title: A novel approach to prediction of the 3-dimensional structures of protein backbones by neural networks.
abstract: Three-dimensional structures of protein backbones have been predicted using neural networks. A feed forward neural network was trained on a class of functionally, but not structurally, homologous proteins, using backpropagation learning. The network generated tertiary structure information in the form of binary distance constraints for the C(alpha) atoms in the protein backbone. The binary distance between two C(alpha) atoms was 0 if the distance between them was less than a certain threshold distance, and 1 otherwise. The distance constraints predicted by the trained neural network were utilized to generate a folded conformation of the protein backbone, using a steepest descent minimization approach.
authors.affiliation: Risø National Laboratory, Roskilde.
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: H
authors.initials: H
authors.lastname: Fredholm
authors.forename: B
authors.initials: B
authors.lastname: Lautrup
authors.forename: S B
authors.initials: SB
authors.lastname: Petersen
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D008958:Models, Molecular; D016571:Neural Networks, Computer; D011487:Protein Conformation; D051381:Rats; D020539:Sequence Analysis, Protein; D014357:Trypsin / Q000737:chemistry / Q000648:ultrastructure
chemical_list: D014357:Trypsin