Difference between revisions of "Sequence-based predictions (Phenylketonuria)"
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=== IUPred === |
=== IUPred === |
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With IUPred one can predict long and short disorders as well as globular domains. ... |
With IUPred one can predict long and short disorders as well as globular domains. ... |
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| − | First we cpmpiled IUPred with following command: |
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| + | First we compiled IUPred with following command: |
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cc /opt/iupred/iupred.c -o /mnt/home/student/worfk/Masterpractical/Task3/iupred |
cc /opt/iupred/iupred.c -o /mnt/home/student/worfk/Masterpractical/Task3/iupred |
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Revision as of 19:39, 13 May 2013
Page is still under construction!!!
Contents
Summary
Sequence-based prediction approaches are useful to predict a variety of structural and functional properties of proteins. Here, we used different methods to provide useful information about our protein sequence of phenylalanine hydroxylase (PAH - P00439) and in some cases likewise for other given proteins (in brackets):
- ReProf for secondary structure prediction (P10775, Q9X0E6, Q08209)
- IUPred and MD (MetaDisorder) for the prediction of the disorder (P10775, Q9X0E6, Q08209)
- PolyPhobius and MEMSAT-SVM to predict transmembrane helices (P35462, Q9YDF8, P47863)
- SignalP to predict signal peptides (P02768, P47863, P11279)
- GOPET and ProtFun2.0 to predict GO terms
- Pfam with a sequence search to find out more about the Pfam family of our protein
The results are here presented and discussed in detail.
Secondary structure
We wrote a program to filter the ReProf, PsiPred and DSSP outputs for the secondary structure: filter_seqStruc.pl
For DSSP PDB files are needed. Empty positions are converted to '-'. The PDB IDs are:
- P10775: 2BNH
- Q9X0E6: 1VHF
- Q08209: ...
- P00439: 1PAH
| "Secondary Structure" | |||||||
|---|---|---|---|---|---|---|---|
| Type | ReProf | PsiPred | DSSP | ||||
| Helix (alpha) | H | H | GHI | ||||
| Extended strand (beta) | E | E | BE | ||||
| Loops/Turns | L | C | ST | ||||
P10775 (RNH1)
| "Sensitivity of predicted secondary structures against DSSP structures." | |||||||
|---|---|---|---|---|---|---|---|
| Letter | FASTA | PSSM-Big | PSSM-Swissprot | PsiPred | Uniprot | ||
| E | 21.0 | 63.0 | 81.0 | 84.0 | 100.0 | ||
| H | 72.0 | 95.0 | 92.0 | 83.0 | 100.0 | ||
| L | 72.0 | 85.0 | 79.0 | 95.0 | 0.0 | ||
| total | 63.0 | 87.0 | 87.0 | 86.0 | 96.0 | ||
| "Sensitivity of predicted secondary structures against Uniprot structures." | |||||||
|---|---|---|---|---|---|---|---|
| Letter | FASTA | PSSM-Big | PSSM-Swissprot | PsiPred | Uniprot | ||
| E | 22.0 | 56.0 | 71.0 | 73.0 | 80.0 | ||
| H | 74.0 | 97.0 | 95.0 | 89.0 | 100.0 | ||
| L | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ||
| total | 64.0 | 89.0 | 90.0 | 86.0 | 96.0 | ||
Q9X0E6 (CUTA)
...
Q08209 (PPP3CA)
...
P00439 (PAH)
...
Disorder
...
IUPred
With IUPred one can predict long and short disorders as well as globular domains. ...
First we compiled IUPred with following command:
cc /opt/iupred/iupred.c -o /mnt/home/student/worfk/Masterpractical/Task3/iupred
Afterwards one can use the following command for the prediction. Since the output is only given to the standardout, one should save the output into a file.
iupred sequence.fasta long/short/glob > out.txt
MD (MetaDisorder)
...
Transmembrane helices
...
PolyPhobius
...
P00439 (PAH)
...
P35462 (DRD3)
...
Q9YDF8 (KVAP)
...
P47863 (AQP4)
...
MEMSAT-SVM
...
Signal peptides
...
P00439 (PAH)
...
P02768 (ALB)
...
P47863 (AQP4)
...
P11279 (LAMP1)
...
GO terms
...
Pfam
...
Discussion
Questions:
- What features are predicted?
- Discuss the results for your protein and the example proteins. Using the predictions, what could you learn about your protein and the example proteins? Compare to the available knowledge in UniProt, PDB, DisProt, OPM, PDBTM, Pfam...
- Look for other methods to get an idea how many different tools are available to predict: secondary structure, disorder, transmembrane, signal peptides and GO terms. You should be able to name several more methods in the discussion. (You can also try out more methods.)
- What else can/is be predicted from protein sequence alone?
- Which predictions can be improved considerably by structure-based approaches?
