Identification of helix capping and β-turn motifs from NMR chemical shifts
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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 β-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 0.7–0.9 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.
KeywordsArtificial neural network Backbone chemical shift Helix capping β-turn CS-Rosetta MCC score Protein structure prediction Rosetta Secondary structure prediction
We thank Frank Delaglio for helpful comments and suggestions. This work was funded by the Intramural Research Program of the NIDDK and by the Intramural AIDS-Targeted Antiviral Program of the Office of the Director, NIH.
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