Flow-resolution Enhancement in Electrophoretic NMR Using De-noising and Linear Prediction

Sunitha B. Thakur; Yinpeng Jin; Haihang Sun; Andrew F. Laine; Qiuhong He

Flow-resolution Enhancement in Electrophoretic NMR Using De-noising and Linear Prediction
Thakur, Sunitha B.
Jin, Yinpeng
Sun, Haihang
Laine, Andrew F.
He, Qiuhong
Biomedical Engineering
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Presented at the 43rd Experimental Nuclear Magnetic Resonance Conference, Asilomar Conference Center, Pacific Grove, Calif., April 14-19, 2002.
Detection of electrophoretic motion of ionic species using multi-dimensional Electrophoretic NMR (nD-ENMR) has demonstrated the potential to distinguish signals from two molecules in a solution mixture without their physical separation. Therefore, this technique may be applied for simultaneous structure determination of proteins and protein conformations, even during their biochemical interactions. Indeed, this has been achieved by introducing an additional dimension of electrophoretic mobility to the conventional multi-dimensional NMR by applying an external DC electric field. Consequently, the protein spectra are differently modulated by their electrophoretic mobilities in the electrophoretic flow dimension. Unfortunately, spectral resolution in the flow dimension has been limited by severe signal truncations due to the limited DC electric field available before onset of heating-induced convection. Linear prediction, which have been widely used for high-resolution spectral estimation from finite Fourier samples, have already been proposed to extend the truncated ENMR flow oscillation curves. However, we found that the spectral quality of linear prediction deteriorates as the spectral S/N decreases. To alleviate this problem, we have denoised the ENMR data using low pass filters prior to linear prediction. This technique has lead to improved resolution in the electrophoretic flow dimension. The approach was applied to analyze a 2D ENMR data matrix obtained from a mixture solution of two proteins ubiquitin and bovine serum albumin (BSA) in D2O.
Biomedical engineering
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Suggested Citation:
Sunitha B. Thakur, Yinpeng Jin, Haihang Sun, Andrew F. Laine, Qiuhong He, , Flow-resolution Enhancement in Electrophoretic NMR Using De-noising and Linear Prediction, Columbia University Academic Commons, .

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