Sequence-based predictions (Phenylketonuria)

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Revision as of 12:58, 14 May 2013 by Worfk (talk | contribs) (Disprot)

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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)

In the following tables the predicted structures of ReProf are compared against the structure prediciton of DSSP and PsiPred. Furthermore they are compared to the recorded structure in UniProt.

"Sensitivity of predicted secondary structures against the DSSP structure."
Letter FASTA PSSM-Big PSSM-Swissprot PsiPred Uniprot
E 21 63 81 84 63
H 72 95 92 83 91
L 72 85 79 95 73
total 46 64 64 63 73
"Sensitivity of predicted secondary structures against the PsiPred structure."
Letter FASTA PSSM-Big PSSM-Swissprot DSSP Uniprot
E 20 69 91 87 60
H 78 100 99 98 96
L 62 77 71 33 0
total 63 84 84 63 42



"Sensitivity of predicted secondary structures against the Uniprot structure."
Letter FASTA PSSM-Big PSSM-Swissprot PsiPred DSSP
E 22 56 71 73 80
H 74 97 95 89 100
L 0 0 0 0 0
total 31 43 44 42 73


Bad total values at DSSP and Uniprot comparison are caused by the unknown ... ...

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/.../iupred

Afterwards one can invoke the programm as shown here:

iupred sequence.fasta long/short/glob > output.txt

Since the output is only given to Standard Out, we had to save the output into a file.

MD (MetaDisorder)

MetaDisorder is a ...

To invoke the programm one can use following command:

predictprotein --seqfile sequence.fasta --target metadisorder -p output_name -o output-directory


Disprot

We could not find exact matchings for our protein as well as two other proteins, so we used the following best hits done with Sequence Search and Smith Waterman Search Algorithm:

The only protein with a match in Disprot, was Q08209 (PPP3CA).

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?