A Dataset of Simple 1-D and 2-D NMR Spectra of Peptides, Including all Encoded Amino Acids, for Introductory Instruction in Protein Biomolecular NMR Spectroscopy

NMR spectroscopy is the most important technique for understanding the structure of peptides and proteins in solution, providing information at the single-residue and single-atom level. However, written instruction in the interpretation of NMR spectra of peptides and proteins is generally focused on advanced techniques and highly complex spectra, with a lack of simple spectra and guides available for beginning students. In order to address this instructional limitation, we have generated a dataset of 1H NMR spectra of a series of simple peptides that include all canonical amino acids. Peptides examined include Ac-X(S/pS)-NH2, Ac-X(T/pT)-NH2, and Ac-XPPGY-NH2, where X = all encoded amino acids, pS = phosphorylated Ser, and pT = phosphorylated Thr. The characterization of each peptide includes a 1-D spectrum and a TOCSY spectrum, with both the raw and processed data available. The spectra can be used for instructional applications, including analysis of regions of the spectra (e.g. amide HN, aromatic, H, and aliphatic regions); identification of spin systems and residue assignment via TOCSY spectra; analysis of conformational features including amide HN chemical shift dispersion and changes due to hydrogen bonding or post-translational modifications; the 3JN coupling constant that reports on the torsion angle and on order versus disorder at a given residue; conformational preferences at H via chemical shift index analysis; understanding of diastereotopic hydrogens; dynamic processes, including hydrogen exchange; and identification of proline cis-trans isomerism. In addition, for a limited number of peptides, NOESY spectra are included to allow sequential resonance assignment and for assignment of trans versus cis proline conformations. Spectra from closely related peptides allow the analysis of the relative effects of single amino acid changes. The paper is written to be directly accessible to students as a tutorial guide. In addition, the data can be used by instructors for problem sets and exams.