Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides.
We have developed an automated method for predicting signal peptide sequences and their cleavage sites in eukaryotic and bacterial protein sequences. It is a 2-layer predictor: the 1st-layer prediction engine is to identify a query protein as secretory or non-secretory; if it is secretory, the process will be automatically continued with the 2nd-layer prediction engine to further identify the cleavage site of its signal peptide. The new predictor is called Signal-CF, where C stands for "coupling" and F for "fusion", meaning that Signal-CF is formed by incorporating the subsite coupling effects along a protein sequence and by fusing the results derived from many width-different scaled windows through a voting system. Signal-CF is featured by high success prediction rates with short computational time, and hence is particularly useful for the analysis of large-scale datasets. Signal-CF is freely available as a web-server at http://chou.med.harvard.edu/bioinf/Signal-CF/ or http://202.120.37.186/bioinf/Signal-CF/.
Chou
Kuo-Chen
KC
Gordon Life Science Institute, 13784 Torrey Del Mar Drive, San Diego, CA 92130, USA. kchou@san.rr.com
Shen
Hong-Bin
HB
D000465:Algorithms; D000595:Amino Acid Sequence; D000818:Animals; D001426:Bacterial Proteins / Q000032:analysis / Q000235:genetics / Q000502:physiology; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D005057:Eukaryotic Cells / Q000378:metabolism; D006801:Humans; D008565:Membrane Proteins / Q000378:metabolism; D008954:Models, Biological; D021382:Protein Sorting Signals; D011506:Proteins / Q000032:analysis* / Q000235:genetics / Q000502:physiology; D012697:Serine Endopeptidases / Q000378:metabolism
D001426:Bacterial Proteins; D008565:Membrane Proteins; D021382:Protein Sorting Signals; D011506:Proteins; D012697:Serine Endopeptidases; C026515:type I signal peptidase