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Protein secondary structure and homology by neural networks The α‐helices in rhodopsin
Author(s) -
Bohr Henrik,
Bohr Jakob,
Brunak Søren,
Cotterill Rodney M.J.,
Lautrup Benny,
Nørskov Leif,
Olsen Ole H.,
Petersen Steffen B.
Publication year - 1988
Publication title -
febs letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.593
H-Index - 257
eISSN - 1873-3468
pISSN - 0014-5793
DOI - 10.1016/0014-5793(88)81066-4
Subject(s) - rhodopsin , protein secondary structure , homology (biology) , protein primary structure , alpha helix , beta sheet , random coil , artificial neural network , protein structure , chemistry , threading (protein sequence) , computational biology , peptide sequence , artificial intelligence , amino acid , pattern recognition (psychology) , biophysics , biology , computer science , biochemistry , gene , retinal
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: α‐helix or not, β‐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.