title: Predicting the beta-helix fold from protein sequence data.
abstract: A method is presented that uses beta-strand interactions to predict the parallel right-handed beta-helix super-secondary structural motif in protein sequences. A program called BetaWrap implements this method and is shown to score known beta-helices above non-beta-helices in the Protein Data Bank in cross-validation. It is demonstrated that BetaWrap learns each of the seven known SCOP beta-helix families, when trained primarily on beta-structures that are not beta-helices, together with structural features of known beta-helices from outside the family. BetaWrap also predicts many bacterial proteins of unknown structure to be beta-helices; in particular, these proteins serve as virulence factors, adhesins, and toxins in bacterial pathogenesis and include cell surface proteins from Chlamydia and the intestinal bacterium Helicobacter pylori. The computational method used here may generalize to other beta-structures for which strand topology and profiles of residue accessibility are well conserved.
authors.affiliation: Department of EECS, Tufts University, Medford, MA 02155, USA.
authors.forename: Lenore
authors.initials: L
authors.lastname: Cowen
authors.forename: Phil
authors.initials: P
authors.lastname: Bradley
authors.forename: Matthew
authors.initials: M
authors.lastname: Menke
authors.forename: Jonathan
authors.initials: J
authors.lastname: King
authors.forename: Bonnie
authors.initials: B
authors.lastname: Berger
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001426:Bacterial Proteins / Q000737:chemistry / Q000235:genetics; D019295:Computational Biology; D030562:Databases, Protein; D008958:Models, Molecular; D017510:Protein Folding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry* / Q000235:genetics*; D016415:Sequence Alignment / Q000706:statistics & numerical data; D012984:Software
chemical_list: D001426:Bacterial Proteins; D011506:Proteins