title: The DEF data base of sequence based protein fold class predictions.
abstract: A new method for predicting protein fold-classes and protein domains from sequence data is constructed and used for generating a data base of protein fold-class assignments. Any given sequence of amino acids is assigned a specific prediction of one out of 45 typical protein fold-classes, a prediction of one out of 4 super fold-classes for the content of secondary structures and a profile of fold-class predictions along the sequence. The prediction accuracy for the super fold-classes is around 91% correct and 82% correct for the specific fold-classes. This accuracy is maintained down to a few percent of sequence identity.
authors.affiliation: DKFZ-German Cancer Research Center, Heidelberg, Germany.
authors.forename: M
authors.initials: M
authors.lastname: Reczko
authors.forename: H
authors.initials: H
authors.lastname: Bohr
mesh_terms: D000595:Amino Acid Sequence; D016208:Databases, Factual; D017510:Protein Folding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D017421:Sequence Analysis; D012984:Software
chemical_list: D011506:Proteins