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

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 timefrequency 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 ...

• Type: Conference
• Year: 2001
• Where: RECOMB
• Authors: Christopher James Langmead, Bruce Randall Donald
• Keywords: Data-directed Computational Protocols, Multidimensional Nmr Analysis, Nuclear Magnetic Resonance, RECOMB 2001