NetGPI - 1.1

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NetGPI is a glycosylphosphatidylinositol anchoring (GPI-anchoring or glypiation), prediction tool. It is a member of our protein sorting prediction tool suite. NetGPI is a deep learning approach, which is based on recurrent neural networks and incorporates an attention mechanism to "point" out potential ω-sites.

NetGPI expects all entries to be residue sequences, designated for the secretory pathway and relies on prior evidence for the N-terminal signal peptide. If you do not have experimental evidence for the signal peptide, for some or all of the desired entries, then we would like to refer you to SignalP to filter out non-secretory entries. NetGPI only considers, at maximum, the last 100 C-terminal amino acids. In theory NetGPI can process sequences of any length but for practical purposes the submission process rejects submissions if they include sequences with more than 10000 residues.

Two output options are provided: A long output format, which includes prediction probability distribution profile graphs over the last 100 amino acids as well as an appended sentinel *; and a short output format, which reports the prediction interpretation as well as the prediction probability distributions in tabular output files.

One annotation is attributed to each protein, the one that has the highest probability. If the highest probability is within the amino-acid sequence, then the protein is considered GPI-anchored and the amino-acid position at the peak is the predicted ω-site. If the highest probability is at the sentinel, here represented by *, then the protein is considered non GPI-anchored.

Currently the generation of the probability profile graphs is much slower than the actual prediction and we do not recommend the long form output option for much more than 100 entries at a time.

The maximum number of proteins is 5000. The maximum number of residues in any given sequence is 10000. The long output format might timeout for more than 100 entries.

For example proteins Click here

Upload local FASTA file:

Output format: Long output Short output (no figures)

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 alphabetic symbols not in the allowed alphabet will be converted to X before processing. All the non-alphabetic symbols, including white space and digits, will be ignored.

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 5,000 sequences in one submission. The sequences may not be longer than 10,000 amino acids.

2. Customize your run

Generating figures for a large number of samples takes much longer than executing a prediction. Consider using the short option for large sample batches.

• Output format:
  You can choose between two output formats:

  Long

  Appropriate for most users. Shows one plot and one summary per sequence.

  Short

  Convenient if you submit lots of sequences. Shows only one line of output per sequence and no 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.

Example Outputs

By default the server produces the following output for each input sequence. The example below shows the output for intestinal-type alkaline phosphatase 1, taken from the Uniprot entry PPBI1_RAT. The lipidation position prediction is consistent with the database annotation.

One annotation is attributed to each protein, the one that has the highest probability. If the highest probability is within the amino-acid sequence, then it is considered GPI-anchored and the amino-acid position at the peak is the predicted omega-site. If the highest probability is at the sentinel, here represented by *, then the protein is considered non GPI-anchored.

If a GPI-anchor is predicted, the omega-site position is reported as well.

On the plot we see the likelihood distribution over the protein sequence, with the added sentinel *. Only the last 100 amino-acids are considered.

Example: Mature protein - standard output format

The NetGPI dataset

The datasets for training and benchmarking NetGPI-1.1 can be found here. The dataset is provided in 2-line FASTA format.

The format is as follows:

>uniprot_ac|kingdom|anchoring|pos_from_end|pos_from_beginning|part_no|anchor_exp|omega_exp
amino-acid sequence

where:

• uniprot_ac is an accession number
• kingdom is the organism's kingdom
• anchoring is GPI-anchored or non_GPI-anchored
• pos_from_end is the position within the sequence from the end, where 0 is the sentinel
• pos_from_beginning is the position within the truncated sequence from the beginning
• part_no is the partition that the protein is assigned to
• anchor_exp is 1 when the entry has experimental evidence for the GPI-anchoring signal sequence, 0 otherwise
• omega_exp is 1 when the entry has experimental evidence for the omega-site, 0 otherwise

NetGPI dataset: download

Article abstract