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NetPhosBac - 1.0

Generic phosphorylation sites in bacterial proteins

The NetPhosBac 1.0 server predicts serine and threonine phosphorylation sites in bacterial proteins. This service is closely related to NetPhos,NetPhosK and NetPhosYeast.

Submission

Sequence submission: paste the sequence(s) and/or upload a local file

Restrictions:
At most 2,000 sequences and 200,000 amino acids per submission; each sequence not longer than 6,000 amino acids.

Confidentiality:
The sequences are kept confidential and will be deleted after processing.

CITATIONS

For publication of results, please cite:

NetPhosBac - A predictor for Ser/Thr phosphorylation sites in bacterial proteins.
Martin Lee Miller, Boumediene Soufi, Carsten Jers, Nikolaj Blom, Boris Macek and Ivan Majakovic.
Proteomics. 2008 Dec 3. PUBMED

Instructions

1. Specify the input sequences

All the input sequences must be in one-letter amino acid code. The allowed alphabet (not case sensitive) is as follows:

A C D E F G H I K L M N P Q R S T V W Y and X (unknown)

All the other symbols will be converted to X before processing. The sequences can be input in the following two ways:

Paste a single sequence (just the amino acids) or a number of sequences in FASTA format into the upper window of the main server page.
Select a FASTA file on your local disk, either by typing the file name into the lower window or by browsing the disk.

Both ways can be employed at the same time: all the specified sequences will be processed. However, there may be not more than 2,000 sequences and 200,000 amino acids in total in one submission. The sequences longer than 6,000 amino acids are not allowed.

2. Customize your run

By default the server produces graphical output illustrating the predictions (in GIF). The graphs can be very valuable for locating the "hot" spots in your proteins. The generation of graphics can be disabled by un-checking the button labelled 'Generate graphics'.

If the button labelled 'Output in GFF' is checked the text output of the server will be in GFF

3. Submit the job

Click on the "Submit" button. The status of your job (either 'queued' or 'running') will be displayed and constantly updated until it terminates and the server output appears in the browser window.

At any time during the wait you may enter your e-mail address and simply leave the window. Your job will continue; you will be notified by e-mail when it has terminated. The e-mail message will contain the URL under which the results are stored; they will remain on the server for 24 hours for you to collect them.

Output format

DESCRIPTION

For each input sequence the length and the name of the sequence are stated followed by a table with the prediction results. There is a table row for each serine or threonine residue in the sequence; the columns are:

sequence name, truncated to 20 characters;
residue position in the sequence;
residue: serine (S) or threonine (T);
score, a number between 0 and 1; when the score is above 0.5 the residue is a predicted phosphorylation site;
kinase: the current version of NetPhosBac does not make kinase specific predictions;
answer: either the word "YES" or a dot ("."), reflecting the score.

After the table, the whole sequence is printed alongside a summary of the predicted glycation sites and their positions.

Finally, if the 'Generate graphics' button has been checked, the server displays a figure in GIF showing a plot of the score for each serine or threonine residue against the sequence position of that residue. -->

EXAMPLE OUTPUT

The example below shows the output for the UniProt entry P0A6C7 --> Amino-acid acetyltransferase -->

>sp_P0A6C7_ARGA_ECO57 443 amino acids # # netphosbac-1.0a prediction results # # Sequence # x Context Score Kinase Answer # ------------------------------------------------------------------- # sp_P0A6C7_ARGA_ECO57 7 T KERKTELVE 0.319 main . # sp_P0A6C7_ARGA_ECO57 16 S GFRHSVPYI 0.591 main Y # sp_P0A6C7_ARGA_ECO57 22 T PYINTHRGK 0.482 main . # sp_P0A6C7_ARGA_ECO57 27 T HRGKTFVIM 0.474 main . # sp_P0A6C7_ARGA_ECO57 43 S HENFSSIVN 0.461 main . # sp_P0A6C7_ARGA_ECO57 44 S ENFSSIVND 0.498 main . # sp_P0A6C7_ARGA_ECO57 54 S GLLHSLGIR 0.673 main Y # sp_P0A6C7_ARGA_ECO57 89 T NIRVTDAKT 0.199 main . # sp_P0A6C7_ARGA_ECO57 93 T TDAKTLELV 0.420 main . # sp_P0A6C7_ARGA_ECO57 103 T QAAGTLQLD 0.171 main . # sp_P0A6C7_ARGA_ECO57 109 T QLDITARLS 0.452 main . # sp_P0A6C7_ARGA_ECO57 113 S TARLSMSLN 0.271 main . # sp_P0A6C7_ARGA_ECO57 115 S RLSMSLNNT 0.306 main . # sp_P0A6C7_ARGA_ECO57 119 T SLNNTPLQG 0.160 main . # sp_P0A6C7_ARGA_ECO57 130 S INVVSGNFI 0.439 main . # sp_P0A6C7_ARGA_ECO57 150 S DYCHSGRIR 0.839 main Y # sp_P0A6C7_ARGA_ECO57 167 S RQLDSGAIV 0.488 main . # sp_P0A6C7_ARGA_ECO57 179 S PVAVSVTGE 0.305 main . # sp_P0A6C7_ARGA_ECO57 181 T AVSVTGESF 0.285 main . # sp_P0A6C7_ARGA_ECO57 184 S VTGESFNLT 0.499 main . # sp_P0A6C7_ARGA_ECO57 188 T SFNLTSEEI 0.202 main . # sp_P0A6C7_ARGA_ECO57 189 S FNLTSEEIA 0.418 main . # sp_P0A6C7_ARGA_ECO57 194 T EEIATQLAI 0.228 main . # sp_P0A6C7_ARGA_ECO57 210 S IGFCSSQGV 0.476 main . # sp_P0A6C7_ARGA_ECO57 211 S GFCSSQGVT 0.413 main . # sp_P0A6C7_ARGA_ECO57 215 T SQGVTNDDG 0.184 main . # sp_P0A6C7_ARGA_ECO57 223 S GDIVSELFP 0.342 main . # sp_P0A6C7_ARGA_ECO57 245 S GDYNSGTVR 0.533 main Y # sp_P0A6C7_ARGA_ECO57 247 T YNSGTVRFL 0.359 main . # sp_P0A6C7_ARGA_ECO57 260 S KACRSGVRR 0.776 main Y # sp_P0A6C7_ARGA_ECO57 269 S CHLISYQED 0.312 main . # sp_P0A6C7_ARGA_ECO57 282 S QELFSRDGI 0.319 main . # sp_P0A6C7_ARGA_ECO57 288 T DGIGTQIVM 0.264 main . # sp_P0A6C7_ARGA_ECO57 294 S IVMESAEQI 0.516 main Y # sp_P0A6C7_ARGA_ECO57 302 T IRRATINDI 0.318 main . # sp_P0A6C7_ARGA_ECO57 326 S LVRRSREQL 0.456 main . # sp_P0A6C7_ARGA_ECO57 338 T IDKFTIIQR 0.451 main . # sp_P0A6C7_ARGA_ECO57 345 T QRDNTTIAC 0.442 main . # sp_P0A6C7_ARGA_ECO57 346 T RDNTTIACA 0.309 main . # sp_P0A6C7_ARGA_ECO57 374 S PDYRSSSRG 0.287 main . # sp_P0A6C7_ARGA_ECO57 375 S DYRSSSRGE 0.771 main Y # sp_P0A6C7_ARGA_ECO57 376 S YRSSSRGEV 0.701 main Y # sp_P0A6C7_ARGA_ECO57 392 S QAKQSGLSK 0.288 main . # sp_P0A6C7_ARGA_ECO57 395 S QSGLSKLFV 0.581 main Y # sp_P0A6C7_ARGA_ECO57 401 T LFVLTTRSI 0.432 main . # sp_P0A6C7_ARGA_ECO57 402 T FVLTTRSIH 0.362 main . # sp_P0A6C7_ARGA_ECO57 404 S LTTRSIHWF 0.729 main Y # sp_P0A6C7_ARGA_ECO57 414 T ERGFTPVDI 0.359 main . # sp_P0A6C7_ARGA_ECO57 424 S LLPESKKQL 0.294 main . # sp_P0A6C7_ARGA_ECO57 435 S YQRKSKVLM 0.548 main Y # MVKERKTELVEGFRHSVPYINTHRGKTFVIMLGGEAIEHENFSSIVNDIG # 50 LLHSLGIRLVVVYGARPQIDANLAAHHHEPLYHKNIRVTDAKTLELVKQA # 100 AGTLQLDITARLSMSLNNTPLQGAHINVVSGNFIIAQPLGVDDGVDYCHS # 150 GRIRRIDEDAIHRQLDSGAIVLMGPVAVSVTGESFNLTSEEIATQLAIKL # 200 KAEKMIGFCSSQGVTNDDGDIVSELFPNEAQARVEAQEEKGDYNSGTVRF # 250 LRGAVKACRSGVRRCHLISYQEDGALLQELFSRDGIGTQIVMESAEQIRR # 300 ATINDIGGILELIRPLEQQGILVRRSREQLEMEIDKFTIIQRDNTTIACA # 350 ALYPFPEEKIGEMACVAVHPDYRSSSRGEVLLERIAAQAKQSGLSKLFVL # 400 TTRSIHWFQERGFTPVDIDLLPESKKQLYNYQRKSKVLMADLG # 450 %1 ...............S.................................. # 50 %1 ...S.............................................. # 100 %1 .................................................S # 150 %1 .................................................. # 200 %1 ............................................S..... # 250 %1 .........S.................................S...... # 300 %1 .................................................. # 350 %1 ........................SS..................S..... # 400 %1 ...S..............................S........ .

References

NetPhosBac - A predictor for Ser/Thr phosphorylation sites in bacterial proteins. in bacteria.
Martin Lee Miller^1,4, Boumediene Soufi^2,4, Carsten Jers², Nikolaj Blom¹ Boris Macek³ and Ivan Majakovic².
Proteomics. 2008 Dec 3. PUBMED

¹Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, DK-2800 Lyngby, Denmark
²Center for Microbial Biotechnology, Department of Systems Biology, Technical University of Denmark, DK-2800 Lyngby, Denmark
³Department of Proteomics and Signal Transduction, Max-Planck-Institute for Biochemistry, DE-82152 Martinsried, Germany

⁴These authors contributed equally to this work.

Abstract

There is ample evidence for the involvement of protein phosphorylation on serine/threonine/tyrosine in bacterial signaling and regulation, but very few exact phosphorylation sites have been experimentally determined. Recently, gel-free high accuracy MS studies reported over 150 phosphorylation sites in two bacterial model organisms Bacillus subtilis and Escherichia coli. Interestingly, the analysis of these phosphorylation sites revealed that most of them are not characteristic for eukaryotic-type protein kinases, which explains the poor performance of eukaryotic data-trained phosphorylation predictors on bacterial systems. We used these large bacterial datasets and neural network algorithms to create the first bacteria-specific protein phosphorylation predictor: NetPhosBac. With respect to predicting bacterial phosphorylation sites, NetPhosBac significantly outperformed all benchmark predictors. Moreover, NetPhosBac predictions of phosphorylation sites in E. coli proteins were experimentally verified on protein and site-specific levels. In conclusion, NetPhosBac clearly illustrates the advantage of taxa-specific predictors and we hope it will provide a useful asset to the microbiological community.

GETTING HELP

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). 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: