Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites.
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.
Nielsen
H
H
Department of Chemistry, Technical University of Denmark, Lyngby, Denmark.
Engelbrecht
J
J
Brunak
S
S
von Heijne
G
G
D000465:Algorithms; D000595:Amino Acid Sequence / Q000502:physiology*; D066246:ErbB Receptors / Q000737:chemistry; D005057:Eukaryotic Cells / Q000737:chemistry*; D006801:Humans; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D011387:Prokaryotic Cells / Q000737:chemistry*; D011499:Protein Processing, Post-Translational / Q000502:physiology; D021382:Protein Sorting Signals / Q000737:chemistry*; D013329:Structure-Activity Relationship
D021382:Protein Sorting Signals; D066246:ErbB Receptors