High-Resolution Iterative Frequency Identification for NMR as a General Strategy for Multidimensional Data Collection

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Abstract
We describe a novel approach to the rapid collection and processing of multidimensional NMR data: “high-resolution iterative frequency identification for NMR” (HIFI−NMR). As with other reduced dimensionality approaches, HIFI−NMR collects n-dimensional data as a set of two-dimensional (2D) planes. The HIFI−NMR algorithm incorporates several innovative features. (1) Following the initial collection of two orthogonal 2D planes, tilted planes are selected adaptively, one-by-one. (2) Spectral space is analyzed in a rigorous statistical manner. (3) An online algorithm maintains a model that provides a probabilistic representation of the three-dimensional (3D) peak positions, derives the optimal angle for the next plane to be collected, and stops data collection when the addition of another plane would not improve the data model. (4) A robust statistical algorithm extracts information from the plane projections and is used to drive data collection. (5) Peak lists with associated probabilities are generated directly, without total reconstruction of the 3D spectrum; these are ready for use in subsequent assignment or structure determination steps. As a proof of principle, we have tested the approach with 3D triple-resonance experiments of the kind used to assign protein backbone and side-chain resonances. Peaks extracted automatically by HIFI−NMR, for both small and larger proteins, included ∼98% of real peaks obtained from control experiments in which data were collected by conventional 3D methods. HIFI−NMR required about one-tenth the time for data collection and avoided subsequent data processing and peak-picking. The approach can be implemented on commercial NMR spectrometers and is extensible to higher-dimensional NMR.