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© IRL Press

Improving signal peptide prediction accuracy by simulated neural network

I. Ladunga 1, F. Czakó 2, I. Csabai 2 and T. Geszti 2

1Departments of Genetics, Eötvös University H-1088 Budapest, Muzeum krt. 4/a, Hungary
2Atomic Physics, Eötvös University H-1088 Budapest, Muzeum krt. 4/a, Hungary

The accuracy of distinguishing amino-terminal signal peptides from cytosolic proteins has been improved to 95% by combining a neural network classifier with von Heijne's statistical prediction, the latter is itself 85–90% reliable. The network processed not the cleavage site, but amino-terminal 20-residue segments by the ‘tiling’ algorithm. Concordant positive predictions of both methods led to the safe identification of 496 novel signal peptides from the Protein Identification Resources.

Received on December 6, 1990; accepted on February 28, 1991

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