Visualizing large RNA molecules in solution

Gopal, Ajaykumar; Zhou, Z. Hong; Knobler, Charles M.; Gelbart, William M.

doi:10.1261/rna.027557.111

Cited by 99 publications

(91 citation statements)

References 81 publications

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“…Since this particular structural change was G3561A specific, it did not correlate with compensation in general by alterations in domain 2. The phenomenon of RNA:RNA interactions connecting domains protruding from a central backbone is consistent with observations of viral RNA genomes by cryo-electron microscopy, which show condensation of domains in the presence of magnesium, suggesting that tertiary interactions connect secondary-structure elements within and between RNA domains (49).…”

Section: Discussionsupporting

confidence: 87%

Requirement for Host RNA-Silencing Components and the Virus-Silencing Suppressor when Second-Site Mutations Compensate for Structural Defects in the 3′ Untranslated Region

Chattopadhyay

Stupina

Gao

et al. 2015

J Virol

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Turnip crinkle virus (TCV) contains a structured 3= region with hairpins and pseudoknots that form a complex network of noncanonical RNA:RNA interactions supporting higher-order structure critical for translation and replication. We investigated several second-site mutations in the p38 coat protein open reading frame (ORF) that arose in response to a mutation in the asymmetric loop of a critical 3= untranslated region (UTR) hairpin that disrupts local higher-order structure. All tested second-site mutations improved accumulation of TCV in conjunction with a partial reversion of the primary mutation (TCV-rev1) but had neutral or a negative effect on wild-type (wt) TCV or TCV with the primary mutation. SHAPE (selective 2=-hydroxyl acylation analyzed by primer extension) structure probing indicated that these second-site mutations reside in an RNA domain that includes most of p38 (domain 2), and evidence for RNA:RNA interactions between domain 2 and 3=UTR-containing domain 1 was found. However, second-site mutations were not compensatory in the absence of p38, which is also the TCV silencing suppressor, or in dcl-2/dcl4 or ago1/ago2 backgrounds. One second-site mutation reduced silencing suppressor activity of p38 by altering one of two GW motifs that are required for p38 binding to double-stranded RNAs (dsRNAs) and interaction with RNA-induced silencing complex (RISC)-associated AGO1/AGO2. Another second-site mutation substantially reduced accumulation of TCVrev1 in the absence of p38 or DCL2/DCL4. We suggest that the second-site mutations in the p38 ORF exert positive effects through a similar downstream mechanism, either by enhancing accumulation of beneficial DCL-produced viral small RNAs that positively regulate the accumulation of TCV-rev1 or by affecting the susceptibility of TCV-rev1 to RISC loaded with viral small RNAs. IMPORTANCEGenomes of positive-strand RNA viruses fold into high-order RNA structures. Viruses with mutations in regions critical for translation and replication often acquire second-site mutations that exert a positive compensatory effect through reestablishment of canonical base pairing with the altered region. In this study, two distal second-site mutations that individually arose in response to a primary mutation in a critical 3= UTR hairpin in the genomic RNA of turnip crinkle virus did not directly interact with the primary mutation. Although different second-site changes had different attributes, compensation was dependent on the production of the viral p38 silencing suppressor and on the presence of silencing-required DCL and AGO proteins. Our results provide an unexpected connection between a 3= UTR primary-site mutation proposed to disrupt higher-order structure and the RNA-silencing machinery.T he structure of the genomic RNA (gRNA) of positive-strand RNA viruses participates in many basic functions, including replication, translation, and regulation of the competing processes of translation and transcription (1-3). Studies examining the secondary structure and tertiary in...

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Section: Discussionsupporting

confidence: 87%

Requirement for Host RNA-Silencing Components and the Virus-Silencing Suppressor when Second-Site Mutations Compensate for Structural Defects in the 3′ Untranslated Region

Chattopadhyay

Stupina

Gao

et al. 2015

J Virol

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“…5 compare them with Eq. (24) for ( ) x Ψ and the analytical Taylor expansion to order 6 x for ( ) x Φ (see Eq. (20)).…”

Section: Numerical Solutionsmentioning

confidence: 99%

“…The large, single-stranded RNA molecules that constitute the genomes of RNA viruses ("vRNA" molecules) can be viewed as realizations of annealed branched polymers [5]. These vRNA molecules can adopt a very large number of branched "secondary structures" that are within B k T of the groundstate, as demonstrated by microscopy studies of vRNA molecules in solution [6]. Other studies demonstrated the flexibility of the secondary structure: if vRNA molecules are confined then their secondary structure becomes increasingly branched [7].…”

Section: Introductionmentioning

confidence: 99%

The confinement of an annealed branched polymer by a potential well

Grosberg

Kelly

Bruinsma

2017

Low Temperature Physics

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The Lifshitz equation for the confinement of a linear polymer in a spherical cavity of radius R has the form of the Schrödinger equation for a quantum particle trapped in a potential well with flat bottom and infinite walls at radius R. We show that the Lifshitz equation of a confined annealed branched polymer has the form of the Schrödinger equation for a quantum harmonic oscillator. The harmonic oscillator potential results from the repulsion of the many branches from the potential walls. Mathematically, it must be obtained from the solution of the equation of motion of a second, now classical, particle in a non-linear potential that depends self-consistently on the eigenvalue of the quantum oscillator. The resulting confinement energy has a 1/R 4 dependence on the confinement radius R, in agreement with scaling arguments. We discuss the application of this result to the problem of the confinement of single-stranded RNA molecules inside spherical capsids.PACS: 36.20.-r Macromolecules and polymer molecules; 87.15.H-Dynamics of biomolecules.

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“…The measurement is done in cryogenic conditions (cryo-EM) in order to reduce the radiation damage of macromolecules under study 73 . The three-dimensional structure reconstruction is done based on large number of collected two-dimensional projections.…”

Section: Rna Molecule Shapementioning

confidence: 99%

Computational modeling of RNA 3D structures, with the aid of experimental restraints

et al. 2014

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In addition to mRNAs whose primary function is transmission of genetic information from DNA to proteins, numerous other classes of RNA molecules exist, which are involved in a variety of functions, such as catalyzing biochemical reactions or performing regulatory roles. In analogy to proteins, the function of RNAs depends on their structure and dynamics, which are largely determined by the ribonucleotide 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, computational structure prediction methods were developed that simulate either the physical process of RNA structure formation (“Greek science” approach) or utilize information derived from known structures of other RNA molecules (“Babylonian science” approach). All computational methods suffer from various limitations that make them generally unreliable for structure prediction of long RNA sequences. However, in many cases, the limitations of computational and experimental methods can be overcome by combining these two complementary approaches with each other. In this work, we review computational approaches for RNA structure prediction, with emphasis on implementations (particular programs) that can utilize restraints derived from experimental analyses. We also list experimental approaches, whose results can be relatively easily used by computational methods. Finally, we describe case studies where computational and experimental analyses were successfully combined to determine RNA structures that would remain out of reach for each of these approaches applied separately.

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Visualizing large RNA molecules in solution

Cited by 99 publications

References 81 publications

Requirement for Host RNA-Silencing Components and the Virus-Silencing Suppressor when Second-Site Mutations Compensate for Structural Defects in the 3′ Untranslated Region

Requirement for Host RNA-Silencing Components and the Virus-Silencing Suppressor when Second-Site Mutations Compensate for Structural Defects in the 3′ Untranslated Region

The confinement of an annealed branched polymer by a potential well

Computational modeling of RNA 3D structures, with the aid of experimental restraints

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