Extracting structural information using time-frequency analysis of protein NMR data

ABSTRACT

High-throughput, data-directed computational protocols for Structural Genomics (or Proteomics) are required in order to evaluate the protein products of genes for structure and function at rates comparable to current gene-sequencing technology. To develop such methods, new algorithms are required that can quickly extract significantly more structural information from sparse experimental data. This paper presents a new class of signal processing algorithms for nuclear magnetic resonance (NMR) structural biology, based on time-frequency analysis of chemical shift dynamics.

A novel approach to multidimensional NMR analysis is proposed in which the data are interpreted in the time-frequency domain, as opposed to the traditional frequency domain. Time-frequency analysis (TFA) exposes behavior orthogonal to the magnetic coherence transfer pathways, thus affording new avenues of NMR discovery. An implementation yielding new biophysical results is discussed. In particular, we demonstrate the heretofore unknown presence of through-space inter-atomic distance information within ¹⁵N-edited heteronuclear single-quantum coherence(¹⁵N HSQC) data. A biophysical model explains these results, and is supported by further experiments on simulated spectra.

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INDEX TERMS

Primary Classification:
  J. Computer Applications
  J.3 LIFE AND MEDICAL SCIENCES
      Subjects: Biology and genetics

Additional Classification:
  G. Mathematics of Computing

  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.4 Applications
          Subjects: Signal processing

General Terms:
Algorithms, Design, Experimentation, Human Factors, Measurement, Performance, Theory