Dataset Information

A Computational Model for Predicting Experimental RNA Nearest-Neighbor Free Energy Rankings: Inosine•Uridine Pairs.

ABSTRACT: A computational model for predicting RNA nearest neighbor free energy rankings has been expanded to include the nonstandard nucleotide inosine. The model uses average fiber diffraction data and molecular dynamic simulations to generate input geometries for Quantum mechanic calculations. This resulted in calculated intrastrand stacking, interstrand stacking, and hydrogen bonding energies that were combined to give total binding energies. Total binding energies for RNA dimer duplexes containing inosine were ranked and compared to experimentally determined free energy ranks for RNA duplexes containing inosine. Statistical analysis showed significant agreement between the computationally determined ranks and the experimentally determined ranks.

SUBMITTER: Jolley EA

PROVIDER: S-EPMC4621965 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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Publications

A Computational Model for Predicting Experimental RNA Nearest-Neighbor Free Energy Rankings: Inosine•Uridine Pairs.

Jolley Elizabeth A EA Lewis Michael M Znosko Brent M BM

Chemical physics letters 20151001

A computational model for predicting RNA nearest neighbor free energy rankings has been expanded to include the nonstandard nucleotide inosine. The model uses average fiber diffraction data and molecular dynamic simulations to generate input geometries for Quantum mechanic calculations. This resulted in calculated intrastrand stacking, interstrand stacking, and hydrogen bonding energies that were combined to give total binding energies. Total binding energies for RNA dimer duplexes containing in ...[more]

PMID: 26525429

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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

A Computational Model for Predicting Experimental RNA Nearest-Neighbor Free Energy Rankings: Inosine•Uridine Pairs.

Publications

A Computational Model for Predicting Experimental RNA Nearest-Neighbor Free Energy Rankings: Inosine•Uridine Pairs.

Similar Datasets

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