributes to different steps in the viral replication cycle. This is followed by an overview of the HIV-1 transactivation response element (TAR) RNA as a model system for understanding dynamics and interactions in the viral RNA systems. The review concludes with a description of computational studies, highlighting the impact of biomolecular simulations in elucidating the mechanistic details of various steps in the HIV-1's replication cycle.

Native Top-Down Mass Spectrometry Uncovers Two Distinct Binding Motifs of a Functional Neomycin-Sensing Riboswitch Aptamer

Understanding how ligands bind to ribonucleic acids (RNA) is important for understanding RNA recognition in biological processes and drug development. Here, we have studied neomycin B binding to neomycin-sensing riboswitch aptamer constructs by native top-down mass spectrometry (MS) using electrospray ionization (ESI) and collisionally activated dissociation (CAD). Our MS data for a 27 nt aptamer construct reveal the binding site and ligand interactions, in excellent agreement with the structure derived from nuclear magnetic resonance (NMR) studies. Strikingly, for an extended 40 nt aptamer construct, which represents the sequence with the highest regulatory factor for riboswitch function, we identified two binding motifs for neomycin B binding, one corresponding to the bulge-loop motif of the 27 nt construct and the other one in the minor groove of the lower stem, which according to the MS data are equally populated. By replacing a noncanonical with a canonical base pair in the lower stem of the 40 nt aptamer, we can reduce binding to the minor groove motif from ∼50 to ∼30%. Conversely, the introduction of a CUG/CUG motif in the lower stem shifts the binding equilibrium in favor of minor groove binding. The MS data reveal site-specific and stoichiometry-resolved information on aminoglycoside binding to RNA that is not directly accessible by other methods and underscore the role of noncanonical base pairs in RNA recognition by aminoglycosides.

HIV-1 Tat amino acid residues that influence Tat-TAR binding affinity: a scoping review

HIV-1 remains a global health concern and to date, nearly 38 million people are living with HIV. The complexity of HIV-1 pathogenesis and its subsequent prevalence is influenced by several factors including the HIV-1 subtype. HIV-1 subtype variation extends to sequence variation in the amino acids of the HIV-1 viral proteins. Of particular interest is the transactivation of transcription (Tat) protein due to its key function in viral transcription. The Tat protein predominantly functions by binding to the transactivation response (TAR) RNA element to activate HIV-1 transcriptional elongation. Subtype-specific Tat protein sequence variation influences Tat-TAR binding affinity. Despite several studies investigating Tat-TAR binding, it is not clear which regions of the Tat protein and/or individual Tat amino acid residues may contribute to TAR binding affinity. We, therefore, conducted a scoping review on studies investigating Tat-TAR binding. We aimed to synthesize the published data to determine (1) the regions of the Tat protein that may be involved in TAR binding, (2) key Tat amino acids involved in TAR binding and (3) if Tat subtype-specific variation influences TAR binding. A total of thirteen studies met our inclusion criteria and the key findings were that (1) both N-terminal and C-terminal amino acids outside the basic domain (47-59) may be important in increasing Tat-TAR binding affinity, (2) substitution of the amino acids Lysine and Arginine (47-59) resulted in a reduction in binding affinity to TAR, and (3) none of the included studies have investigated Tat subtype-specific substitutions and therefore no commentary could be made regarding which subtype may have a higher Tat-TAR binding affinity. Future studies investigating Tat-TAR binding should therefore use full-length Tat proteins and compare subtype-specific variations. Studies of such a nature may help explain why we see differential pathogenesis and prevalence when comparing HIV-1 subtypes.

An overview of structural approaches to study therapeutic RNAs

RNAs provide considerable opportunities as therapeutic agent to expand the plethora of classical therapeutic targets, from extracellular and surface proteins to intracellular nucleic acids and its regulators, in a wide range of diseases. RNA versatility can be exploited to recognize cell types, perform cell therapy, and develop new vaccine classes. Therapeutic RNAs (aptamers, antisense nucleotides, siRNA, miRNA, mRNA and CRISPR-Cas9) can modulate or induce protein expression, inhibit molecular interactions, achieve genome editing as well as exon-skipping. A common RNA thread, which makes it very promising for therapeutic applications, is its structure, flexibility, and binding specificity. Moreover, RNA displays peculiar structural plasticity compared to proteins as well as to DNA. Here we summarize the recent advances and applications of therapeutic RNAs, and the experimental and computational methods to analyze their structure, by biophysical techniques (liquid-state NMR, scattering, reactivity, and computational simulations), with a focus on dynamic and flexibility aspects and to binding analysis. This will provide insights on the currently available RNA therapeutic applications and on the best techniques to evaluate its dynamics and reactivity.

SILCS-RNA: Toward a Structure-Based Drug Design Approach for Targeting RNAs with Small Molecules

RNA molecules can act as potential drug targets in different diseases, as their dysregulated expression or misfolding can alter various cellular processes. Noncoding RNAs account for ∼70% of the human genome, and these molecules can have complex tertiary structures that present a great opportunity for targeting by small molecules. In the present study, the site identification by ligand competitive saturation (SILCS) computational approach is extended to target RNA, termed SILCS-RNA. Extensions to the method include an enhanced oscillating excess chemical potential protocol for the grand canonical Monte Carlo calculations and individual simulations of the neutral and charged solutes from which the SILCS functional group affinity maps (FragMaps) are calculated for subsequent binding site identification and docking calculations. The method is developed and evaluated against seven RNA targets and their reported small molecule ligands. SILCS-RNA provides a detailed characterization of the functional group affinity pattern in the small molecule binding sites, recapitulating the types of functional groups present in the ligands. The developed method is also shown to be useful for identification of new potential binding sites and identifying ligand moieties that contribute to binding, granular information that can facilitate ligand design. However, limitations in the method are evident including the ability to map the regions of binding sites occupied by ligand phosphate moieties and to fully account for the wide range of conformational heterogeneity in RNA associated with binding of different small molecules, emphasizing inherent challenges associated with applying computer-aided drug design methods to RNA. While limitations are present, the current study indicates how the SILCS-RNA approach may enhance drug discovery efforts targeting RNAs with small molecules.

Mechanism of RNA recognition by a Musashi RNA-binding protein

The Musashi RNA-binding proteins (RBPs) regulate translation of target mRNAs and maintenance of cell stemness and tumorigenesis. Musashi-1 (MSI1), long considered as an intestinal and neural stem cell marker, has been more recently found to be over expressed in many cancers. It has served as an important drug target for treating acute myeloid leukemia and solid tumors such as ovarian, colorectal and bladder cancer. One of the reported binding targets of MSI1 is Numb, a negative regulator of the Notch signaling. However, the dynamic mechanism of Numb RNA binding to MSI1 remains unknown, largely hindering effective drug design targeting this critical interaction. Here, we have performed extensive all-atom microsecond-timescale simulations using a robust Gaussian accelerated molecular dynamics (GaMD) method, which successfully captured multiple times of spontaneous and highly accurate binding of the Numb RNA from bulk solvent to the MSI1 protein target site. GaMD simulations revealed that Numb RNA binding to MSI1 involved largely induced fit in both the RNA and protein. The simulations also identified important low-energy intermediate conformational states during RNA binding, in which Numb interacted mainly with the β2-β3 loop and C terminus of MSI1. The mechanistic understanding of RNA binding obtained from our GaMD simulations is expected to facilitate rational structure-based drug design targeting MSI1 and other RBPs.

Structure-based deep learning for binding site detection in nucleic acid macromolecules

Structure-based drug design (SBDD) targeting nucleic acid macromolecules, particularly RNA, is a gaining momentum research direction that already resulted in several FDA-approved compounds. Similar to proteins, one of the critical components in SBDD for RNA is the correct identification of the binding sites for putative drug candidates. RNAs share a common structural organization that, together with the dynamic nature of these molecules, makes it challenging to recognize binding sites for small molecules. Moreover, there is a need for structure-based approaches, as sequence information only does not consider conformation plasticity of nucleic acid macromolecules. Deep learning holds a great promise to resolve binding site detection problem, but requires a large amount of structural data, which is very limited for nucleic acids, compared to proteins. In this study we composed a set of ∼2000 nucleic acid-small molecule structures comprising ∼2500 binding sites, which is ∼40-times larger than previously used one, and demonstrated the first structure-based deep learning approach, BiteNet_N, to detect binding sites in nucleic acid structures. BiteNet_N operates with arbitrary nucleic acid complexes, shows the state-of-the-art performance, and can be helpful in the analysis of different conformations and mutant variants, as we demonstrated for HIV-1 TAR RNA and ATP-aptamer case studies.

Chemical reactivity and binding interactions in ribonucleic acid-peptide complexes

The covalent and noncovalent backbone binding interactions in RNA-peptide complexes were studied by DFT methods. Four RNA structures R1(GGCUAGCC), R2(AAUCGAUU), R3(GGGAUCCC), and R4(AAAGCUUU) has been selected for eight protonated peptides (DR, ER, GR, KR, NGR, RR, tmeGnd (tme), and VR) interactions based on an experimental study (Anal Chem. 2019; 91:1659-1664). Chemical reactivity theory is used to study the reactivity of eight peptides with global descriptors. Lower hardness values reflected low stability and high reactivity for the protonated peptides. DR, ER, GR, KR, NGR, RR, and VR show lower value of ω, μ while tme has high value of ω, μ. Larger highest occupied molecular orbital (HOMO)-lowest unoccupied molecular orbital (LUMO) gap for ER, GR, and KR showed greater structural stability for peptides. AutoDock and PatchDock results indicated that R1, R2, and R4 retain hairpin structures while interacting with peptide complexes. The calculated binding energies of (R1-R4)-peptide complexes from AutoDock tools are (1.49-11.12) kcal/mol. Results showed that the noncovalent interactions are stronger than the covalent interactions for R1-peptide complexes. The reason might be the transfer of proton from protonated ligand to deprotonated RNA, which has initiated the loss of the ligand. Also it has been observed that proton transfer has become energetically unfavorable in presence of additional hydrogen bonds which is predicted in the experimental results.

Reweighting of molecular simulations with explicit-solvent SAXS restraints elucidates ion-dependent RNA ensembles

Small-angle X-ray scattering (SAXS) experiments are increasingly used to probe RNA structure. A number of forward models that relate measured SAXS intensities and structural features, and that are suitable to model either explicit-solvent effects or solute dynamics, have been proposed in the past years. Here, we introduce an approach that integrates atomistic molecular dynamics simulations and SAXS experiments to reconstruct RNA structural ensembles while simultaneously accounting for both RNA conformational dynamics and explicit-solvent effects. Our protocol exploits SAXS pure-solute forward models and enhanced sampling methods to sample an heterogenous ensemble of structures, with no information towards the experiments provided on-the-fly. The generated structural ensemble is then reweighted through the maximum entropy principle so as to match reference SAXS experimental data at multiple ionic conditions. Importantly, accurate explicit-solvent forward models are used at this reweighting stage. We apply this framework to the GTPase-associated center, a relevant RNA molecule involved in protein translation, in order to elucidate its ion-dependent conformational ensembles. We show that (a) both solvent and dynamics are crucial to reproduce experimental SAXS data and (b) the resulting dynamical ensembles contain an ion-dependent fraction of extended structures.

Role of conformational heterogeneity in ligand recognition by viral RNA molecules

Ribonucleic acid (RNA) molecules are known to undergo conformational changes in response to various environmental stimuli including temperature, pH, and ligands. In particular, viral RNA molecules are a key example of conformationally adapting molecules that have evolved to switch between many functional conformations. The transactivation response element (TAR) RNA from the type-1 human immunodeficiency virus (HIV-1) is a viral RNA molecule that is being increasingly explored as a potential therapeutic target due to its role in the viral replication process. In this work, we have studied the dynamics in TAR RNA in apo and liganded states by performing explicit-solvent molecular dynamics (MD) simulations initiated with 27 distinct structures. We determined that the TAR RNA structure is significantly stabilized on ligand binding with especially decreased fluctuations in its two helices. This rigidity is further coupled with the decreased flipping of bulge nucleotides, which were observed to flip more frequently in the absence of ligands. We found that initially-distinct structures of TAR RNA converged to similar conformations on removing ligands. We also report that conformational dynamics in unliganded TAR structures leads to the formation of binding pockets capable of accommodating ligands of various sizes.

Mining for Ligandable Cavities in RNA

Identifying potential ligand binding cavities is a critical step in structure-based screening of biomolecular targets. Cavity mapping methods can detect such binding cavities; however, for ribonucleic acid (RNA) targets, determining which of the detected cavities are "ligandable" remains an unsolved challenge. In this study, we trained a set of machine learning classifiers to distinguish ligandable RNA cavities from decoy cavities. Application of our classifiers to two independent test sets demonstrated that we could recover ligandable cavities from decoys with an AUC > 0.83. Interestingly, when we applied our classifiers to a library of modeled structures of the HIV-1 transactivation response (TAR) element RNA, we found that several of the conformers that harbored cavities with high ligandability scores resembled known holo-TAR structures. On the basis of our results, we envision that our classifiers could find utility as a tool to parse RNA structures and prospectively mine for ligandable binding cavities and, in so doing, facilitate structure-based virtual screening efforts against RNA drug targets.

MD simulations reveal the basis for dynamic assembly of Hfq-RNA complexes

The conserved protein Hfq is a key factor in the RNA-mediated control of gene expression in most known bacteria. The transient intermediates Hfq forms with RNA support intricate and robust regulatory networks. In Pseudomonas, Hfq recognizes repeats of adenine–purine–any nucleotide (ARN) in target mRNAs via its distal binding side, and together with the catabolite repression control (Crc) protein, assembles into a translation–repression complex. Earlier experiments yielded static, ensemble-averaged structures of the complex, but details of its interface dynamics and assembly pathway remained elusive. Using explicit solvent atomistic molecular dynamics simulations, we modeled the extensive dynamics of the Hfq–RNA interface and found implications for the assembly of the complex. We predict that syn/anti flips of the adenine nucleotides in each ARN repeat contribute to a dynamic recognition mechanism between the Hfq distal side and mRNA targets. We identify a previously unknown binding pocket that can accept any nucleotide and propose that it may serve as a ‘status quo’ staging point, providing nonspecific binding affinity, until Crc engages the Hfq–RNA binary complex. The dynamical components of the Hfq–RNA recognition can speed up screening of the pool of the surrounding RNAs, participate in rapid accommodation of the RNA on the protein surface, and facilitate competition among different RNAs. The register of Crc in the ternary assembly could be defined by the recognition of a guanine-specific base–phosphate interaction between the first and last ARN repeats of the bound RNA. This dynamic substrate recognition provides structural rationale for the stepwise assembly of multicomponent ribonucleoprotein complexes nucleated by Hfq–RNA binding.

The Perturbed Free-Energy Landscape: Linking Ligand Binding to Biomolecular Folding

The effects of ligand binding on biomolecular conformation are crucial in drug design, enzyme mechanisms, the regulation of gene expression, and other biological processes. Descriptive models such as "lock and key", "induced fit", and "conformation selection" are common ways to interpret such interactions. Another historical model, linked equilibria, proposes that the free-energy landscape (FEL) is perturbed by the addition of ligand binding energy for the bound population of biomolecules. This principle leads to a unified, quantitative theory of ligand-induced conformation change, building upon the FEL concept. We call the map of binding free energy over biomolecular conformational space the "binding affinity landscape" (BAL). The perturbed FEL predicts/explains ligand-induced conformational changes conforming to all common descriptive models. We review recent experimental and computational studies that exemplify the perturbed FEL, with emphasis on RNA. This way of understanding ligand-induced conformation dynamics motivates new experimental and theoretical approaches to ligand design, structural biology and systems biology.

A method of incorporating rate constants as kinetic constraints in molecular dynamics simulations

From the point of view of statistical mechanics, a full characterization of a molecular system requires an accurate determination of its possible states, their populations, and the respective interconversion rates. Toward this goal, well-established methods increase the accuracy of molecular dynamics simulations by incorporating experimental information about states using structural restraints and about populations using thermodynamics restraints. However, it is still unclear how to include experimental knowledge about interconversion rates. Here, we introduce a method of imposing known rate constants as constraints in molecular dynamics simulations, which is based on a combination of the maximum-entropy and maximum-caliber principles. Starting from an existing ensemble of trajectories, obtained from either molecular dynamics or enhanced trajectory sampling, this method provides a minimally perturbed path distribution consistent with the kinetic constraints, as well as modified free energy and committor landscapes. We illustrate the application of the method to a series of model systems, including all-atom molecular simulations of protein folding. Our results show that by combining experimental rate constants and molecular dynamics simulations, this approach enables the determination of transition states, reaction mechanisms, and free energies. We anticipate that this method will extend the applicability of molecular simulations to kinetic studies in structural biology and that it will assist the development of force fields to reproduce kinetic and thermodynamic observables. Furthermore, this approach is generally applicable to a wide range of systems in biology, physics, chemistry, and material science.

Target-Directed Approaches for Screening Small Molecules against RNA Targets

RNA molecules have a variety of cellular functions that can drive disease pathologies. They are without a doubt one of the most intriguing yet controversial small-molecule drug targets. The ability to widely target RNA with small molecules could be revolutionary, once the right tools, assays, and targets are selected, thereby defining which biomolecules are targetable and what constitutes drug-like small molecules. Indeed, approaches developed over the past 5-10 years have changed the face of small molecule-RNA targeting by addressing historic concerns regarding affinity, selectivity, and structural dynamics. Presently, selective RNA-protein complex stabilizing drugs such as branaplam and risdiplam are in clinical trials for the modulation of SMN2 splicing, compounds identified from phenotypic screens with serendipitous outcomes. Fully developing RNA as a druggable target will require a target engagement-driven approach, and evolving chemical collections will be important for the industrial development of this class of target. In this review we discuss target-directed approaches that can be used to identify RNA-binding compounds and the chemical knowledge we have today of small-molecule RNA binders.

The roles of structural dynamics in the cellular functions of RNAs

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.

How to learn from inconsistencies: Integrating molecular simulations with experimental data

Molecular simulations and biophysical experiments can be used to provide independent and complementary insights into the molecular origin of biological processes. A particularly useful strategy is to use molecular simulations as a modeling tool to interpret experimental measurements, and to use experimental data to refine our biophysical models. Thus, explicit integration and synergy between molecular simulations and experiments is fundamental for furthering our understanding of biological processes. This is especially true in the case where discrepancies between measured and simulated observables emerge. In this chapter, we provide an overview of some of the core ideas behind methods that were developed to improve the consistency between experimental information and numerical predictions. We distinguish between situations where experiments are used to refine our understanding and models of specific systems, and situations where experiments are used more generally to refine transferable models. We discuss different philosophies and attempt to unify them in a single framework. Until now, such integration between experiments and simulations have mostly been applied to equilibrium data, and we discuss more recent developments aimed to analyze time-dependent or time-resolved data.

A 300-fold enhancement of imino nucleic acid resonances by hyperpolarized water provides a new window for probing RNA refolding by 1D and 2D NMR

NMR sensitivity-enhancement methods involving hyperpolarized water could be of importance for solution-state biophysical investigations. Hyperpolarized water (HyperW) can enhance the ¹H NMR signals of exchangeable sites by orders of magnitude over their thermal counterparts, while providing insight into chemical exchange and solvent accessibility at a site-resolved level. As HyperW's enhancements are achieved by exploiting fast solvent exchanges associated with minimal interscan delays, possibilities for the rapid monitoring of chemical reactions and biomolecular (re)folding are opened. HyperW NMR can also accommodate heteronuclear transfers, facilitating the rapid acquisition of 2-dimensional (2D) ¹⁵N-¹H NMR correlations, and thereby combining an enhanced spectral resolution with speed and sensitivity. This work demonstrates how these qualities can come together for the study of nucleic acids. HyperW injections were used to target the guanine-sensing riboswitch aptamer domain (GSR^apt) of the xpt-pbuX operon in Bacillus subtilis Unlike what had been observed in proteins, where residues benefited of HyperW NMR only if/when sufficiently exposed to water, these enhancements applied to every imino resonance throughout the RNA. The >300-fold enhancements observed in the resulting ¹H NMR spectra allowed us to monitor in real time the changes that GSR^apt undergoes upon binding hypoxanthine, a high-affinity interaction leading to conformational refolding on a ∼1-s timescale at 36 °C. Structural responses could be identified for several nucleotides by 1-dimensional (1D) imino ¹H NMR as well as by 2D HyperW NMR spectra acquired upon simultaneous injection of hyperpolarized water and hypoxanthine. The folding landscape revealed by this HyperW strategy for GSR^apt, is briefly discussed.

Determination of a Structural Ensemble Representing the Dynamics of a G-Quadruplex DNA

It is increasingly recognized that the structures and dynamics of G-quadruplex DNA molecules are dictated by their sequences and greatly affected by environmental factors. The core guanine tetrads (G-tetrads) coordinate cations and display a strong conformational rigidity compared with that of the connecting loops. Although long loops linking the G-tetrads are typically disfavored, when present, they provide a striking illustration of the dynamics of short, single-stranded DNA regions. In addition to their role in determining the stability of the G-quadruplex state, these loops are also interesting as potential drug targets. To characterize accurately the dynamics of this DNA state, we apply here the principles of structural ensemble determination developed in the past two decades for protein molecules to DNA molecules. We thus perform extensive molecular dynamics simulations restrained with nuclear magnetic resonance residual dipolar couplings to determine a structural ensemble of the human CEB25 minisatellite G-quadruplex, which contains a connecting loop of nine nucleotides. This structural ensemble displays a wide set of arrangements for the loop and a compact, well-defined G-quadruplex core. Our results show the importance of stacking interactions in the loop and strengthen the ability of the closing base pairs to confer a large thermodynamic stability to the G-quadruplex structure.

RNA Dynamics by NMR Spectroscopy

An ever-increasing number of functional RNAs require a mechanistic understanding. RNA function relies on changes in its structure, so-called dynamics. To reveal dynamic processes and higher energy structures, new NMR methods have been developed to elucidate these dynamics in RNA with atomic resolution. In this Review, we provide an introduction to dynamics novices and an overview of methods that access most dynamic timescales, from picoseconds to hours. Examples are provided as well as insight into theory, data acquisition and analysis for these different methods. Using this broad spectrum of methodology, unprecedented detail and invisible structures have been obtained and are reviewed here. RNA, though often more complicated and therefore neglected, also provides a great system to study structural changes, as these RNA structural changes are more easily defined-Lego like-than in proteins, hence the numerous revelations of RNA excited states.

An ultra-high affinity ligand of HIV-1 TAR reveals the RNA structure recognized by P-TEFb

The HIV-1 trans-activator protein Tat binds the trans-activation response element (TAR) to facilitate recruitment of the super elongation complex (SEC) to enhance transcription of the integrated pro-viral genome. The Tat–TAR interaction is critical for viral replication and the emergence of the virus from the latent state, therefore, inhibiting this interaction has long been pursued to discover new anti-viral or latency reversal agents. However, discovering active compounds that directly target RNA with high affinity and selectivity remains a significant challenge; limiting pre-clinical development. Here, we report the rational design of a macrocyclic peptide mimic of the arginine rich motif of Tat, which binds to TAR with low pM affinity and 100-fold selectivity against closely homologous RNAs. Despite these unprecedented binding properties, the new ligand (JB181) only moderately inhibits Tat-dependent reactivation in cells and recruitment of positive transcription elongation factor (P-TEFb) to TAR. The NMR structure of the JB181–TAR complex revealed that the ligand induces a structure in the TAR loop that closely mimics the P-TEFb/Tat1:57/AFF4/TAR complex. These results strongly suggest that high-affinity ligands which bind the UCU bulge are not likely to inhibit recruitment of the SEC and suggest that targeting of the TAR loop will be an essential feature of effective Tat inhibitors.

Design of RNA-targeting macrocyclic peptides

RNA structures play a pivotal role in many biological processes and the progression of human disease, making them an attractive target for therapeutic development. Often RNA structures operate through the formation of complexes with RNA-binding proteins, however, much like protein-protein interactions, RNA-protein interactions span large surface areas and often lack traditional druggable properties, making it challenging to target them with small molecules. Peptides provide much greater surface areas and therefore greater potential for forming specific and high affinity interactions with RNA. In this chapter, we discuss our approach for engineering peptides that bind to structured RNAs by highlighting methods and design strategies from previous successful projects aimed at inhibiting the HIV Tat-TAR interaction and the biogenesis of oncogenic microRNAs.

Face-time with TAR: Portraits of an HIV-1 RNA with diverse modes of effector recognition relevant for drug discovery

Small molecules and short peptides that potently and selectively bind RNA are rare, making the molecular structures of these complexes highly exceptional. Accordingly, several recent investigations have provided unprecedented structural insights into how peptides and proteins recognize the HIV-1 transactivation response (TAR) element, a 59-nucleotide-long, noncoding RNA segment in the 5' long terminal repeat region of viral transcripts. Here, we offer an integrated perspective on these advances by describing earlier progress on TAR binding to small molecules, and by drawing parallels to recent successes in the identification of compounds that target the hepatitis C virus internal ribosome entry site (IRES) and the flavin-mononucleotide riboswitch. We relate this work to recent progress that pinpoints specific determinants of TAR recognition by: (i) viral Tat proteins, (ii) an innovative lab-evolved TAR-binding protein, and (iii) an ultrahigh-affinity cyclic peptide. New structural details are used to model the TAR-Tat-super-elongation complex (SEC) that is essential for efficient viral transcription and represents a focal point for antiviral drug design. A key prediction is that the Tat transactivation domain makes modest contacts with the TAR apical loop, whereas its arginine-rich motif spans the entire length of the TAR major groove. This expansive interface has significant implications for drug discovery and design, and it further suggests that future lab-evolved proteins could be deployed to discover steric restriction points that block Tat-mediated recruitment of the host SEC to HIV-1 TAR.

Relative Strength of Noncovalent Interactions and Covalent Backbone Bonds in Gaseous RNA-Peptide Complexes

Interactions of ribonucleic acids (RNA) with basic ligands such as proteins or aminoglycosides play a key role in fundamental biological processes. Native top-down mass spectrometry (MS) has recently been extended to binding site mapping of RNA-ligand interactions by collisionally activated dissociation, without the need for laborious sample preparation procedures. The technique relies on the preservation of noncovalent interactions at energies that are sufficiently high to cause RNA backbone cleavage. In this study, we address the question of how many and what types of noncovalent interactions allow for binding site mapping by top-down MS. We show that proton transfer from protonated ligand to deprotonated RNA within salt bridges initiates loss of the ligand, but that proton transfer becomes energetically unfavorable in the presence of additional hydrogen bonds such that the noncovalent interactions remain stronger than the covalent RNA backbone bonds.

Combining NMR Spectroscopy and Molecular Dynamic Simulations to Solve and Analyze the Structure of Protein-RNA Complexes

Understanding the RNA binding specificity of protein is of primary interest to decipher their function in the cell. Here, we review the methodology used to solve the structures of protein-RNA complexes using solution-state NMR spectroscopy: from sample preparation to structure calculation procedures. We also describe how molecular dynamics simulations can help providing additional information on the role of key amino acid side chains and of water molecules in protein-RNA recognition.

QM/MM Calculations on Protein-RNA Complexes: Understanding Limitations of Classical MD Simulations and Search for Reliable Cost-Effective QM Methods

Although atomistic explicit-solvent Molecular Dynamics (MD) is a popular tool to study protein-RNA recognition, satisfactory MD description of protein-RNA complexes is not always achieved. Unfortunately, it is often difficult to separate MD simulation instabilities primarily caused by the simple point-charge molecular mechanics (MM) force fields from problems related to the notorious uncertainties in the starting structures. Herein, we report a series of large-scale QM/MM calculations on the U1A protein-RNA complex. This experimentally well-characterized system has an intricate protein-RNA interface, which is very unstable in MD simulations. The QM/MM calculations identify several H-bonds poorly described by the MM method and thus indicate the sources of instabilities of the U1A interface in MD simulations. The results suggest that advanced QM/MM computations could be used to indirectly rationalize problems seen in MM-based MD simulations of protein-RNA complexes. As the most accurate QM method, we employ the computationally demanding meta-GGA density functional TPSS-D3(BJ)/def2-TZVP level of theory. Because considerably faster methods would be needed to extend sampling and to study even larger protein-RNA interfaces, a set of low-cost QM/MM methods is compared to the TPSS-D3(BJ)/def2-TZVP data. The PBEh-3c and B97-3c density functional composite methods appear to be suitable for protein-RNA interfaces. In contrast, HF-3c and the tight-binding Hamiltonians DFTB3-D3 and GFN-xTB perform unsatisfactorily and do not provide any advantage over the MM description. These conclusions are supported also by similar analysis of a simple HutP protein-RNA interface, which is well-described by MD with the exception of just one H-bond. Some other methodological aspects of QM/MM calculations on protein-RNA interfaces are discussed.

Structure of HIV TAR in complex with a Lab-Evolved RRM provides insight into duplex RNA recognition and synthesis of a constrained peptide that impairs transcription

Natural and lab-evolved proteins often recognize their RNA partners with exquisite affinity. Structural analysis of such complexes can offer valuable insight into sequence-selective recognition that can be exploited to alter biological function. Here, we describe the structure of a lab-evolved RNA recognition motif (RRM) bound to the HIV-1 trans-activation response (TAR) RNA element at 1.80 Å-resolution. The complex reveals a trio of arginines in an evolved β2-β3 loop penetrating deeply into the major groove to read conserved guanines while simultaneously forming cation-π and salt-bridge contacts. The observation that the evolved RRM engages TAR within a double-stranded stem is atypical compared to most RRMs. Mutagenesis, thermodynamic analysis and molecular dynamics validate the atypical binding mode and quantify molecular contributions that support the exceptionally tight binding of the TAR-protein complex (KD,App of 2.5 ± 0.1 nM). These findings led to the hypothesis that the β2-β3 loop can function as a standalone TAR-recognition module. Indeed, short constrained peptides comprising the β2-β3 loop still bind TAR (KD,App of 1.8 ± 0.5 μM) and significantly weaken TAR-dependent transcription. Our results provide a detailed understanding of TAR molecular recognition and reveal that a lab-evolved protein can be reduced to a minimal RNA-binding peptide.

Structural basis for cross-reactivity and conformation fluctuation of the major beech pollen allergen Fag s 1

Fag s 1 is a member of the Pathogen Related protein family 10 (PR-10) and can elicit cross-reaction with IgE antibodies produced against the birch pollen allergen Bet v 1. The Nuclear Magnetic Resonance (NMR) structure of Fag s 1 is presented along with its dynamic properties. It shares 66% identity with Bet v 1 and exhibits the expected three α-helices and seven β-sheets arranged as a semi-beta barrel and exposing the residues mapped as the Bet v 1 IgE epitope. The structural dynamics of Fag s 1 were monitored on the fast and intermediate timescales, using relaxation rates. The complex dynamics of Fag s 1 are closely related to the internal cavity, and they modulate IgE and ligand binding.

Metadynamic metainference: Convergence towards force field independent structural ensembles of a disordered peptide

Metadynamic metainference has been recently introduced as a theoretical framework to determine structural ensembles by combining and weighting their noise multiple sources of experimental data with molecular mechanics force fields and metadynamics simulations. Here we build upon these initial developments to further extend and streamline the computational approach. We also show that metadynamic metainference can actually determine a structural ensemble for a disordered peptide that is essentially independent from the employed force field. We further show that it is possible to use a very computationally efficient implicit solvent force field in the place of very expensive state-of-the-art explicit solvent ones without a significant loss in accuracy.

Conformational ensembles of RNA oligonucleotides from integrating NMR and molecular simulations

RNA molecules are key players in numerous cellular processes and are characterized by a complex relationship between structure, dynamics, and function. Despite their apparent simplicity, RNA oligonucleotides are very flexible molecules, and understanding their internal dynamics is particularly challenging using experimental data alone. We show how to reconstruct the conformational ensemble of four RNA tetranucleotides by combining atomistic molecular dynamics simulations with nuclear magnetic resonance spectroscopy data. The goal is achieved by reweighting simulations using a maximum entropy/Bayesian approach. In this way, we overcome problems of current simulation methods, as well as in interpreting ensemble- and time-averaged experimental data. We determine the populations of different conformational states by considering several nuclear magnetic resonance parameters and point toward properties that are not captured by state-of-the-art molecular force fields. Although our approach is applied on a set of model systems, it is fully general and may be used to study the conformational dynamics of flexible biomolecules and to detect inaccuracies in molecular dynamics force fields.

RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview

With both catalytic and genetic functions, ribonucleic acid (RNA) is perhaps the most pluripotent chemical species in molecular biology, and its functions are intimately linked to its structure and dynamics. Computer simulations, and in particular atomistic molecular dynamics (MD), allow structural dynamics of biomolecular systems to be investigated with unprecedented temporal and spatial resolution. We here provide a comprehensive overview of the fast-developing field of MD simulations of RNA molecules. We begin with an in-depth, evaluatory coverage of the most fundamental methodological challenges that set the basis for the future development of the field, in particular, the current developments and inherent physical limitations of the atomistic force fields and the recent advances in a broad spectrum of enhanced sampling methods. We also survey the closely related field of coarse-grained modeling of RNA systems. After dealing with the methodological aspects, we provide an exhaustive overview of the available RNA simulation literature, ranging from studies of the smallest RNA oligonucleotides to investigations of the entire ribosome. Our review encompasses tetranucleotides, tetraloops, a number of small RNA motifs, A-helix RNA, kissing-loop complexes, the TAR RNA element, the decoding center and other important regions of the ribosome, as well as assorted others systems. Extended sections are devoted to RNA-ion interactions, ribozymes, riboswitches, and protein/RNA complexes. Our overview is written for as broad of an audience as possible, aiming to provide a much-needed interdisciplinary bridge between computation and experiment, together with a perspective on the future of the field.

Exploring RNA structure and dynamics through enhanced sampling simulations

RNA function is intimately related to its structural dynamics. Molecular dynamics simulations are useful for exploring biomolecular flexibility but are severely limited by the accessible timescale. Enhanced sampling methods allow this timescale to be effectively extended in order to probe biologically relevant conformational changes and chemical reactions. Here, we review the role of enhanced sampling techniques in the study of RNA systems. We discuss the challenges and promises associated with the application of these methods to force-field validation, exploration of conformational landscapes and ion/ligand-RNA interactions, as well as catalytic pathways. Important technical aspects of these methods, such as the choice of the biased collective variables and the analysis of multi-replica simulations, are examined in detail. Finally, a perspective on the role of these methods in the characterization of RNA dynamics is provided.

Swellix: a computational tool to explore RNA conformational space

Background

The sequence of nucleotides in an RNA determines the possible base pairs for an RNA fold and thus also determines the overall shape and function of an RNA. The Swellix program presented here combines a helix abstraction with a combinatorial approach to the RNA folding problem in order to compute all possible non-pseudoknotted RNA structures for RNA sequences. The Swellix program builds on the Crumple program and can include experimental constraints on global RNA structures such as the minimum number and lengths of helices from crystallography, cryoelectron microscopy, or in vivo crosslinking and chemical probing methods.

Results

The conceptual advance in Swellix is to count helices and generate all possible combinations of helices rather than counting and combining base pairs. Swellix bundles similar helices and includes improvements in memory use and efficient parallelization. Biological applications of Swellix are demonstrated by computing the reduction in conformational space and entropy due to naturally modified nucleotides in tRNA sequences and by motif searches in Human Endogenous Retroviral (HERV) RNA sequences. The Swellix motif search reveals occurrences of protein and drug binding motifs in the HERV RNA ensemble that do not occur in minimum free energy or centroid predicted structures.

Conclusions

Swellix presents significant improvements over Crumple in terms of efficiency and memory use. The efficient parallelization of Swellix enables the computation of sequences as long as 418 nucleotides with sufficient experimental constraints. Thus, Swellix provides a practical alternative to free energy minimization tools when multiple structures, kinetically determined structures, or complex RNA-RNA and RNA-protein interactions are present in an RNA folding problem.

Electronic supplementary material

The online version of this article (10.1186/s12859-017-1910-7) contains supplementary material, which is available to authorized users.

Linking functions: an additional role for an intrinsically disordered linker domain in the transcriptional coactivator CBP

The multi-domain transcriptional coactivators CBP/p300 integrate a multitude of signaling inputs, interacting with more than 400 proteins via one or more of their globular domains. While CBP/p300 function is typically considered in terms of these structured domains, about half of the protein consists of intrinsically disordered regions (IDRs) of varying length. However, these IDRs have only been thought of as linkers that allow flexible spatial arrangement of the structured domains, but recent studies have shown that similar IDRs mediate specific and critical interactions in other proteins. To examine the roles of IDRs in CBP, we performed yeast-two-hybrid screenings of placenta and lung cancer cDNA libraries, which demonstrated that the long IDR linking the KIX domain and bromodomain of CBP (termed ID3) can potentially bind to several proteins. The RNA-binding Zinc-finger protein 106 (ZFP106) detected in both libraries was identified as a novel substrate for CBP-mediated acetylation. Nuclear magnetic resonance (NMR) spectroscopy combined with cross-linking experiments and competition-binding assays showed that the fully disordered isolated ID3 transiently interacts with an IDR of ZFP106 in a fashion that disorder of both regions is maintained. These findings demonstrate that beside the linking function, ID3 can also interact with acetylation substrates of CBP.

Applications of NMR to structure determination of RNAs large and small

Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool to investigate the structure and dynamics of RNA, because many biologically important RNAs have conformationally flexible structures, which makes them difficult to crystallize. Functional, independently folded RNA domains, range in size between simple stem-loops of as few as 10-20 nucleotides, to 50-70 nucleotides, the size of tRNA and many small ribozymes, to a few hundred nucleotides, the size of more complex RNA enzymes and of the functional domains of non-coding transcripts. In this review, we discuss new methods for sample preparation, assignment strategies and structure determination for independently folded RNA domains of up to 100 kDa in molecular weight.

Dynamic Descriptions of Highly Flexible Molecules from NMR Dipolar Couplings: Physical Basis and Limitations

Biomolecules that control physiological function by changing their conformation play key roles in biology and remain poorly characterized. NMR dipolar couplings (DCs) depend intrinsically on both molecular shape and structural fluctuations, thereby providing the enticing prospect of tracking these conformational changes at atomic detail. Although this dual dependence has until now severely complicated analysis of DCs from highly dynamic systems, general approaches have recently been proposed that simplify interpretation of experimental DCs, by entirely eliminating molecular alignment from the analysis. Using simple and intuitive simulation of target ensembles, we investigate the impact of such approaches on the resulting descriptions of the conformational energy landscape. We find that ensemble descriptions of highly flexible systems derived from DCs without explicit consideration of the alignment properties of the constituent conformations can be compromised and inaccurate, despite exhibiting high correlation with experimental measurement.

Simultaneous NMR characterisation of multiple minima in the free energy landscape of an RNA UUCG tetraloop

RNA molecules in solution tend to undergo structural fluctuations of relatively large amplitude and to populate a range of different conformations some of which with low populations. It is still very challenging, however, to characterise the structures of these low populated states and to understand their functional roles. In the present study, we address this problem by using NMR residual dipolar couplings (RDCs) as structural restraints in replica-averaged metadynamics (RAM) simulations. By applying this approach to a 14-mer RNA hairpin containing the prototypical UUCG tetraloop motif, we show that it is possible to construct the free energy landscape of this RNA molecule. This free energy landscapes reveals the surprisingly rich dynamics of the UUCG tetraloop and identifies the multiple substates that exist in equilibrium owing to thermal fluctuations. The approach that we present is general and can be applied to the study of the free energy landscapes of other RNA or RNA-protein systems.