Computational Investigation of RNA C<u>U</u>G Repeats Responsible for Myotonic Dystrophy 1

Yildirim, Ilyas; Chakraborty, Debayan; Disney, Matthew D.; Wales, David J.; Schatz, George C.

doi:10.1021/acs.jctc.5b00728

Cited by 26 publications

(43 citation statements)

References 103 publications

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“…The same results were observed in the disconnectivity graph of a model 1 × 1 UU internal loop (Figures S13 and S14). (54) This observation, however, needs to be experimentally tested in detail.…”

Section: Discussionmentioning

confidence: 99%

“…(54) DPS is an approach to scanning configurational space efficiently to discover global and local minimum states of RNA systems, and transformation pathways that cannot be accessed with regular MD simulations. In the work presented here, DPS was applied to study the 1 × 1 AA internal loops.…”

Section: Methodsmentioning

confidence: 99%

“…Molecular dynamics simulations on this construct indicated that the UU loops prefer one- or two-hydrogen bond states to a no-hydrogen bond state. (54)…”

mentioning

confidence: 99%

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Structure and Dynamics of RNA Repeat Expansions That Cause Huntington’s Disease and Myotonic Dystrophy Type 1

et al. 2017

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RNA repeat expansions cause a host of incurable, genetically defined diseases. The most common class of RNA repeats consists of trinucleotide repeats. These long, repeating transcripts fold into hairpins containing 1 × 1 internal loops that can mediate disease via a variety of mechanism(s) in which RNA is the central player. Two of these disorders are Huntington’s disease and myotonic dystrophy type 1, which are caused by r(CAG) and r(CUG) repeats, respectively. We report the structures of two RNA constructs containing three copies of a r(CAG) [r(3×CAG)] or r(CUG) [r(3×CUG)] motif that were modeled with nuclear magnetic resonance spectroscopy and simulated annealing with restrained molecular dynamics. The 1 × 1 internal loops of r(3×CAG) are stabilized by one-hydrogen bond (cis Watson–Crick/Watson–Crick) AA pairs, while those of r(3×CUG) prefer one- or two-hydrogen bond (cis Watson–Crick/Watson–Crick) UU pairs. Assigned chemical shifts for the residues depended on the identity of neighbors or next nearest neighbors. Additional insights into the dynamics of these RNA constructs were gained by molecular dynamics simulations and a discrete path sampling method. Results indicate that the global structures of the RNA are A-form and that the loop regions are dynamic. The results will be useful for understanding the dynamic trajectory of these RNA repeats but also may aid in the development of therapeutics.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Structure and Dynamics of RNA Repeat Expansions That Cause Huntington’s Disease and Myotonic Dystrophy Type 1

et al. 2017

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“…One area where FFs have shown certain degree of success is in the study of particular RNA motifs. One example of this type of work was recently published by Yildirim et al 74 who predicted the structure and thermodynamics of the 1 3 1 internal loop in CUG repeats based on MD and discrete path sampling calculations, finding a complex conformational scenario characterized by a dual base-pair scheme explaining the binding mode of drugs active against myotonic dystrophy. 75 Similar techniques were used to determine the stability of the bioactive form of anticodon stem loops of tRNA GLY iso-acceptors when the specific modification G/C 34 4A 34 (first position in the anticodon loop) is introduced.…”

Section: The Risk Of Overtrainingmentioning

confidence: 99%

Modeling, Simulations, and Bioinformatics at the Service of RNA Structure

Dans

Gallego

Balaceanu

et al. 2019

Chem

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Although chemically close to DNA, RNA can adopt a wide range of structures from regular helices to complex globular conformations, showing a complexity similar to that of proteins. The determination of the structure of RNA molecules, crucial for functional understanding, is severely handicapped by their size and flexibility, which makes the systematic use of experimental approaches difficult. Simulation techniques are also suffering from very severe problems related to the accuracy of the methods and their ability to sample a large and complex conformational landscape. Here, we systematically review recent approaches created to reduce the shortcoming of the current generation of simulation methods-from highly accurate models able to deal with small systems to coarse-grained approaches that are less accurate but applicable to dealing with large models. WHAT HAVE WE LEARNED ABOUT RNA STRUCTURE FROM QM AND QM/MM METHODS?Physics teaches us that ab initio quantum mechanics (QM) can represent with high accuracy any biomolecular system, among them RNA. Unfortunately, because of their computational cost, the practical application of ab initio QM formalisms to large systems such as RNA is often impossible. In fact, even simpler QM methods such as those based on density functional theory (DFT) fail to treat systems larger than 10 2 atoms, several orders of magnitude less than the size required to study RNAs in solution. 1 Further simplifications of the basic QM formalism, such as those implicit in semiempirical (SE) methods, can extend the range of applicability of QM theory but at the expense of an expected loss of accuracy. 2 High-level QM and DFT calculations have had a central role in the development and validation of recent RNA force fields (FFs; see below). A recent example is the B97D3/AUG-CC-PVTZ study of the backbone and glycosidic torsions by Aytenfisu et al., 3 who highlighted systematic errors in current RNA FFs that might lead to incorrect molecular dynamics (MD) trajectories. Different conclusions were reached by the group led by Sponer, who again used DFT calculations as a reference, namely that the errors in current RNA FFs are related to imbalanced hydration and not to intrinsic errors in the classical gas phase Hamiltonian. 4 The same group recently studied 46 different backbone conformations of the UpU dinucleotide step (see Figure 1) by using a variety of QM methods from the state-of-the-art CCSD(T) to the latest-generation SE algorithms, 5 providing the community with an invaluable dataset for refinement of RNA FFs. Very recently our group used DFT/MM (density functional theory and molecular mechanics) calculations to fit some dihedrals directly for QM calculations in solution, opening a new approach to using DFT calculations in the refinement of RNA FF (see the next section). 6 The Bigger PictureRNAs are the ultimate frontier of structural biology. They are large and complex molecules that can adopt complex structures displaying a wide variety of functions from carriers of genetic information to regu...

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“…In DM1, the CUG-expanded RNA forms hairpins that sequester a class of splicing regulatory RBPs – Muscleblind 1-3 (MBNL1-3) – at the periphery of the nuclear splicing speckles [80,81]. MBNL1 participates in foci formation by binding to distorted GC bases or unpaired UU bases [82–85]. Its depletion has recently been linked to mRNA mislocalization and miRNA misprocessing [86,87].…”

Section: Rna Toxicity and Foci In Dmmentioning

confidence: 99%

RNA toxicity and foci formation in microsatellite expansion diseases

Zhang

Ashizawa

2017

Current Opinion in Genetics & Development

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More than 30 incurable neurological and neuromuscular diseases are caused by simple microsatellite expansions consisted of 3–6 nucleotides. These repeats can occur in non-coding regions and often result in a dominantly inherited disease phenotype that is characteristic of a toxic RNA gain-of-function. The expanded RNA adopts unusual secondary structures, sequesters various RNA binding proteins to form insoluble nuclear foci, and causes cellular defects at a multisystem level. Nuclear foci are dynamic in size, shape and colocalization of RNA binding proteins in different expansion diseases and tissue types. This review sets to provide new insights into the disease mechanisms of RNA toxicity and foci modulation, in light of recent advancement on bi-directional transcription, antisense RNA, repeat-associated non-ATG translation and beyond.

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Computational Investigation of RNA CUG Repeats Responsible for Myotonic Dystrophy 1

Cited by 26 publications

References 103 publications

Structure and Dynamics of RNA Repeat Expansions That Cause Huntington’s Disease and Myotonic Dystrophy Type 1

Structure and Dynamics of RNA Repeat Expansions That Cause Huntington’s Disease and Myotonic Dystrophy Type 1

Modeling, Simulations, and Bioinformatics at the Service of RNA Structure

RNA toxicity and foci formation in microsatellite expansion diseases

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