[nmrlearner]

Identification of helix capping and beta-turn motifs from NMR chemical shifts


Abstract We present an empirical method for identification of distinct structural motifs in proteins on the basis of experimentally determined backbone and 13Cβ chemical shifts. Elements identified include the N-terminal and C-terminal helix capping motifs and five types of beta-turns: I, II, I', II' and VIII. Using a database of proteins of known structure, the NMR chemical shifts, together with the PDB-extracted amino acid preference of the helix capping and β-turn motifs are used as input data for training an artificial neural network algorithm, which outputs the statistical probability of finding each motif at any given position in the protein. The trained neural networks, contained in the MICS (motif identification from chemical shifts) program, also provide a confidence level for each of their predictions, and values ranging from ca 0.7â??0.9 for the Matthews correlation coefficient of its predictions far exceed those attainable by sequence analysis. MICS is anticipated to be useful both in the conventional NMR structure determination process and for enhancing on-going efforts to determine protein structures solely on the basis of chemical shift information, where it can aid in identifying protein database fragments suitable for use in building such structures.

• Content Type Journal Article
• Category Article
• Pages 1-22
• DOI 10.1007/s10858-012-9602-0
• Authors
  • Yang Shen, Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Building 5, Room 126, NIH, Bethesda, MD 20892-0520, USA
  • Ad Bax, Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Building 5, Room 126, NIH, Bethesda, MD 20892-0520, USA

• • Journal Journal of Biomolecular NMR
  • Online ISSN 1573-5001
  • Print ISSN 0925-2738

Source: Journal of Biomolecular NMR