Evidence map›Paper›PMID 26629336›Full record

ArticleBMC biophysics2015

Prediction of solution properties and dynamics of RNAs by means of Brownian dynamics simulation of coarse-grained models: Ribosomal 5S RNA and phenylalanine transfer RNA.

Aarón Ayllón Benítez, José Ginés Hernández Cifre, Francisco Guillermo Díaz Baños, José García de la Torre

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Article in BMC biophysics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

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6citing papers in PubMed
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3 · Its place in the literature

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6 citing papers in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Aarón Ayllón BenítezDepartamento de Química Física, Universidad de Murcia, Murcia, 30100 Spain.
José Ginés Hernández CifreDepartamento de Química Física, Universidad de Murcia, Murcia, 30100 Spain.
Francisco Guillermo Díaz BañosDepartamento de Química Física, Universidad de Murcia, Murcia, 30100 Spain.
José García de la TorreDepartamento de Química Física, Universidad de Murcia, Murcia, 30100 Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe possibility of validating biological macromolecules with locally disordered domains like RNA against solution properties is helpful to understand their function. In this work, we present a computational scheme for predicting global properties and mimicking the internal dynamics of RNA molecules in solution. A simple coarse-grained model with one bead per nucleotide and two types of intra-molecular interactions (elastic interactions and excluded volume interactions) is used to represent the RNA chain. The elastic interactions are modeled by a set of Hooke springs that form a minimalist elastic network. The Brownian dynamics technique is employed to simulate the time evolution of the RNA conformations.

resultsThat scheme is applied to the 5S ribosomal RNA of E. Coli and the yeast phenylalanine transfer RNA. From the Brownian trajectory, several solution properties (radius of gyration, translational diffusion coefficient, and a rotational relaxation time) are calculated. For the case of yeast phenylalanine transfer RNA, the time evolution and the probability distribution of the inter-arm angle is also computed.

conclusionsThe general good agreement between our results and some experimental data indicates that the model is able to capture the tertiary structure of RNA in solution. Our simulation results also compare quite well with other numerical data. An advantage of the scheme described here is the possibility of visualizing the real time macromolecular dynamics.

Indexed as

Brownian dynamicsCoarse-grained modelDiffusion coefficientsHydrodynamicsInternal dynamicsRibosomal RNATransfer RNA

Identifiers

PMID26629336
PMCPMC4666080

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