Sequence-based predictions (Phenylketonuria)
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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
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P00439 (PAH)
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P10775 (RNH1)
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Q9X0E6 (CUTA)
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Q08209 (PPP3CA)
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Disorder
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IUPred
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MD (MetaDisorder)
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Transmembrane helices
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PolyPhobius
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P00439 (PAH)
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P35462 (DRD3)
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Q9YDF8 (KVAP)
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P47863 (AQP4)
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MEMSAT-SVM
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Signal peptides
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GO terms
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Pfam
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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?
