Advances in the Structure of GGGGCC Repeat RNA Sequence and Its Interaction with Small Molecules and Protein Partners

The aberrant expansion of GGGGCC hexanucleotide repeats within the first intron of the C9orf72 gene represent the predominant genetic etiology underlying amyotrophic lateral sclerosis (ALS) and frontal temporal dementia (FTD). The transcribed r(GGGGCC)n RNA repeats form RNA foci, which recruit RNA binding proteins and impede their normal cellular functions, ultimately resulting in fatal neurodegenerative disorders. Furthermore, the non-canonical translation of the r(GGGGCC)n sequence can generate dipeptide repeats, which have been postulated as pathological causes. Comprehensive structural analyses of r(GGGGCC)n have unveiled its polymorphic nature, exhibiting the propensity to adopt dimeric, hairpin, or G-quadruplex conformations, all of which possess the capacity to interact with RNA binding proteins. Small molecules capable of binding to r(GGGGCC)n have been discovered and proposed as potential lead compounds for the treatment of ALS and FTD. Some of these molecules function in preventing RNA-protein interactions or impeding the phase transition of r(GGGGCC)n. In this review, we present a comprehensive summary of the recent advancements in the structural characterization of r(GGGGCC)n, its propensity to form RNA foci, and its interactions with small molecules and proteins. Specifically, we emphasize the structural diversity of r(GGGGCC)n and its influence on partner binding. Given the crucial role of r(GGGGCC)n in the pathogenesis of ALS and FTD, the primary objective of this review is to facilitate the development of therapeutic interventions targeting r(GGGGCC)n RNA.

Characterizing hydrogen bonds in intact RNA from MS2 bacteriophage using magic angle spinning NMR

RNA is a polymer with pivotal functions in many biological processes. RNA structure determination is thus a vital step toward understanding its function. The secondary structure of RNA is stabilized by hydrogen bonds formed between nucleotide basepairs, and it defines the positions and shapes of functional stem-loops, internal loops, bulges, and other functional and structural elements. In this work, we present a methodology for studying large intact RNA biomolecules using homonuclear ¹⁵N solid-state NMR spectroscopy. We show that proton-driven spin-diffusion experiments with long mixing times, up to 16 s, improved by the incorporation of multiple rotor-synchronous ¹H inversion pulses (termed radio-frequency dipolar recoupling pulses), reveal key hydrogen-bond contacts. In the full-length RNA isolated from MS2 phage, we observed strong and dominant contributions of guanine-cytosine Watson-Crick basepairs, and beyond these common interactions, we observe a significant contribution of the guanine-uracil wobble basepairs. Moreover, we can differentiate basepaired and non-basepaired nitrogen atoms. Using the improved technique facilitates characterization of hydrogen-bond types in intact large-scale RNA using solid-state NMR. It can be highly useful to guide secondary structure prediction techniques and possibly structure determination methods.

Advances in RNA 3D Structure Modeling Using Experimental Data

RNA is a unique bio-macromolecule that can both record genetic information and perform biological functions in a variety of molecular processes, including transcription, splicing, translation, and even regulating protein function. RNAs adopt specific three-dimensional conformations to enable their functions. Experimental determination of high-resolution RNA structures using x-ray crystallography is both laborious and demands expertise, thus, hindering our comprehension of RNA structural biology. The computational modeling of RNA structure was a milestone in the birth of bioinformatics. Although computational modeling has been greatly improved over the last decade showing many successful cases, the accuracy of such computational modeling is not only length-dependent but also varies according to the complexity of the structure. To increase credibility, various experimental data were integrated into computational modeling. In this review, we summarize the experiments that can be integrated into RNA structure modeling as well as the computational methods based on these experimental data. We also demonstrate how computational modeling can help the experimental determination of RNA structure. We highlight the recent advances in computational modeling which can offer reliable structure models using high-throughput experimental data.

Solid-state NMR spectroscopy for characterization of RNA and RNP complexes

Ribonucleic acids are driving a multitude of biological processes where they act alone or in complex with proteins (ribonucleoproteins, RNP). To understand these processes both structural and mechanistic information about RNA is necessary. Due to their conformational plasticity RNA pose a challenge for mainstream structural biology methods. Solid-state NMR (ssNMR) spectroscopy is an emerging technique that can be applied to biomolecular complexes of any size in close-to-native conditions. This review outlines recent methodological developments in ssNMR for structural characterization of RNA and protein-RNA complexes and provides relevant examples.

A solid-state NMR tool box for the investigation of ATP-fueled protein engines

Motor proteins are involved in a variety of cellular processes. Their main purpose is to convert the chemical energy released during adenosine triphosphate (ATP) hydrolysis into mechanical work. In this review, solid-state Nuclear Magnetic Resonance (NMR) approaches are discussed allowing studies of structures, conformational events and dynamic features of motor proteins during a variety of enzymatic reactions. Solid-state NMR benefits from straightforward sample preparation based on sedimentation of the proteins directly into the Magic-Angle Spinning (MAS) rotor. Protein resonance assignment is the crucial and often time-limiting step in interpreting the wealth of information encoded in the NMR spectra. Herein, potentials, challenges and limitations in resonance assignment for large motor proteins are presented, focussing on both biochemical and spectroscopic approaches. This work highlights NMR tools available to study the action of the motor domain and its coupling to functional processes, as well as to identify protein-nucleotide interactions during events such as DNA replication. Arrested protein states of reaction coordinates such as ATP hydrolysis can be trapped for NMR studies by using stable, non-hydrolysable ATP analogues that mimic the physiological relevant states as accurately as possible. Recent advances in solid-state NMR techniques ranging from Dynamic Nuclear Polarization (DNP), ³¹P-based heteronuclear correlation experiments, ¹H-detected spectra at fast MAS frequencies >100 kHz to paramagnetic NMR are summarized and their applications to the bacterial DnaB helicase from Helicobacter pylori are discussed.

High-resolution solid-state NMR spectroscopy of hydrated non-crystallized RNA

We highlight that sufficient hydration of non-crystallized RNA could provide high-resolution solid-state NMR (SSNMR) spectra, with similar spectral quality to the crystallized RNA. This leads to a greatly simplified RNA preparation approach by ethanol precipitation for high-resolution SSNMR studies. It will greatly broaden the scope of SSNMR applications to the characterization of RNAs.

Longitudinal Relaxation Optimization Enhances 1 H-Detected HSQC in Solid-State NMR Spectroscopy on Challenging Biological Systems

Solid-state (SS) NMR spectroscopy is a powerful technique for studying challenging biological systems, but it often suffers from low sensitivity. A longitudinal relaxation optimization scheme to enhance the signal sensitivity of HSQC experiments in SSNMR spectroscopy is reported. Under the proposed scheme, the ¹ H spins of ¹ H-X (¹⁵ N or ¹³ C) are selected for signal acquisition, whereas other vast ¹ H spins are flipped back to the axis of the static magnetic field to accelerate the spin recovery of the observed ¹ H spins, resulting in enhanced sensitivity. Three biological systems are used to evaluate this strategy, including a seven-transmembrane protein, an RNA, and a whole-cell sample. For all three samples, the proposed scheme largely shortens the effective ¹ H longitudinal relaxation time and results in a 1.3-2.5-fold gain in sensitivity. The selected systems are representative of challenging biological systems for observation by means of SSNMR spectroscopy; thus indicating the general applicability of this method, which is particularly important for biological samples with a short lifetime or with limited sample quantities.

Computational modeling of RNA 3D structure based on experimental data

RNA molecules are master regulators of cells. They are involved in a variety of molecular processes: they transmit genetic information, sense cellular signals and communicate responses, and even catalyze chemical reactions. As in the case of proteins, RNA function is dictated by its structure and by its ability to adopt different conformations, which in turn is encoded in the sequence. Experimental determination of high-resolution RNA structures is both laborious and difficult, and therefore the majority of known RNAs remain structurally uncharacterized. To address this problem, predictive computational methods were developed based on the accumulated knowledge of RNA structures determined so far, the physical basis of the RNA folding, and taking into account evolutionary considerations, such as conservation of functionally important motifs. However, all theoretical methods suffer from various limitations, and they are generally unable to accurately predict structures for RNA sequences longer than 100-nt residues unless aided by additional experimental data. In this article, we review experimental methods that can generate data usable by computational methods, as well as computational approaches for RNA structure prediction that can utilize data from experimental analyses. We outline methods and data types that can be potentially useful for RNA 3D structure modeling but are not commonly used by the existing software, suggesting directions for future development.