Protein secondary structure and homology by neural networks. The alpha-helices in rhodopsin.
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.
Bohr
H
H
Technical University, Lyngby, Denmark.
Bohr
J
J
Brunak
S
S
Cotterill
R M
RM
Lautrup
B
B
Nørskov
L
L
Olsen
O H
OH
Petersen
S B
SB
D000595:Amino Acid Sequence; D008959:Models, Neurological; D008962:Models, Theoretical; D008969:Molecular Sequence Data; D011487:Protein Conformation; D012168:Retinal Pigments; D012243:Rhodopsin
D012168:Retinal Pigments; D012243:Rhodopsin