Parameter Estimation of Multidimensional NMR Signals based on High-Resolution Subband Analysis of 2D NMR Projections

• NMR spectroscopy is a powerful technique used in protein research for comprehensive functional characterizations, e.g. structure determination at atomic resolution. Due to the molecular size (typically>1000 atoms), proteinNMR spectra contain a large number of signal frequencies. Resolving these requires high-dimensional spectroscopy. However, when the number of frequency exceeds three, conventional approaches start to demand unrealistic long experiment time, and the data analysis becomes challenging. In this paper we explore a combination of novel methods: Data from 5D NMR experiments are recorded as a series of 2D projections, which are then subjected to 2D subband filters and 2D LS-ESPRIT for estimation of signal parameters. Based on the relations established between 5D NMR signals and their 2D counterparts, projection spectroscopy allows to extract highly similar information as what would be found in conventional 5D NMR experiments; however, while the latter would require months of experiment time, the recording of all necessary projections can be accomplished within 1-2 days. Preliminary results show the efficiency of the method with respect to accuracy and resolution of the parameter estimates as compared with conventional methods.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Kemiteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Chemical Engineering (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)

NATURVETENSKAP  -- Biologi -- Biokemi och molekylärbiologi (hsv//swe)

NATURAL SCIENCES  -- Biological Sciences -- Biochemistry and Molecular Biology (hsv//eng)

Keyword

multidimensional NMR

projection spectroscopy

2D ESPRIT

2D subband

frequency estimation

protein structures.

multidimensional NMR

Publication and Content Type

ref (subject category)

kon (subject category)