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

Authors:
Christopher James Langmead;Bruce Randall Donald
Affiliations:
Dartmouth Computer Science Department, Hanover, NH;Dartmouth Chemistry Department, Hanover, NH and Dartmouth Center for Structural Biology and Computational Chemistry, Hanover, NH and 6211 Sudikoff Laboratory, Dartmouth, Computer Science Departmen ...
Venue:
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Year:
2001

Citing 4
Cited 0

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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.