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Updated: Jun 10, 2026

15N CPMG Relaxation Dispersion for the Investigation of Protein Conformational Dynamics on the µs-ms Timescale
Published on: April 19, 2021
hnCOcaNH and hncoCANH pulse sequences for rapid and unambiguous backbone assignment in (13C, 15N) labeled proteins
Dinesh Kumar1, Jithender G Reddy, Ramakrishna V Hosur
1Department of Chemical Sciences, Tata Institute of Fundamental Research (TIFR), 1-Homi Bhabha Road, Colaba, Mumbai 400 005, India.
Abstract:
Time-saving in data acquisition is a major thrust of NMR pulse sequence development in the context of structural proteomics research. The conventional HNCA and HN(CA)CO pulse sequences, routinely used for sequential backbone assignment, have the limitation that they cannot distinguish inter- and intra-residue correlations. In order to remove this ambiguity, one has to record HNCO and HN(CO)CA or sequential HNCA experiments which provide unambiguous information of sequential correlations. However, this almost doubles the experimental time. Besides, they require repeated scanning through the (15)N planes to search for the matching peaks along the carbon dimension. In this background, we present here two pulse sequences, termed as hncoCANH and hnCOcaNH that lead to spectra equivalent to HNCA and HN(CA)CO spectra, respectively, but with direct distinction of inter- and intra-residue peaks; these occur with opposite signs in the new experiments. The two pulse sequences have been derived by simple modification of the previously described HN(C)N pulse sequence [Panchal et al., J. Biomol. NMR 20 (2001) 135-147] to frequency-label (13)C(alpha) or (13)C' instead of (15)N during the t(1) period. Like HN(C)N, these spectra also exhibit special patterns of self and sequential peaks around glycines and prolines, which enable direct identification of certain triplets of residues and thus provide internal checks during the sequential assignment walk. The spectra enable rapid and unambiguous assignment of H(N), (15)N and (13)C(alpha) (or (13)C') in a single experiment, and thus would be of great value in high-throughput structural proteomics.
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