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Effect of Linear Prediction on Distance Constraints Obtained from Quantitative Evaluation of NOESY Data in Conjunction with Complete Relaxation Matrix Analysis
Author(s) -
Babcook David M.,
Sahasrabudhe Parag V.,
Gmeiner William H.
Publication year - 1996
Publication title -
magnetic resonance in chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.483
H-Index - 72
eISSN - 1097-458X
pISSN - 0749-1581
DOI - 10.1002/(sici)1097-458x(199611)34:11<851::aid-omr988>3.0.co;2-6
Subject(s) - two dimensional nuclear magnetic resonance spectroscopy , chemistry , relaxation (psychology) , matrix (chemical analysis) , data set , fourier transform , singular value decomposition , data matrix , mathematics , mathematical analysis , algorithm , statistics , stereochemistry , chromatography , clade , biochemistry , gene , phylogenetic tree , psychology , social psychology
The effects of extrapolating 2D NOESY data in the t 1 dimension using linear prediction (LP) based on singular value decomposition were examined to determine if data extended with this processing method accurately reproduce quantitative distance constraints obtained from complete relaxation matrix analysis based on NOESY data acquired with more t 1 data points, but processed without linear prediction. NOESY data for the self‐complementary decamer 5′ dGCGAAUUCGC were collected with either 256, 512 or 800 points experimentally acquired in t 1 . The data were extended to 2048 points either by zero‐filling or by using forward LP using all t 1 data points. The data were apodized, Fourier transformed and baseline corrected and the resultant 2D NMR spectra were qualitatively and quantitatively evaluated. Eighteen cross peaks were numerically integrated for each set of experimental conditions and the relative volumes for each of these cross peaks were used to estimate interproton distances and upper and lower distance constraints via relaxation matrix analysis using the program MARDIGRAS. The reproducibility of volumes for a cross peak from a given set of experimental conditions was also evaluated by quantitatively assessing the same 18 cross peaks from NOESY data collected in quadruplicate with 512 points in t 1 . The relative volumes of NOESY cross peaks do not systematically vary depending on the number of points acquired in t 1 or whether the data are extended with zero‐filling or linear prediction based on singular value decomposition prior to Fourier transformation. The estimated interproton distance, and upper and lower distance bounds, obtained from a relaxation matrix analysis based on the NOESY data are also insensitive to the use of LP to extend relatively smaller data sets. The results indicate that LP can be used to reduce the acquisition time of 2D NOESY data sets by a factor of two to three without misrepresenting the cross peak volumes used in the determination of the three‐dimensional structures of duplex DNA.