Modeling nucleic acids

Sim, Adelene Y. L.; Mináry, Péter; Levitt, Michael

doi:10.1016/j.sbi.2012.03.012

Cited by 88 publications

(71 citation statements)

References 50 publications

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“…Currently, there are multiple tools and computational approaches that can describe RNA structure at various levels of detail. [14][15][16] In an important series of works [17][18][19][20][21][22][23] the thermodynamics of RNA secondary structure (i.e., the list of base pairs in the folded state of RNA) was characterized in terms of a nearest-neighbor model for calculating the free energies of various secondary-structure motifs. The nearest-neighbor model is the basis of various tools for the prediction of the secondary structure.…”

Section: Introductionmentioning

confidence: 99%

A nucleotide-level coarse-grained model of RNA

Šulc

Romano

Ouldridge

et al. 2014

The Journal of Chemical Physics

142

132

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We present a new, nucleotide-level model for RNA, oxRNA, based on the coarse-graining methodology recently developed for the oxDNA model of DNA. The model is designed to reproduce structural, mechanical, and thermodynamic properties of RNA, and the coarse-graining level aims to retain the relevant physics for RNA hybridization and the structure of single-and double-stranded RNA. In order to explore its strengths and weaknesses, we test the model in a range of nanotechnological and biological settings. Applications explored include the folding thermodynamics of a pseudoknot, the formation of a kissing loop complex, the structure of a hexagonal RNA nanoring, and the unzipping of a hairpin motif. We argue that the model can be used for efficient simulations of the structure of systems with thousands of base pairs, and for the assembly of systems of up to hundreds of base pairs. The source code implementing the model is released for public use. © 2014 AIP Publishing LLC. [http://dx

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Section: Introductionmentioning

confidence: 99%

A nucleotide-level coarse-grained model of RNA

Šulc

Romano

Ouldridge

et al. 2014

The Journal of Chemical Physics

142

132

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“…The traditional approach for determining point charges in the force field development, usually referred to as the ESP (electrostatic potential) method, 44 is to fit the point charges against the reference quantum mechanical (QM) MEP φ QM by minimizing the sum of squared residuals φ QM -φ PC calculated over the N point on a grid: (1) where φ PC is the potential produced by the point charges: (2) Examples of different implementations of this method include Merz-Kollman, 45,46 CHELP, 47 and CHELPG, 48 which mainly differ by the choice of the reference grid. These approaches typically employ Lagrange multipliers to impose a constraint on the overall molecular charge and, sometimes, on the molecular dipole moment.…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm Optimization of Point Charges in Force Field Development: Challenges and Insights

2015

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Evolutionary methods, such as genetic algorithms (GAs), provide powerful tools for optimization of the force field parameters, especially in the case of simultaneous fitting of the force field terms against extensive reference data. However, GA fitting of the nonbonded interaction parameters that includes point charges has not been explored in the literature, likely due to numerous difficulties with even a simpler problem of the least-squares fitting of the atomic point charges against a reference molecular electrostatic potential (MEP), which often demonstrates an unusually high variation of the fitted charges on buried atoms. Here, we examine the performance of the GA approach for the least-squares MEP point charge fitting, and show that the GA optimizations suffer from a magnified version of the classical buried atom effect, producing highly scattered yet correlated solutions. This effect can be understood in terms of the linearly independent, natural coordinates of the MEP fitting problem defined by the eigenvectors of the least-squares sum Hessian matrix, which are also equivalent to the eigenvectors of the covariance matrix evaluated for the scattered GA solutions. GAs quickly converge with respect to the high-curvature coordinates defined by the eigenvectors related to the leading terms of the multipole expansion, but have difficulty converging with respect to the low-curvature coordinates that mostly depend on the buried atom charges. The performance of the evolutionary techniques dramatically improves when the point charge optimization is performed using the Hessian or covariance matrix eigenvectors, an approach with a significant potential for the evolutionary optimization of the fixed-charge biomolecular force fields.

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“…Recent developments have expanded the accuracy of CG methods, especially for B-DNA, as extensively reviewed in a series of excellent articles by the groups of Levitt [119], Papoian [120], Marrink [121] and Noid [122] among others, and a book chapter by Leonarski and Trylska [123]. We will limit ourselves here to the latest (from 2013) advances in particle-based CG methods, both those oriented towards the study of DNA in biological context [124•-133], and those designed for nanocomposites (see the recent revisions of Yingling et al [134] and Ouldridge [135] [142] have been used in both fields.…”

Section: Coarse-grain Studiesmentioning

confidence: 99%

Multiscale simulation of DNA

Dans¹,

Walther

Gómez

et al. 2016

Current Opinion in Structural Biology

144

131

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DNA is not only among the most important molecules in life, but a meeting point for biology, physics and chemistry, being studied by numerous techniques. Theoretical methods can help in gaining a detailed understanding of DNA structure and function, but their practical use is hampered by the multiscale nature of this molecule. In this regard, the study of DNA covers a broad range of different topics, from sub-Angstrom details of the electronic distributions of nucleobases, to the mechanical properties of millimeter-long chromatin fibers. Some of the biological processes involving DNA occur in femtoseconds, while others require years. In this review, we describe the most recent theoretical methods that have been considered to study DNA, from the electron to the chromosome, enriching our knowledge on this fascinating molecule.

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Modeling nucleic acids

Cited by 88 publications

References 50 publications

A nucleotide-level coarse-grained model of RNA

A nucleotide-level coarse-grained model of RNA

Genetic Algorithm Optimization of Point Charges in Force Field Development: Challenges and Insights

Multiscale simulation of DNA

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