title: Protein secondary structure and homology by neural networks. The alpha-helices in rhodopsin.
abstract: Neural networks provide a basis for semiempirical studies of pattern matching between the primary and secondary structures of proteins. Networks of the perceptron class have been trained to classify the amino-acid residues into two categories for each of three types of secondary feature: alpha-helix or not, beta-sheet or not, and random coil or not. The explicit prediction for the helices in rhodopsin is compared with both electron microscopy results and those of the Chou-Fasman method. A new measure of homology between proteins is provided by the network approach, which thereby leads to quantification of the differences between the primary structures of proteins.
authors.affiliation: Technical University, Lyngby, Denmark.
authors.forename: H
authors.initials: H
authors.lastname: Bohr
authors.forename: J
authors.initials: J
authors.lastname: Bohr
authors.forename: S
authors.initials: S
authors.lastname: Brunak
authors.forename: R M
authors.initials: RM
authors.lastname: Cotterill
authors.forename: B
authors.initials: B
authors.lastname: Lautrup
authors.forename: L
authors.initials: L
authors.lastname: Nørskov
authors.forename: O H
authors.initials: OH
authors.lastname: Olsen
authors.forename: S B
authors.initials: SB
authors.lastname: Petersen
mesh_terms: D000595:Amino Acid Sequence; D008959:Models, Neurological; D008962:Models, Theoretical; D008969:Molecular Sequence Data; D011487:Protein Conformation; D012168:Retinal Pigments; D012243:Rhodopsin
chemical_list: D012168:Retinal Pigments; D012243:Rhodopsin