Dataset Information

Nuclear Magnetic Resonance-Assisted Prediction of Secondary Structure for RNA: Incorporation of Direction-Dependent Chemical Shift Constraints.

ABSTRACT: Knowledge of RNA structure is necessary to determine structure-function relationships and to facilitate design of potential therapeutics. RNA secondary structure prediction can be improved by applying constraints from nuclear magnetic resonance (NMR) experiments to a dynamic programming algorithm. Imino proton walks from NOESY spectra reveal double-stranded regions. Chemical shifts of protons in GH1, UH3, and UH5 of GU pairs, UH3, UH5, and AH2 of AU pairs, and GH1 of GC pairs were analyzed to identify constraints for the 5' to 3' directionality of base pairs in helices. The 5' to 3' directionality constraints were incorporated into an NMR-assisted prediction of secondary structure (NAPSS-CS) program. When it was tested on 18 structures, including nine pseudoknots, the sensitivity and positive predictive value were improved relative to those of three unrestrained programs. The prediction accuracy for the pseudoknots improved the most. The program also facilitates assignment of chemical shifts to individual nucleotides, a necessary step for determining three-dimensional structure.

SUBMITTER: Chen JL

PROVIDER: S-EPMC4666457 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Publications

Nuclear Magnetic Resonance-Assisted Prediction of Secondary Structure for RNA: Incorporation of Direction-Dependent Chemical Shift Constraints.

Chen Jonathan L JL Bellaousov Stanislav S Tubbs Jason D JD Kennedy Scott D SD Lopez Michael J MJ Mathews David H DH Turner Douglas H DH

Biochemistry 20151103 45

Knowledge of RNA structure is necessary to determine structure-function relationships and to facilitate design of potential therapeutics. RNA secondary structure prediction can be improved by applying constraints from nuclear magnetic resonance (NMR) experiments to a dynamic programming algorithm. Imino proton walks from NOESY spectra reveal double-stranded regions. Chemical shifts of protons in GH1, UH3, and UH5 of GU pairs, UH3, UH5, and AH2 of AU pairs, and GH1 of GC pairs were analyzed to id ...[more]

PMID: 26451676

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Project description:PurposeTo explore, at a high field strength of 7T, the performance of various fat spectral models on the quantification of triglyceride composition and proton density fat fraction (PDFF) using chemical-shift encoded MRI (CSE-MRI).MethodsMR data was acquired from CSE-MRI experiments for various fatty materials, including oil and butter samples and in vivo brown and white adipose mouse tissues. Triglyceride composition and PDFF were estimated using various a priori 6- or 9-peak fat spectral models. To serve as references, NMR spectroscopy experiments were conducted to obtain material specific fat spectral models and triglyceride composition estimates for the same fatty materials. Results obtained using the spectroscopy derived material specific models were compared to results obtained using various published fat spectral models.ResultsUsing a 6-peak fat spectral model to quantify triglyceride composition may lead to large biases at high field strengths. When using a 9-peak model, triglyceride composition estimations vary greatly depending on the relative amplitudes of the chosen a priori spectral model, while PDFF estimations show small variations across spectral models. Material specific spectroscopy derived spectral models produce estimations that better correlate with NMR spectroscopy estimations in comparison to those obtained using non-material specific models.ConclusionAt a high field strength of 7T, a material specific 9-peak fat spectral model, opposed to a widely accepted or generic human liver model, is necessary to accurately quantify triglyceride composition when using CSE-MRI estimation methods that assume the spectral model to be known as a priori information. CSE-MRI allows for the quantification of the spatial distribution of triglyceride composition for certain in vivo applications. Additionally, PDFF quantification is shown to be independent of the chosen a priori spectral model, which agrees with previously reported results obtained at lower field strengths (e.g. 3T).

Dataset Information

Nuclear Magnetic Resonance-Assisted Prediction of Secondary Structure for RNA: Incorporation of Direction-Dependent Chemical Shift Constraints.

Publications

Nuclear Magnetic Resonance-Assisted Prediction of Secondary Structure for RNA: Incorporation of Direction-Dependent Chemical Shift Constraints.

Similar Datasets

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