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. 1989 Jan;86(1):152–156. doi: 10.1073/pnas.86.1.152

Protein secondary structure prediction with a neural network.

L H Holley 1, M Karplus 1
PMCID: PMC286422  PMID: 2911565

Abstract

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%.

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Selected References

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