NMR biomolecular studies

Multidimensional Nuclear Magnetic Resonance (NMR) studies have been recently emerged to better understand protein internal dynamics and determine protein structures. In these studies, a major time-consuming step is the generation of accurate NMR spectral peak lists, which usually requires significant amount of manual interaction. Human intervention becomes tedious and error prone at higher dimensions. Accurate peak lists are difficult to extract because of many ambiguities in low dimensional NMR spectra and because of a large number of low signal-to-noise ratio spectral peaks in high dimensional NMR spectra. Therefore, accurate spectral parameters estimation and adaptivity to contrasts between the sensitivity and resolution of multidimensional NMR spectrum require rigorous and computationally efficient analysis in a way which incorporates knowledge of the underlying physics. Moreover, multidimensional NMR experiments in protein structure and dynamics studies vary in complexity and specifications. The focus of my research in this field is to develop novel statistical procedures for analysis of multi-dimensional NMR spectrum, which are computational efficient, fully automated and apply to most of NMR experimental specifications.

This web page has been initiated with the National Science Foundation support (DMS-1105191).

Papers:
1. Serban, N. (2007), "MICE: Multiple-peak Identification, Estimation, and Characterization", Biometrics, 63,531-539.[.pdf ]

2. Serban, N. (2010), "Noise Reduction for Enhanced Component Identification in Multi-Dimensional Biomolecular NMR Studies", Computational Statistics and Data Analysis, 54 (4), pp 1051-1065.[.pdf ]

3. Serban, N., Li, P. (2014) "A statistical test for Mixture Detection in A Multi-dimensional Regression Model", Canadian Journal of Statistics, 42(1), 36-60.[.pdf ]

4. Hilton, R., Serban, N. (2014) "Theoretical Limits of Component Identification in a Separable Nonlinear Least Squares Problem", Journal of Nonparametric Statistics, 26(4), 769-791.
[Paper Document] [Software]


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