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.
authors.affiliation: NEC Laboratories America, Inc. 4 Independence Way, Princeton, NJ 08544, USA. kruus@nec-labs.com
authors.forename: Erik
authors.initials: E
authors.lastname: Kruus
authors.forename: Peter
authors.initials: P
authors.lastname: Thumfort
authors.forename: Chao
authors.initials: C
authors.lastname: Tang
authors.forename: Ned S
authors.initials: NS
authors.lastname: Wingreen
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D030562:Databases, Protein; D008958:Models, Molecular; D017433:Protein Structure, Secondary; D020539:Sequence Analysis, Protein / Q000379:methods*