DTU Health Tech
Department of Health Technology
This link is for the general contact of the DTU Health Tech institute.
If you need help with the bioinformatics programs, see the "Getting Help" section below the program.
The LipoP 1.0 server produces predictions of lipoproteins and discriminates between lipoprotein signal peptides, other signal peptides and n-terminal membrane helices in Gram-negative bacteria.
Note: Although LipoP 1.0 has been trained on sequences from Gram-negative bacteria only, the following paper reports that it has a good performance on sequences from Gram-positive bacteria also:
Methods for the bioinformatic identification of bacterial lipoproteins encoded in the genomes of Gram-positive bacteria
O. Rahman, S. P. Cummings, D. J. Harrington and I. C. Sutcliffe
World Journal of Microbiology and Biotechnology 24(11):2377-2382 (2008)
NOTE: LipoP is outdated and is only kept online for reference. Lipoprotein signal peptides are better predicted by the current version of SignalP! |
Restrictions
At most 5000 sequences and 500,000 amino acids per submission; each sequence not less than 70 and not more than 5,000 amino acids.
Confidentiality
The sequences are kept confidential and will be deleted after processing.
CITATIONS
For publication of results, please cite:
Prediction of lipoprotein signal peptides in Gram-negative bacteria.
A. S. Juncker, H. Willenbrock, G. von Heijne, H. Nielsen, S. Brunak
and A. Krogh.
Protein Sci. 12(8):1652-62, 2003
This is an example (one protein):
>5H2A_CRIGR you can have comments after the ID
MEILCEDNTSLSSIPNSLMQVDGDSGLYRNDFNSRDANSSDASNWTIDGENRTNLSFEGYLPPTCLSILHL
QEKNWSALLTAVVIILTIAGNILVIMAVSLEKKLQNATNYFLMSLAIADMLLGFLVMPVSMLTILYGYRWP
LPSKLCAVWIYLDVLFSTASIMHLCAISLDRYVAIQNPIHHSRFNSRTKAFLKIIAVWTISVGVSMPIPVF
GLQDDSKVFKQGSCLLADDNFVLIGSFVAFFIPLTIMVITYFLTIKSLQKEATLCVSDLSTRAKLASFSFL
PQSSLSSEKLFQRSIHREPGSYTGRRTMQSISNEQKACKVLGIVFFLFVVMWCPFFITNIMAVICKESCNE
HVIGALLNVFVWIGYLSSAVNPLVYTLFNKTYRSAFSRYIQCQYKENRKPLQLILVNTIPALAYKSSQLQA
GQNKDSKEDAEPTDNDCSMVTLGKQQSEETCTDNINTVNEKVSCV
Only the first 70 amino acids are used for prediction.
Both ways can be employed at the same time: all the specified sequences will be processed.
Select one of the three output options ("Extensive, with graphics", "Extensive, no graphics", or "Short") and click on the "Submit" button.
The output format is essentially in GFF format. The default (long) output format looks like this:
# ANIA_NEIGO SpII score=29.6052 margin=11.2327 cleavage=18-19 Pos+2=G # Cut-off=-3 ANIA_NEIGO LipoP1.0:Best SpII 1 1 29.6052 ANIA_NEIGO LipoP1.0:Margin SpII 1 1 11.2327 ANIA_NEIGO LipoP1.0:Class SpI 1 1 18.3725 ANIA_NEIGO LipoP1.0:Class CYT 1 1 -0.200913 ANIA_NEIGO LipoP1.0:Signal CleavII 18 19 29.6052 # FALAA|CGGEQ Pos+2=G ANIA_NEIGO LipoP1.0:Signal CleavI 24 25 18.0333 # GGEQA|AQAPA ANIA_NEIGO LipoP1.0:Signal CleavI 20 21 15.9259 # LAACG|GEQAA ANIA_NEIGO LipoP1.0:Signal CleavI 26 27 12.0794 # EQAAQ|APAET ANIA_NEIGO LipoP1.0:Signal CleavI 25 26 11.4077 # GEQAA|QAPAE ANIA_NEIGO LipoP1.0:Signal CleavI 27 28 9.40252 # QAAQA|PAETP(output trunctated)
The first line, which is the only line if short
output is chosen, summarizes the best prediction. In the example the
best prediction is a lipoprotein with a cleavage site between amino acid
18 and 19 and amino acid G (glycine) in position +2 after the cleavage site.
The second line gives the cut-off used. In the following the columns contain
These 4 clases are predicted
SpI: signal peptide (signal peptidase I)
SpII: lipoprotein signal peptide (signal peptidase II)
TMH: n-terminal transmembrane helix. This is generally not a very reliable prediction and should be tested. This part of the model is mainly there to avoid tranmembrane helices being falsely predicted as signal peptides.
CYT: cytoplasmic. It really just means all the rest.
For technical reasons (see paper) the score for CYT is always the same.
These signals are predicted:
CleavI: Cleavage sites for (signal peptidase I).
CleavII: Cleavage sites for (signal peptidase II).
Below the plot there are links to
Correct class |
Predicted class |
||||
SPaseI |
SPaseII |
Cytoplasmic |
TMH |
Total |
|
SPaseI |
309 |
2 |
14 |
3 |
328 |
SPaseII |
2 |
61 |
0 |
0 |
63 |
Cytoplasmic |
5 |
1 |
382 |
0 |
388 |
TMH |
8 |
0 |
21 |
142 |
171 |
1
Center for Biological Sequence Analysis, BioCentrum-DTU,
The Technical University of Denmark, DK-2800 Lyngby, Denmark
2
Department of Biochemistry, Stockholm University,
S-106 91 Stockholm, Sweden
3
Bioinformatics Centre, University of Copenhagen,
Universitetsparken 15, 2100 Copenhagen, Denmark
PMID: 12876315
If you need help regarding technical issues (e.g. errors or missing results) contact Technical Support. Please include the name of the service and version (e.g. NetPhos-4.0) and the options you have selected. If the error occurs after the job has started running, please include the JOB ID (the long code that you see while the job is running).
If you have scientific questions (e.g. how the method works or how to interpret results), contact Correspondence.
Correspondence:
Technical Support: