Dataset Information

Computational model for predicting experimental RNA and DNA nearest-neighbor free energy rankings.

ABSTRACT: Hydrogen-bonding, intrastrand base-stacking, and interstrand base-stacking energies were calculated for RNA and DNA dimers at the MP2(full)/6-311G** level of theory. Standard A-form RNA and B-form DNA geometries from average fiber diffraction data were employed for all base monomer and dimer geometries, and all dimer binding energies were obtained via single-point calculations. The effects of water solvation were considered using the PCM model. The resulting dimer binding energies were used to calculate the 10 unique RNA and 10 unique DNA computational nearest-neighbor energies, and the ranking of these computational nearest neighbor energies are in excellent agreement with the ranking of the experimental nearest-neighbor free energies. These results dispel the notion that average fiber diffraction geometries are insufficient for calculating RNA and DNA stacking energies.

SUBMITTER: Johnson CA

PROVIDER: S-EPMC3142330 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Computational model for predicting experimental RNA and DNA nearest-neighbor free energy rankings.

Johnson Charles A CA Bloomingdale Richard J RJ Ponnusamy Vikram E VE Tillinghast Conor A CA Znosko Brent M BM Lewis Michael M

The journal of physical chemistry. B 20110630 29

Hydrogen-bonding, intrastrand base-stacking, and interstrand base-stacking energies were calculated for RNA and DNA dimers at the MP2(full)/6-311G** level of theory. Standard A-form RNA and B-form DNA geometries from average fiber diffraction data were employed for all base monomer and dimer geometries, and all dimer binding energies were obtained via single-point calculations. The effects of water solvation were considered using the PCM model. The resulting dimer binding energies were used to c ...[more]

PMID: 21619071

Similar Datasets

Project description:BackgroundThe enumeration of tetrameric and other sequence motifs that are positively or negatively correlated with in vivo antisense DNA effects has been a useful addition to the arsenal of information needed to predict effective targets for antisense DNA control of gene expression. Such retrospective information derived from in vivo cellular experiments characterizes aspects of the sequence dependence of antisense inhibition that are not predicted by nearest-neighbor (NN) thermodynamic parameters derived from in vitro experiments. However, quantitation of the antisense contributions of motifs is problematic, since individual motifs are not isolated from the effects of neighboring nucleotides, and motifs may be overlapping. These problems are circumvented by a next-nearest-neighbor (NNN) analysis of antisense DNA effects in which the overlapping nature of nearest-neighbors is taken into account.ResultsNext-nearest-neighbor triplet combinations of nucleotides are the simplest that include overlapping sequence effects and therefore can encompass interactions beyond those of nearest neighbors. We used singular value decomposition (SVD) to fit experimental data from our laboratory in which phosphorothioate-modified antisense DNAs (S-DNAs) 20 nucleotides long were used to inhibit cellular protein expression in 112 experiments involving four gene targets and two cell lines. Data were fitted using a NNN model, neglecting end effects, to derive NNN inhibition parameters that could be combined to give parameters for a set of 49 sequences that represents the inhibitory effects of all possible overlapping triplet interactions in the cellular targets of these antisense S-DNAs. We also show that parameters to describe subsets of the data, such as the mRNAs being targeted and the cell lines used, can be included in such a derivation. While NNN triplet parameters provided an adequate model to fit our data, NN doublet parameters did not.ConclusionsThe methodology presented illustrates how NNN antisense inhibitory information can be derived from in vivo cellular experiments. Subsequent calculations of the antisense inhibitory parameters for any mRNA target sequence automatically take into account the effects of all possible overlapping combinations of nearest-neighbors in the sequence. This procedure is more robust than the tallying of tetrameric motifs that have positive or negative antisense effects. The specific parameters derived in this work are limited in their applicability by the relatively small database of experiments that was used in their derivation.

Dataset Information

Computational model for predicting experimental RNA and DNA nearest-neighbor free energy rankings.

Publications

Computational model for predicting experimental RNA and DNA nearest-neighbor free energy rankings.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure