Protein secondary structure and homology by neural networks The α‐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: 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.