@article{90626,
  keywords = {Models, Molecular, algorithms, Protein Structure, Secondary, Databases, Protein, Amino Acid Motifs, Sequence Analysis, Protein},
  author = {Erik Kruus and Peter Thumfort and Chao Tang and Ned Wingreen},
  title = {Gibbs sampling and helix-cap motifs.},
  abstract = { Protein backbones have characteristic secondary structures, including alpha-helices and beta-sheets. Which structure is adopted locally is strongly biased by the local amino acid sequence of the protein. Accurate (probabilistic) mappings from sequence to structure are valuable for both secondary-structure prediction and protein design. For the case of alpha-helix caps, we test whether the information content of the sequence-structure mapping can be self-consistently improved by using a relaxed definition of the structure. We derive helix-cap sequence motifs using database helix assignments for proteins of known structure. These motifs are refined using Gibbs sampling in competition with a null motif. Then Gibbs sampling is repeated, allowing for frameshifts of +/-1 amino acid residue, in order to find sequence motifs of higher total information content. All helix-cap motifs were found to have good generalization capability, as judged by training on a small set of non-redundant proteins and testing on a larger set. For overall prediction purposes, frameshift motifs using all training examples yielded the best results. Frameshift motifs using a fraction of all training examples performed best in terms of true positives among top predictions. However, motifs without frameshifts also performed well, despite a roughly one-third lower total information content. },
  year = {2005},
  journal = {Nucleic Acids Res},
  volume = {33},
  pages = {5343-53},
  issn = {1362-4962},
  doi = {10.1093/nar/gki842},
  language = {eng},
}