Abstract
We have developed a new method for the identification of signal peptides and their cleavage sites based on neural networks trained on separate sets of prokaryotic and eukaryotic sequence. The method performs significantly better than previous prediction schemes and can easily be applied on genome-wide data sets. Discrimination between cleaved signal peptides and uncleaved N-terminal signal-anchor sequences is also possible, though with lower precision. Predictions can be made on a publicly available WWW server.
Keywords
Cleavage (geology)Signal peptideIdentification (biology)Computational biologySIGNAL (programming language)GenomeArtificial neural networkComputer scienceBiologyArtificial intelligenceGeneticsPeptide sequenceGene
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Publication Info
- Year
- 1997
- Type
- article
- Volume
- 10
- Issue
- 1
- Pages
- 1-6
- Citations
- 5370
- Access
- Closed
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Cite This
Henrik Nielsen,
Jacob Engelbrecht,
Søren Brunak
et al.
(1997).
Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites.
Protein Engineering Design and Selection
, 10
(1)
, 1-6.
https://doi.org/10.1093/protein/10.1.1
Identifiers
- DOI
- 10.1093/protein/10.1.1