title: Wrap-and-Pack: a new paradigm for beta structural motif recognition with application to recognizing beta trefoils.
abstract: A method is presented that uses beta-strand interactions at both the sequence and the atomic level, to predict beta-structural motifs of protein sequences. A program called Wrap-and- Pack implements this method and is shown to recognize beta-trefoils, an important class of globular beta-structures, in the Protein Data Bank with 92% specificity and 92.3% sensitivity in cross-validation. It is demonstrated that Wrap-and-Pack learns each of the ten known SCOP beta-trefoil families, when trained primarily on beta-structures that are not beta-trefoils, together with three-dimensional structures of known beta-trefoils from outside the family. Wrap-and-Pack also predicts many proteins of unknown structure to be beta-trefoils. 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: Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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
authors.forename: Lenore
authors.initials: L
authors.lastname: Cowen
mesh_terms: 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: D011506:Proteins