Sequence-based predictions (Phenylketonuria)

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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

Secondary structure of a protein is ... (alpha-helices, beta-strand, loops...).

As it is not easy to look at secondary structures, there are some methods that can predict them:

  • ReProf is already installed on the students lab. An example call is shown here:
 reprof -i swiss_matrix_P00439.pssm
  • For the secondary structure prediction with PsiPred v3.3, we used the PSIPRED server
  • For DSSP PDB files are needed and the DSSP server is used to create dssp files.

There are more than one PDB ID for the Uniprot IDs and they are not completely identical to the Uniprot sequences. For example 1PAH only shows the residues 117 to 424. Nevertheless we tried to choose the most similar and align them by hand. Positions, for which no secondary structure are predicted, are marked with a '-'. Furthermore the different secondary structures are assimilated (<xr id="secondary structure"/>) so they all show the same secondary structure format like in ReProf with letters E, H and L. Therefore we wrote a program to filter the ReProf, PsiPred and DSSP outputs for the secondary structure: filter_seqStruc.pl <figtable id="secondary structure">

"Secondary Structure"
Type ReProf PsiPred DSSP
Helix (alpha) H H GHI
Extended strand (beta) E E BE
Loops/Turns L C ST
The different types to represent secondary structure in ReProf, PsiPred and DSSP.

</figtable>

P10775 (RNH1)

ReProf has the posibility to use a PSSM matrix or a FASTA sequence as input to predict secondary structure. We used three different inputs and compare them. First the FASTA sequence itself is applied, then a PSSM matrix generated by PSI-Blast against the big80 database and another matrix against the swissprot database are used for the ReProf prediction. In the following the predicted structures of ReProf are compared against the structure prediciton of DSSP and PsiPred (<xr id="DSSP"/>, <xr id="PsiPred"/>). Furthermore they are compared to the recorded structure in UniProt (<xr id="uniprot"/>). A java script (SecStrucComparison.jar) was written which counts the number of matches between two sequences and the total number of residues. Then the precision for each letter and in total is calculated and given in %.

precision = number of matches / number of residues 

As there are positions which have no prediction in DSSP or Uniprot (indicated by '-'), those positions are ignored.

<figtable id="DSSP">

"Precision of predicted secondary structures against the DSSP structure."
Letter FASTA PSSM-Big PSSM-Swissprot PsiPred Uniprot
E 21.05 63.16 80.7 84.21 100.0
H 71.94 94.9 92.35 83.16 100.0
L 71.95 85.37 79.27 95.12 0.0
total 63.28 87.16 87.16 86.27 95.96
Comparison of the secondary structures to the DSSP structure

</figtable> <figtable id="PsiPred">

"Precision of predicted secondary structures against the PsiPred structure."
Letter FASTA PSSM-Big PSSM-Swissprot DSSP Uniprot
E 20.0 69.09 90.91 97.96 100.0
H 77.71 100.0 99.4 98.19 100.0
L 61.7 77.02 70.64 65.0 0.0
total 62.5 84.43 83.55 86.27 86.1
Comparison of the secondary structures to the PsiPred structure

</figtable> <figtable id="uniprot">

"Precision of predicted secondary structures against the Uniprot structure."
Letter FASTA PSSM-Big PSSM-Swissprot PsiPred DSSP
E 22.22 55.56 71.11 73.33 80.0
H 74.16 97.19 94.94 89.33 100.0
L 0.0 0.0 0.0 0.0 0.0
total 63.68 88.79 90.13 86.1 95.96
Comparison of the secondary structures to the Uniprot structure

</figtable> In the structure of P10775 found at Uniprot no loops or turns are included, which is why L has 0% for those comparisons. All three comparisons show that the results for ReProf using the FASTA sequence are worse than using PSSMs especially for extended strands where it only has a true positive rate of about 20% to 22%. The two different databases (big80 and Swissprot), however, show nearly no differences. As the beta strand was better predicted at the Swissprot database and it has a better result at comparison with Uniprot, this database is applied for the other proteins. Additionally, PsiPred has similar results as the two Reprof predictions with PSSM matrices. Although DSSP uses the knowledge of the PDB structure, differences between the DSSP and the Uniprot secondary structure can be seen.

ReProf comparison (Q9X0E6, Q08209, P00439)

After choosing the SwissProt database for the PSSM matrices, ReProf, PsiPred and DSSP secondary structure predictions were done for the two proteins Q9X0E6 and Q08209 as well as for our protein P00439. Again the results are analyzed with our java script (<xr id="reprof"/>). <figtable id="reprof">

Q9X0E6 (CUTA)

Secondary structure comparison

Type PsiPred DSSP
E 80.95 90.0
H 62.5 69.23
L 66.67 38.1
total 67.33 66.67
Q08209 (PPP3CA)

Secondary structure comparison

Type PsiPred DSSP
E 51.52 71.7
H 80.59 85.71
L 86.67 46.43
total 80.23 64.39
P00439 (PAH)

Secondary structure comparison

Type PsiPred DSSP
E 74.0 57.14
H 88.21 85.82
L 90.82 56.82
total 87.83 73.2


Comparison of the ReProf result with PsiPred and DSSP for the three proteins Q9X0E6, Q08209 and P00439. </figtable>

After comparing the ratios of matches to the number of residues, a higher similarity of the secondary structure of ReProf to PsiPred than to DSSP can be seen. In most cases helices are predicted quite good but also the other forms are predicted well. In total the prediction of our protein P00439 show highest secondary structure similarity. Only the prediction of extended strand in comparison with DSSP show a worse result than at the other proteins. Altogether ReProf seems to make a good prediciton.

//TODO? maybe a comparison of PsiPred to DSSP?

P00439 sequences

Here the amino acid sequence of our PAH protein P00439 and its secondary structure predictions and entries are shown.

 FASTA : MSTAVLENPG LGRKLSDFGQ ETSYIEDNCN QNGAISLIFS LKEEVGALAK VLRLFEENDV
 ReProf: LLLLELLLLL LLLLLLLLLL LLLLLLLLLL LLLEEEEEEE ELLLLHHHHH HHHHHHHLLL
PsiPred: LLLLLLLLLL LLLLLLLLLL LLLLLLLLLL LLLEEEEEEE ELLLLLHHHH HHHHHHHLLL
   DSSP: ---------- ---------- ---------- ---------- ---------- ----------
Uniprot: ---------- ---------- ---------- ---------- ---------- ----------  
 
 FASTA : NLTHIESRPS RLKKDEYEFF THLDKRSLPA LTNIIKILRH DIGATVHELS RDKKKDTVPW
 ReProf: LEEEEELLLL LLLLLLEEEE EEEELLLHHH HHHHHHHHHL LLLLLLLLLL LLLLLLLLLL
PsiPred: EEEEEELLLL LLLLLLEEEE EEELLLLLHH HHHHHHHHHH LLLLLLLLLL LLLLLLLLLL
   DSSP: ---------- ---------- ---------- ---------- ---------- ----------
Uniprot: ---------- ---------- ---------- ---------- ---------- ----------

 FASTA : FPRTIQELDR FANQILSYGA ELDADHPGFK DPVYRARRKQ FADIAYNYRH GQPIPRVEYM
 ReProf: LLHHHHHHHH HHHHHHLLLL LLLLLLLLLL LLHHHHHHHH HHHLLLLLLL LLLLLLLLLL
PsiPred: LLLLHHHHHH HHHHHHHLLL LLLLLLLLLL LHHHHHHHHH HHHHHHLLLL LLLLLLLLLL
   DSSP: --LEHHHHHH HHHHLELL-H HHLLLLLLLL -HHHHHHHHH HHHHHHL--L LL--------
Uniprot: ----HHHHHH HH--EEE--H H-----LLL- -HHHHHHHHH HHHHHH---- ----------

 FASTA : EEEKKTWGTV FKTLKSLYKT HACYEYNHIF PLLEKYCGFH EDNIPQLEDV SQFLQTCTGF
 ReProf: HHHHHHHHHH HHHHHHHLHH HLHHHHHHHH HHHHHHLLLL LLLLLLHHHH HHHHHHLLLL
PsiPred: HHHHHHHHHH HHHHHHHHHL LLHHHHHHHH HHHHHHLLLL LLLLLLHHHH HHHHHHHLLE
   DSSP: HHHHHHHHHH HHHHHHHHHH HE-HHHHHHH HHHHHHH--E LLE---HHHH HHHHHHHHL-
Uniprot: HHHHHHHHHH HHHHHHHHHH ---HHHHHHH HHHHHH---- ------HHHH HHHHHHH---

 FASTA : RLRPVAGLLS SRDFLGGLAF RVFHCTQYIR HGSKPMYTPE PDICHELLGH VPLFSDRSFA
 ReProf: EEEEEELLLL LHHHHHHHHH LHHHHHHEEL LLLLLLLLLL LLHHHHHHLL LLLLLLHHHH
PsiPred: EEEELLLLLL HHHHHHHHHL LEELLLLLLL LLLLLLLLLL LLHHHHHHLL LLLLLLHHHH
   DSSP: EEEE--LE-- HHHHHHHHLL LEEEE----- -LLLLL--LL --HHHHHHHH HHHHLLHHHH
Uniprot: EEE--EE--- HHHHHHHH-- -EEE------ ---------- --HHHHHH-- HHH---HHHH

 FASTA : QFSQEIGLAS LGAPDEYIEK LATIYWFTVE FGLCKQGDSI KAYGAGLLSS FGELQYCLSE
 ReProf: HHHHHHHHHL LLLLHHHHHH HHHHHEEEEE EEEELLLLLL EEEEEEELLL LLHHHHHHLL
PsiPred: HHHHHHHHHL LLLLHHHHHH HHHHHHEEEE EEEEEELLLE EEELLLLLLL HHHHHHHHLL
   DSSP: HHHHHHHHHH LL--HHHHHH HHHHHHHHHH L-EEEELLEE EE--HHHHL- HHHHHHLLLL
Uniprot: HHHHHHHHH- ----HHHHHH HHHHHH-LLL --EEE---EE E---HHH--- HHHHHH--EE

 FASTA : KPKLLPLELE KTAIQNYTVT EFQPLYYVAE SFNDAKEKVR NFAATIPRPF SVRYDPYTQR
 ReProf: LLLLLLLLHH HLLLLLLLLL LLLLHEEHHH HHHHHHHHHH HHHHHLLLLL LLEELLLLLL
PsiPred: LLLLLLLLHH HHHLLLLLLL LLLLLEEEEL LHHHHHHHHH HHHHLLLLLL LLLLLLLLLE
   DSSP: LLEEEE--HH HHLL----LL L--LEEEEEL -HHHHHHHHH HHHHLL--LL -EEEELLLLE
Uniprot: EEEEE---HH H-------EE ---EEEEEE- -HHHHHHHHH HH------EE EEEE-LLL-E

 FASTA : IEVLDNTQQL KILADSINSE IGILCSALQK IK
 ReProf: HHHHLLHHHH HHHHHHHHHH HHHHHHHHHH HL
PsiPred: EEELLLHHHH HHHHHHHHHH HHHHHHHHHH HL
   DSSP: EEE------- ---------- ---------- --
Uniprot: EEE--HHHHH HHHHHHHHHH HHHHHHHHH- --

Again very similar outcomes can be seen for ReProf and Psipred and for DSSP and Uniprot. Thereby the later start of the secondary structure for both DSSP and Uniprot is conspicious. However none of the structures is identical to another one. Differences most often results from slightly shifted predictions, so for example in one prediction a helix strand starts a few amino acid earlier or is a few amino acids longer than in the other prediciton.

Disorder

A special interest of protein structure prediction are the so called disordered regions of protein chain, which have no fixed spatial structure in the native state. There are two different kinds of disordering:

  • some regions are structured, when they bind to other molecules or under changing conditions of biochemical medium
  • others are always disordered and never become structured

Disordered regions often cause complications in the expression, purification and crystalization of the protein containing them. This is the reason, why much attention is being paid on their examination and prediction. Most prediction methods identify disorder in proteins through the analysis of the contained amino acids or evolutionary conserved regions. <ref> Michil Yu. Lobanov, Eugeniya I. Furletova, Natalya S. Bogatyreva, Michail A. Roytberg, Oxana V. Galzitskaya (2010): "Library of Disordered Patterns in 3D Protein Structures". PLoS Comput Biol Vol.6:e1000958. doi:10.1371/journal.pcbi.1000958 </ref>

In this part, we wanted to analyse the disordered regions of our protein (P00439) as well as from the proteins from the secondary structure prediction (P10775, Q9X0E6, Q08209).

IUPred

In IUPred the prediction of disordered regions was done with amino acid sequences by a novel algorithm, which estimates their total pairwise interresidue interaction energy. This is based on the assumption that IUP sequences do not fold due to their inability to form sufficient stabilizing interresidue interactions. Thereby, IUPred provides three optional parameters:

//TODO: Overwork text

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.

IUPred webserver

MD (MetaDisorder)

MetaDisorder is a ... <ref name="md"> Lukasz P. Kozlowski and Janusz M. Bujnicki (2012): "MetaDisorder: a meta-server for the prediction of intrinsic disorder in proteins". BMC Bioinformatics Vol.13:111. doi:10.1186/1471-2105-13-111 </ref>

To invoke the programm one can use following command:

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

DisProt

DisProt is a database of ... <ref name="disprot"> Megan Sickmeier, Justin A. Hamilton, Tanguy LeGall, Vladimir Vacic, Marc S. Cortese, Agnes Tantos, Beata Szabo, Peter Tompa, Jake Chen, Vladimir N. Uversky, Zoran Obradovic and A. Keith Dunker (2007): "DisProt: the Database of Disordered Proteins". Nucleic Acids Research Vol.35, Database issue: D786–D793. doi:10.1093/nar/gkl893 </ref>

We could not find exact matchings on DisProt 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 PSI-Blast search algorithm gave the same best hits, except for the CUTA protein, but here was the E-Value in the Smith Waterman search better than in PSI-Blast, so we used this hit.

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

In the images below, one can see the regions of order and disorder of a given sequence.

Legend for the DisProt images.
Map of ordered and disordered regions from DisProt for the best sequence hit of P00439 (Tyrosine 3-monooxygenase). The disordered region is located between the 1-155 sequence position.
Map of ordered and disordered regions from DisProt for the best sequence hit of P10775 (NALP1). The disordered region is located between the 31-50 sequence position.
Map of ordered and disordered regions from DisProt for the best sequence hit of Q9X0E6 (Uncharacterized protein). The disordered region is located on the whole protein sequence.
Map of ordered and disordered regions from DisProt for the protein Q08209. Disordered regions are located between the 1-13, 374 - 468, 390 - 414, 469 - 486, 487 - 521 and the ordered region between the 14 - 373 sequence position.


Transmembrane helices

A transmembrane (TM) helix is defined as a membrane-spanning domain with hydrogen-bonded helical configuration <ref>http://www.uniprot.org Transmembrane helix definition on UniProt, retrieved May 18, 2013</ref>. Alpha-helical TM proteins are involved in a wide range of important biological processes including transport of membrane-impermeable molecules and cellular features like cell signaling, recognition or adhesion. Given that many of them are also prime drug targets one estimates that over half of the drugs on the market are targeting membrane proteins. A big problem in the prediction of transmembrane proteins is the discrimination between TM helices and other features usually composed by hydrophobic residues. These include targeting motifs like e.g. signal peptides, amphipathic helices and re-entrant helices - entering and exiting membranes on the same side. The high similarity between such properties and the hydrophobic profile of a TM helix leads in many cases to crosstalk between the different types of predictions. Should these components be predicted as TM helices, the subsequent topology prediction can be corrupted. This is the reason, why TM proteins are experimentally difficult to find and therefore are strongly under-represented in structural databases. However, sequence-based methods in the absence of structural data allow the investigation of TM protein topology. <ref name="memsat"> Timothy Nugent and David T Jones (2009): "Transmembrane protein topology prediction using support vector machinesTransmembrane protein topology prediction using support vector machines". BMC Bioinformatics Vol.10:159. doi:10.1186/1471-2105-10-159 </ref>

Here, we predicted transmembrane helices with the sequence-based tools PolyPhobius and MEMSAT-SVM for the following proteins:

Then we compared our results with the ones generated with OPM and PDBTM.

PolyPhobius

PolyPhobius is an enhanced version of Phobius and uses Hidden Markov Models (HMM) for the prediction of transmembrane topology and signal peptides. Thereby homology information is utilized to increase the accuracy of the prediction. The method depends on a high quality global multiple sequence alignment (MSA). <ref name="polyphobius"> Lukas Käll, Anders Krogh and Erik L. L. Sonnhammer (2005): "An HMM posterior decoder for sequence feature prediction that includes homology information". BMC Bioinformatics Vol.21:i251–i257. doi:10.1093/bioinformatics/bti1014 </ref>

We used the PolyPhobius installed on the server on the following path: /mnt/project/pracstrucfunc13/polyphobius/

For the prediction as well as a graphical output one can use the PolyPhobius webserver.

...

MEMSAT-SVM

MEMSAT-SVM is a revised version of MEMSAT-3 and is based on support vector machines (SVM) for the prediction of transmembrane protein topology. Signal peptides and re-entrant helices prediction are both integrated in this method. Also the discrimination between transmembrane and globular proteins can effectively be done (extremely low false positive and false negative rates). MEMSAT-SVM showed a better accuracy than MEMSAT-3. <ref name="memsat"/>

Since MEMSAT-SVM is currently not running on the biolab servers, we used the MEMSAT-SVM webserver and the fasta sequences of the above named proteins for our prediction.

...

Comparison to OPM and PDBTM

The Orientations of Proteins in Membranes (OPM) database represents a collection of transmembrane, monotopic and peripheral proteins from the Protein Data Bank (PDB). Thereby, a computational approach to calculate the spatial arrangements of protein structures in lipid bilayers has been developed and compared with experimental data. In this database one can analyse, sort and search of membrane proteins based on different properties. The created coordinate files with the calculated membrane boundaries can then all be downloaded. <ref name="opm"> Mikhail A. Lomize, Andrei L. Lomize, Irina D. Pogozheva and Henry I. Mosberg (2006): "OPM: Orientations of Proteins in Membranes database". BMC Bioinformatics Vol.22:623–625. doi:10.1093/bioinformatics/btk023 </ref>

...

The PDBTM is a database, which contains transmembrane proteins with known structures. Thereby, all transmembrane proteins, that could be found in the Protein Data Bank (PDB) database, are collected and their membrane-spanning regions are determined. These calculations are based on the TMDET algorithm, which uses only structural information to calculate the most probable location of the lipid bilayer. It can also distinguish between transmembrane and globular proteins like MEMSAT-SVM. The database is updated every week to keep it with the PDB entries synchronized. <ref name="pdbtm"> Gabor E. Tusnady, Zsuzsanna Dosztanyi and Istvan Simon (2005): "PDB_TM: selection and membrane localization of transmembrane proteins in the protein data bank". BMC Bioinformatics Vol.20:2964-2972; Nucleic Acids Research Vol.33, Database issue D275–D278. doi:10.1093/nar/gki002 </ref>

...

Signal peptides

Signal peptides are sequences of amino acids in a protein that determine the pathway of the protein in the cell to its destination.
On the server version 3.0 of SignalP predicitions is installed. We tried two different parameters for our predictions:
First we simple run SignalP without any constraints. The only thing, which has to be stated is -t euk as all four sequences are eukaryotic. Otherwise SignalP only would accept Gran+ or Gran-. -o can be set, so the output is written automatically in output.txt or it can be set with '>'.

 signalp -t euk <UniprotID>.fasta > <UniprotID>_output.out 

In our second run we choose only the N-terminal with 70 residues as it is recommended in the manual page of SignalP to avoid false positives.

 signalp -trunc 70 -t euk <UniprotID>.fasta > <UniprotID>_trunc.out 

The internet server of SignalP has version 4.1. Here only the FASTA sequences are delivered. Parameters are not changed. The output is given as <ref name="output_signalp"> http://www.cbs.dtu.dk/services/SignalP: Description of the output format of SignalP, retrieved May 20, 2013</ref>:

  • SignalP-NN (neuronal network) with the maximal values of C-score (raw cleavage site score), S-score (signal peptide score) and Y-score (combined cleavage site score). Additionaly the mean S (average S-score of the possible signal peptide) and the D-score (discrimination score), which is a weighted average of the mean S and the max. Y scores are reported. The D-Score discriminates signal peptides from non-signal peptides.
  • SignalP-HMM returns the posterior probabilities for cleavage site (C) and signal peptide (S) for each position in the input sequences. The signal peptide probability is divided into three region probabilities (n, h, c). The maximal cleavage site probability is reported with its position. For eukaryotes also the probability of a signal anchor is reported.

In our case there are only few differences between the runs for the whole sequence or only the N-terminal. For example for the whole sequence the NN result of P47863 gives also a YES for C and not only for max.S. <xr id="signalp"/> shows the results of the N-terminal run only.

<figtable id="signalp">

SignalP 3.0 SignalP 4.1 SignalP (website)
UniProtID SignalP-NN SignalP-HMM Max cleavage site prediction prediction prediction
P00439 5 x NO 0.0 signal peptide, 0.0 signal anchor 0.000 non-secretory protein no signal peptide not in database
P02768 5 x YES 1.0 signal peptide, 0.0 signal anchor 0.785 signal peptide signal peptide confirmed [1]
P11279 5 x YES 1.0 signal peptide, 0.0 signal anchor 0.847 signal peptide signal peptide confirmed [2]
P47863 4 x NO, 1 x YES (max. S) 0.526 signal peptide, 0.457 signal anchor 0.388 signal peptide no signal peptide not in database

Output of SignalP for the four proteins: The second column (SignalP-NN) shows how many of the calculated values are above the threshold to be a sognal peptide, whereas the third column (SignalP-HMM) gives information about how probable it is, that it has a signal peptide. Also a probability for having a signal anchor is given. Thereby 0.0 means it has a probability of 0% and 1.0 stands for 100%. The next column indicates the highest probability of a cleavage site and after that the predictions themselves follow for SignalP version 3.0, version 4.1 and if the prediction is correct by comparing with the database. </figtable>

For P47863 different predictions are made for the two different versions of SignalP. However, the predictions for the other proteins are the same. Furthermore, signal peptide only has a probability of about 53% and the probability of the maximum cleavage site only has 39%. In Uniprot the protein is classified as multi-pass membrane protein and maybe this is the cause for identifying the N-terminal as signal peptide. Our protein P00439 has no signal peptide which is predicted correctly, whereas P02768 and P11279 both show a signal peptide at the N-terminal region all three with high credibility. The cleavage sites predicted with version 4.1 coincide with the confirmed results on the webserver. As looking at the transmembrane helices on the webserver, none is predicted for P02768, whereas for P11279 a potential transmembrane region between position 383 and 405 can be found. ????

Other prediction tools are:

  • TatP (predicts presence and location of Twin-arginine signal peptide cleavage sites in bacteria)
  • Phobius (predicts both transmembrane topology and signal peptide)
  • PrediSi (predicts signal peptides)

We tested PrediSi with our proteins and almost get the same results as for the prediction with SignalP version 3. So for P47863 a signal peptide was predicted, while the predictions for the other three proteins are correct. Phobius, however, as it also predicts transmembrane shows the same results as SignalP version 4.

GO terms

In this part we discover two different GO annotation prediction tools. This tools try to predict function and other features using the sequences.

GOPET

The first tool is called GOPET. In a GOPET run the confidence threshold can be chosen. On default it is at 60%, but a threshold of 50% was tried, too. However, there were only 4 more predictions which are not found at QuickGO for this protein and therefore not mentioned. All other inputs are left on default. The resulting predictions can be found in <xr id="gopet"/>.

<figtable id="gopet">

GOPET prediction for UniprotID: P00439
GOid Aspect Condidence GO term
GO:0003824 F 94% catalytic activity
GO:0016491 F 88% oxidoreductase activity
GO:0004497 F 87% monooxygenase activity
GO:0004505 F 84% phenylalanine 4-monooxygenase activity
*GO:0004510 F 80% tryptophan 5-monooxygenase activity
*GO:0004511 F 79% tyrosine 3-monooxygenase activity
GO:0046872 F 78% metal ion binding
GO:0005506 F 78% iron ion binding
*GO:0008199 F 72% ferric iron binding
*GO:0008198 F 72% ferrous iron binding
GO:0016597 F 71% amino acid binding

GOs predicted with GOPET. The first column represents the GO-IDs, the second their aspect, where F stands for molecular function, the third column indicates the confidence value of the prediction of the particular GO and in the last column a short description of the GO can be found. </figtable>

Altogether eleven terms were found and besides of the four GOs marked with * all were found at QuickGO (see also GO-terms). As GOPET is a prediction tool for molecular functions, all results have F as aspect. The three predicted GOs with highest confidence level are most general and should be correct. The other eight also have a quite good confidence value. That the terms for the four GOs not found in QuickGO are very similar to some of the others (two both have monooxygenase activity, whereas the other to are iron binding) is notably. It seems that GOPET cannot distinguish similar terms very well, but nevertheless is a good prediciton tool for first annotation.

ProtFun

The second tool ProtFun determines four different classes/categories for a protein: Functional category, enzyme or no enzyme, enzyme class with EC number and gene ontology category. Furthermore each predicted term has two scores. The first one indicates the estimated probability that the entry belongs to the predicted class, whereas the second reports the odds of the sequence belonging to the class or category. <xr id="protfun"/> presents the result for our protein PAH. Thereby, the entries with significant values are marked in green. <figtable id="protfun">

ProtFun prediction for UniprotID: P00439
Functional category                  Prob     Odds
Amino_acid_biosynthesis              0.210    9.530
Biosynthesis_of_cofactors            0.229    3.180
Cell_envelope                        0.034    0.563
Cellular_processes                   0.063    0.867
Central_intermediary_metabolism      0.061    0.970
Energy_metabolism                    0.343    3.815
Fatty_acid_metabolism                0.025    1.889
Purines_and_pyrimidines              0.392    1.615
Regulatory_functions                 0.020    0.125
Replication_and_transcription        0.118    0.438
Translation                          0.204    4.630
Transport_and_binding                0.024    0.060
Enzyme/nonenzyme     Prob     Odds
Enzyme               0.724    2.527
Nonenzyme            0.276    0.387
Enzyme class                     Prob     Odds
Oxidoreductase (EC 1.-.-.-)      0.154    0.738
Transferase    (EC 2.-.-.-)      0.271    0.785
Hydrolase      (EC 3.-.-.-)      0.083    0.261
Lyase          (EC 4.-.-.-)      0.047    1.002
Isomerase      (EC 5.-.-.-)      0.100    3.138
Ligase         (EC 6.-.-.-)      0.019    0.370
Gene Ontology category      Prob     Odds
Signal_transducer           0.075    0.350
Receptor                    0.003    0.016
Hormone                     0.001    0.206
Structural_protein          0.005    0.166
Transporter                 0.025    0.229
Ion_channel                 0.010    0.168
Voltage-gated_ion_channel   0.005    0.232
Cation_channel              0.010    0.215
Transcription               0.043    0.334
Transcription_regulation    0.032    0.255
Stress_response             0.010    0.118
Immune_response             0.012    0.140
Growth_factor               0.006    0.407
Metal_ion_transport         0.009    0.020

GO anotation predicted with ProtFun. The first cell reports the functional categories, the second gives information if the sequence is predicted to be an enzyme or not, whereas the third cell then displays the possible enzyme classes. The final cell, finally, denotes the GO categories for the sequence. Thereby each prediction is related with its probability and odds. </figtable> Most significant in functional category is amino acid biosynthesis, whereas the other categories are not significant for PAH, which is correct. Furhtermore the protein is correctly identified as enzyme, however, the enzyme class mistakenly is detected as isomerase instead of oxidoreductase, which only reached the fourth best score. Maybe the isomerase is included in the amino acid biosynthesis and thereby not completely wrong. The correct EC number of PAH is EC 1.14.16.1. Remarkably, there is no significant score for any of the predicted gene ontology categories. Altogether the prediction mostly seems to be reasonable. Additionally individual features used by ProtFun 2.2 can be viewed if selected.

Another tool to predict function of a protein for example would be PFP <ref name="pfp"> Troy Hawkins, Meghana Chitale, Stanislav Luban, and Daisuke Kihara (2009): "PFP: Automated prediction of gene ontology functional annotations with confidence scores using protein sequence data". Proteins Vol.74:566-582. doi:10.1002/prot.22172. </ref>. The results can be seen on PFP(PAH).

PFam

PFam is a database where protein families are collected.
Our protein (Pfam P00439) belongs to the ACT and the Biopterin_H (biopterin-dependent aromatic amino acid hydroxylases) family:

  • ACT are protein domains associated with metabolism as they often are linked to metabolic enzymes. In PAH ACT is located on the N-terminus of the protein.
  • Biopterin_H represents a family of aromatic amino acid hydroxylases. All members have a rate-limiting influence on important metabolic pathways. They are regulated by phosporylation at serines in their N-termini and it is believed that they include a conserved C-terminal cataltic domain and an unrelated N-terminal regulatory domain. Deficiencies in Biopterin_H cause PKU.

When you run a sequence search, the results are nearly the same (sequence search). ...

Other Protein familiy search tools: DomainSweep: scannes protein family databases ?

References

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