title: Protein secondary structure: category assignment and predictability.
abstract: In the last decade, the prediction of protein secondary structure has been optimized using essentially one and the same assignment scheme known as DSSP. We present here a different scheme, which is more predictable. This scheme predicts directly the hydrogen bonds, which stabilize the secondary structures. Single sequence prediction of the new three category assignment gives an overall prediction improvement of 3.1% and 5.1% compared to the DSSP assignment and schemes where the helix category consists of alpha-helix and 3(10)-helix, respectively. These results were achieved using a standard feed-forward neural network with one hidden layer on a data set identical to the one used in earlier work.
authors.affiliation: Center for Biological Sequence Analysis, BioCentrum, Technical University of Denmark, Denmark.
authors.forename: C A
authors.initials: CA
authors.lastname: Andersen
authors.forename: H
authors.initials: H
authors.lastname: Bohr
authors.forename: S
authors.initials: S
authors.lastname: Brunak
mesh_terms: D000465:Algorithms; D006860:Hydrogen Bonding; D008956:Models, Chemical; D008958:Models, Molecular; D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins