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Review
, 22 (3), 273-8

Modeling Nucleic Acids

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Review

Modeling Nucleic Acids

Adelene Y L Sim et al. Curr Opin Struct Biol.

Abstract

Nucleic acids are an important class of biological macromolecules that carry out a variety of cellular roles. For many functions, naturally occurring DNA and RNA molecules need to fold into precise three-dimensional structures. Due to their self-assembling characteristics, nucleic acids have also been widely studied in the field of nanotechnology, and a diverse range of intricate three-dimensional nanostructures have been designed and synthesized. Different physical terms such as base-pairing and stacking interactions, tertiary contacts, electrostatic interactions and entropy all affect nucleic acid folding and structure. Here we review general computational approaches developed to model nucleic acid systems. We focus on four key areas of nucleic acid modeling: molecular representation, potential energy function, degrees of freedom and sampling algorithm. Appropriate choices in each of these key areas in nucleic acid modeling can effectively combine to aid interpretation of experimental data and facilitate prediction of nucleic acid structure.

Figures

Figure 1
Figure 1
This schematic diagram classifies nucleic acid modeling according to molecular representation and potential energy function; both affect computational complexity. Very detailed potentials are not applicable to molecular representations that are too coarse (dark shaded triangle). Computational complexity also depends on the number and type of degrees of freedom of the particular system that in turn depend on the potential energy function and molecular representation. Large systems with more atoms are generally more complex than small systems.
Figure 2
Figure 2
Different ways of manipulating nucleic acid degrees of freedom (DOFs) in modeling. Use of Cartesian DOFs is the default in most modeling procedures, but generally results in large sampling dimensionality (number of DOFs) for systems of biomedical or nano-engineering significance. Modeling with torsional DOFs significantly reduces sampling dimensionality, however, due to the lever-arm effect (see main text), such an approach can cause a low acceptance ratio in Monte Carlo simulations. Recently, hierarchical embedded DOFs [36] together with stochastic chain closure [35] were introduced; both facilitate efficient sampling of nucleic acid structure. Different levels of embedded rigid body motions (centers of motions are illustrated by red dots) ensures that no segment of the nucleic acid is completely rigid, to give a sampling distribution that is similar to sampling with just natural moves but with substantially improved sampling efficiency [36].

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