Improving signal peptide prediction accuracy by simulated neural network.
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.
Ladunga
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Department of Genetics, Eötvös University, Budapest, Hungary.
Czakó
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F
Csabai
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I
Geszti
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T
D000595:Amino Acid Sequence; D016208:Databases, Factual; D004798:Enzymes / Q000235:genetics; D016571:Neural Networks, Computer; D021382:Protein Sorting Signals / Q000235:genetics*; D011506:Proteins / Q000235:genetics*
D004798:Enzymes; D021382:Protein Sorting Signals; D011506:Proteins