Dataset Information

Simple Elastic Network Models for Exhaustive Analysis of Long Double-Stranded DNA Dynamics with Sequence Geometry Dependence.

ABSTRACT: Simple elastic network models of DNA were developed to reveal the structure-dynamics relationships for several nucleotide sequences. First, we propose a simple all-atom elastic network model of DNA that can explain the profiles of temperature factors for several crystal structures of DNA. Second, we propose a coarse-grained elastic network model of DNA, where each nucleotide is described only by one node. This model could effectively reproduce the detailed dynamics obtained with the all-atom elastic network model according to the sequence-dependent geometry. Through normal-mode analysis for the coarse-grained elastic network model, we exhaustively analyzed the dynamic features of a large number of long DNA sequences, approximately ?150 bp in length. These analyses revealed positive correlations between the nucleosome-forming abilities and the inter-strand fluctuation strength of double-stranded DNA for several DNA sequences.

SUBMITTER: Isami S

PROVIDER: S-EPMC4666469 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Simple Elastic Network Models for Exhaustive Analysis of Long Double-Stranded DNA Dynamics with Sequence Geometry Dependence.

Isami Shuhei S Sakamoto Naoaki N Nishimori Hiraku H Awazu Akinori A

PloS one 20151201 12

Simple elastic network models of DNA were developed to reveal the structure-dynamics relationships for several nucleotide sequences. First, we propose a simple all-atom elastic network model of DNA that can explain the profiles of temperature factors for several crystal structures of DNA. Second, we propose a coarse-grained elastic network model of DNA, where each nucleotide is described only by one node. This model could effectively reproduce the detailed dynamics obtained with the all-atom ela ...[more]

PMID: 26624614

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Project description:ClpXP complex is an ATP-dependent mitochondrial matrix protease that binds, unfolds, translocates, and subsequently degrades specific protein substrates. Its mechanisms of operation are still being debated, and several have been proposed, including the sequential translocation of two residues (SC/2R), six residues (SC/6R), and even long-pass probabilistic models. Therefore, it has been suggested to employ biophysical-computational approaches that can determine the kinetics and thermodynamics of the translocation. In this sense, and based on the apparent inconsistency between structural and functional studies, we propose to apply biophysical approaches based on elastic network models (ENM) to study the intrinsic dynamics of the theoretically most probable hydrolysis mechanism. The proposed models ENM suggest that the ClpP region is decisive for the stabilization of the ClpXP complex, contributing to the flexibility of the residues adjacent to the pore, favoring the increase in pore size and, therefore, with the energy of interaction of its residues with a larger portion of the substrate. It is predicted that the complex may undergo a stable configurational change once assembled and that the deformability of the system once assembled is oriented, to increase the rigidity of the domains of each region (ClpP and ClpX) and to gain flexibility of the pore. Our predictions could suggest under the conditions of this study the mechanism of the interaction of the system, of which the substrate passes through the unfolding of the pore in parallel with a folding of the bottleneck. The variations in the distance calculated by molecular dynamics could allow the passage of a substrate with a size equivalent to ∼3 residues. The theoretical behavior of the pore and the stability and energy of binding to the substrate based on ENM models suggest that in this system, there are thermodynamic, structural, and configurational conditions that allow a possible translocation mechanism that is not strictly sequential.

Dataset Information

Simple Elastic Network Models for Exhaustive Analysis of Long Double-Stranded DNA Dynamics with Sequence Geometry Dependence.

Publications

Simple Elastic Network Models for Exhaustive Analysis of Long Double-Stranded DNA Dynamics with Sequence Geometry Dependence.

Similar Datasets

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