Prediction of Intrinsically Unstructured Proteins

 

IUPred
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ANCHOR
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How to use

Title

You can specify the title of your request here. This is optional.

Sequence input

There are two ways to input a protein sequence:

I - If the protein is deposited in the UniProt database (either in SwissProt or TrEMBL) you can specify the accession code or the ID of the protein in the "Enter SWISS-PROT/TrEMBL identifier or accession number" filed. The ANCHOR server is always linked to newest version of UniProt. The header of the UniProt entry will be displayed as the title in the results page.
II - Type or cut and paste your sequence in the "paste the amino acid sequence" filed. The amino acid sequence must be in the standard single letter code format. Spaces and other non-standard characters within the pasted sequence are permitted, however they will be removed with the remaining sequence treated as a single continuous chain. If the first line starts with the ">" character (e.g FASTA sequence headers) it will be used as the title in the results page. The minimum sequence length is 6 residues. The recommended sequence format is this:

>Name of the sequence
MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIE

Prediction type

There are three different prediction types offered, each using different parameters optimized for slightly different applications. These are: long disorder, short disorder, and structured domains.

Long disorder:
The main profile of our server is to predict context-independent global disorder that encompasses at least 30 consecutive residues of predicted disorder. For this application the sequential neighbourhood of 100 residues is considered.

Short disorder:
It uses a parameter set suited for predicting short, probably context-dependent, disordered regions, such as missing residues in the X-ray structure of an otherwise globular protein. For this application the sequential neighbourhood of 25 residues is considered. As chain termini of globular proteins are often disordered in X-ray structures, this is taken into account by an end-adjustment parameter which favors disorder prediction at the ends.

Structured domains:
The dependable identification of ordered regions is a crucial step in target selection for structural studies and structural genomics projects. Finding putative structured domains suitable for stucture determination is another potential application of this server. In this case the algorithm takes the energy profile and finds continuous regions confidently predicted ordered. Neighbouring regions close to each other are merged, while regions shorter than the minimal domain size of at least 30 residues are ignored. When this prediction type is selected, the region(s) predicted to correspond to structured/globular domains are returned.

Output type

Raw data only:
This offers a simple text output. For predicition types "local" and "global" disorder there is a line corresponding to each residue, specifying its sequential number, residues type, and its score. This score can be between 0 and 1. Scores above 0.5 indicate disorder. If the prediction type "structured domains" was selected, only the sequence is returned, with uppercase letters indicating putative globular domains.

Generate plot:
Beside the text output, a graphical image (a png file) is generated using JpGraph softwer. Large sequences are chopped into smaller fragments. The user can change the window size of this plot. If the prediction type "structured domains" was selected, the corresponding regions are indicated by thick lines on the graph.

 
 


References:

The Pairwise Energy Content Estimated from Amino Acid Composition Discriminates between Folded and Intrinsically Unstructured Proteins
Zsuzsanna Dosztányi, Veronika Csizmók, Péter Tompa and István Simon
J. Mol. Biol. (2005) 347, 827-839.

IUPred: web server for the prediction of intrinsically unstructured regions of proteins based on estimated energy content
Zsuzsanna Dosztányi, Veronika Csizmók, Péter Tompa and István Simon
Bioinformatics (2005) 21, 3433-3434.


 
 
Zsuzsanna Dosztanyi | Peter Tompa | Istvan Simon | Institute of Enzymology