Theory and application of the maximum likelihood principle to NMR parameter estimation of multidimensional NMR data

Summary

A general theory has been developed for the application of the maximum likelihood (ML) principle to the estimation of NMR parameters (frequency and amplitudes) from multidimensional time-domain NMR data. A computer program (ChiFit) has been written that carries out ML parameter estimation in the D-1 indirectly detected dimensions of a D-dimensional NMR data set. The performance of this algorithm has been tested with experimental three-dimensional (HNCO) and four-dimensional (HN(CO)-CAHA) data from a small protein labeled with ¹³C and ¹⁵N. These data sets, with different levels of digital resolution, were processed using ChiFit for ML analysis and employing conventional Fourier transform methods with prior extrapolation of the time-domain dimensions by linear prediction. Comparison of the results indicates that the ML approach provides superior frequency resolution compared to conventional methods, particularly under conditions of limited digital resolution in the time-domain input data, as is characteristic of D-dimensional NMR data of biomolecules. Close correspondence is demonstrated between the results of analyzing multidimensional time-domain NMR data by Fourier transformation, Bayesian probability theory [Chylla, R.A. and Markley, J.L. (1993) J. Biomol. NMR, 3, 515–533], and the ML principle.