Hyperdimensional NMR spectroscopy with nonlinear sampling.

An approach is described for joint interleaved recording, real-time processing, and analysis of NMR data sets. The method employs multidimensional decomposition to find common information in a set of conventional triple-resonance spectra recorded in the nonlinear sampling mode, and builds a model of hyperdimensional (HD) spectrum. While preserving sensitivity per unit of measurement time and allowing for maximal spectral resolution, the approach reduces data collection time on average by 2 orders of magnitude compared to the conventional method. The 7-10 dimensional HD spectrum, which is represented as a set of deconvoluted 1D vectors, is easy to handle and amenable for automated analysis. The method is exemplified by automated assignment for two protein systems of low and high spectral complexity: ubiquitin (globular, 8 kDa) and zetacyt (naturally disordered, 13 kDa). The collection and backbone assignment of the data sets are achieved in real time after approximately 1 and 10 h, respectively. The approach removes the most critical time bottlenecks in data acquisition and analysis. Thus, it can significantly increase the value of NMR spectroscopy in structural biology, for example, in high-throughput structural genomics applications.

Removal of a time barrier for high-resolution multidimensional NMR spectroscopy.

We introduce the recursive multidimensional decomposition (R-MDD) method to speed recording of high-resolution NMR spectra. The measurement time is logarithmically dependent on the sizes of indirect spectral dimensions. R-MDD has the sensitivity and resolution advantages of optimized nonuniform acquisition schemes and is applicable to all types of biomolecular spectra. We demonstrated it for triple resonance experiments on three globular proteins (ubiquitin, azurin and the barstar-barnase complex) of 8-22 kDa.