Protein secondary structure prediction with a neural network.
A method is presented for protein secondary structure prediction based on a neural network. A training phase was used to teach the network to recognize the relation between secondary structure and amino acid sequences on a sample set of 48 proteins of known structure. On a separate test set of 14 proteins of known structure, the method achieved a maximum overall predictive accuracy of 63% for three states: helix, sheet, and coil. A numerical measure of helix and sheet tendency for each residue was obtained from the calculations. When predictions were filtered to include only the strongest 31% of predictions, the predictive accuracy rose to 79%.
Holley
L H
LH
Department of Chemistry, Harvard University, Cambridge, MA 02138.
Karplus
M
M
D000596:Amino Acids; D008959:Models, Neurological; D008962:Models, Theoretical; D008969:Molecular Sequence Data; D011487:Protein Conformation; D011506:Proteins
D000596:Amino Acids; D011506:Proteins