3D‐hNCAnH for Simultaneous Backbone Resonance Assignment and One and Two Bond N–Cα Coupling Constants for Secondary Structural Prediction of Proteins
A new protocol based on the 3D-hNCAnH experiment for rapid, unambiguous backbone resonance assignment and estimation of structural information by intensity quantification of the peaks is described here. Along F<sub>1-</sub>F<sub>3</sub> plane at F<sub>2</sub>(<sup>13</sup>Cα<sub>i</sub>) of the 3D-hNCAnH spectrum, each i to i + 1 sequential connectivity (i.e., H<sub>i</sub>N<sub>i</sub> → H<sub>i + 1</sub>N<sub>i + 1</sub>) is confirmed by two inter-residue sequential correlation peaks: H<sub>i</sub>N<sub>i + 1</sub> and H<sub>i + 1</sub>N<sub>i</sub>. This allows unambiguous and direct identification of sequential correlations in HSQC peaks, without the need for extensive searching in different planes of 3D spectra. Further, a protocol utilizing the ratio of the intensities of the diagonal and cross peaks along F<sub>3</sub>(<sup>1</sup>H) dimension centered at self F<sub>2</sub>(<sup>13</sup>Cα<sub>i</sub>) and sequential F<sub>2</sub>(<sup>13</sup>Cα<sub>i-1</sub>) chemical shifts taken from F<sub>2</sub>-F<sub>3</sub> plane at F<sub>1</sub>(<sup>15</sup>N<sub>i</sub>) of the 3D-hNCAnH spectrum for estimating one and two bond N-Cα J-coupling constants, respectively, is described. The reliability of this approach is demonstrated using doubly labelled ubiquitin protein, wherein the coupling constants that are measured by the method described here are compared with previously measured values (BMRB Nos.: 15907 and 16582). The application of the approach to other proteins is demonstrated using doubly labelled human SUMO and Ca<sup>2+</sup> bound M-crystallin proteins.
Authors
- Jithender G. Reddy (ORCID: https://orcid.org/0000-0003-3032-3831)
- Susmitha Ambadipudi (ORCID: https://orcid.org/0000-0002-6232-4233)
Institutions
- National Centre for Cell Science (IN)
- Indian Institute of Chemical Technology (IN)
- Savitribai Phule Pune University (IN)
- Academy of Scientific and Innovative Research (IN)
Publication Details
- Journal
- Magnetic Resonance in Chemistry
- Published
- 2025-09-13
- DOI
- https://doi.org/10.1002/mrc.70040
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 0.00