Bioinformatic Services

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:

NetPhosYeast: Prediction of protein phosphorylation sites in yeast.
Christian Ravnsborg Ingrell, Martin Lee Miller, Ole Nørregaard Jensen and Nikolaj Blom.
Accepted for publication in Bioinformatics, 2007.

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'.

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 NetPhosYeast 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 P17536 (TPM1_YEAST), tropomyosin 1.

>TPM1_YEAST 199 amino acids # # netphosyeast-1.0a prediction results # # Sequence # x Context Score Kinase Answer # ------------------------------------------------------------------- # TPM1_YEAST 9 S REKLSNLKL 0.390 main . # TPM1_YEAST 17 S LEAESWQEK 0.692 main YES # TPM1_YEAST 45 S NQIKSLTVK 0.460 main . # TPM1_YEAST 47 T IKSLTVKNQ 0.508 main YES # TPM1_YEAST 65 S EAGLSDSKQ 0.829 main YES # TPM1_YEAST 67 S GLSDSKQTE 0.502 main YES # TPM1_YEAST 70 T DSKQTEQDN 0.383 main . # TPM1_YEAST 83 S NQIKSLTVK 0.416 main . # TPM1_YEAST 85 T IKSLTVKNH 0.338 main . # TPM1_YEAST 105 S ELAESKQLS 0.434 main . # TPM1_YEAST 109 S SKQLSEDSH 0.750 main YES # TPM1_YEAST 112 S LSEDSHHLQ 0.578 main YES # TPM1_YEAST 117 S HHLQSNNDN 0.152 main . # TPM1_YEAST 123 S NDNFSKKNQ 0.406 main . # TPM1_YEAST 136 S DLEESDTKL 0.620 main YES # TPM1_YEAST 138 T EESDTKLKE 0.308 main . # TPM1_YEAST 143 T KLKETTEKL 0.269 main . # TPM1_YEAST 144 T LKETTEKLR 0.213 main . # TPM1_YEAST 150 S KLRESDLKA 0.685 main YES # TPM1_YEAST 179 T NEELTVKYE 0.172 main . # TPM1_YEAST 195 S EIAASLENL 0.803 main YES # MDKIREKLSNLKLEAESWQEKYEELKEKNKDLEQENVEKENQIKSLTVKN # 50 QQLEDEIEKLEAGLSDSKQTEQDNVEKENQIKSLTVKNHQLEEEIEKLEA # 100 ELAESKQLSEDSHHLQSNNDNFSKKNQQLEEDLEESDTKLKETTEKLRES # 150 DLKADQLERRVAALEEQREEWERKNEELTVKYEDAKKELDEIAASLENL # 200 %1 ................S.............................T... # 50 %1 ..............S.S................................. # 100 %1 ........S..S.......................S.............S # 150 %1 ............................................S....

References

NetPhosYeast: Prediction of protein phosphorylation sites in yeast.
Christian Ravnsborg Ingrell¹, Martin Lee Miller², Ole Nørregaard Jensen¹ and Nikolaj Blom².
Accepted for publication in Bioinformatics, 2007.

¹University of Southern Denmark, Campusvej 55, DK-5230, Odense M, Denmark
²Center for Biological Sequence Analysis, BioCentrum-DTU, The Technical University of Denmark, DK-2800 Lyngby, Denmark

ABSTRACT

We here present a neural network based method for the prediction of protein phosphorylation sites in yeast - an important model organism for basic research. Existing protein phosphorylation site predictors are primarily based on mammalian data and show reduced sensitivity on yeast phosphorylation sites compared to those in humans, suggesting the need for a yeast-specific phosphorylation site predictor. NetPhosYeast achieves a correlation coefficient close to 0.75 with a sensitivity of 0.84 and specificity of 0.90 and outperforms existing predictors in the identification of phosphorylation sites in yeast.

Services

NetPhosYeast - 1.0

Serine and threonine phosphorylation sites in yeast proteins