Difference between revisions of "Sequence-based predictions (Phenylketonuria)"
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|GO:0004505||F||84%||phenylalanine 4-monooxygenase activity |
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− | |GO:0004510 |
+ | |* GO:0004510||F||80%||tryptophan 5-monooxygenase activity |
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− | |GO:0004511 |
+ | |* GO:0004511||F||79%||tyrosine 3-monooxygenase activity |
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|GO:0046872||F||78%||metal ion binding |
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|GO:0005506||F||78%||iron ion binding |
|GO:0005506||F||78%||iron ion binding |
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− | |GO:0008199 |
+ | |* GO:0008199||F||72%||ferric iron binding |
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− | |GO:0008198 |
+ | |* GO:0008198||F||72%||ferrous iron binding |
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|GO:0016597||F||71%||amino acid binding |
|GO:0016597||F||71%||amino acid binding |
Revision as of 10:06, 22 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
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 (Table ...) 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
"Secondary Structure" | |||||||
---|---|---|---|---|---|---|---|
Type | ReProf | PsiPred | DSSP | ||||
Helix (alpha) | H | H | GHI | ||||
Extended strand (beta) | E | E | BE | ||||
Loops/Turns | L | C | ST |
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 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. A java script (...) was written which counts the number of matches between two sequences and the total number of residues. Then the sensitivity for each letter and in total is calculated and given in %.
senstitivity = number of matches / number of residues
As there are positions which have no prediction in DSSP or Uniprot (indicated by '-'), those positions are ignored.
"Sensitivity 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 |
"Sensitivity 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 |
"Sensitivity 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 |
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 (Tables: ...).
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 |
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:
- long disorders
- short disorders
- globular (structured) domains <ref name="iupred"> Zsuzsanna Dosztányi, Veronika Csizmok, Peter Tompa and István Simon (2005): "IUPred: web server for the prediction of intrinsically unstructured regions of proteins based on estimated energy content". BMC Bioinformatics Vol.21:3433–3434. doi:10.1093/bioinformatics/bti541 </ref>
//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.
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:
- P00439 (PAH): best hit with an E-Value of 1.1e-116 leading to the protein Tyrosine 3-monooxygenase (P04177)
- P10775 (RNH1): best hit with an E-Value of 1.4e-24 leading to the protein NALP1 (Q9C000-1)
- Q9X0E6 (CUTA): best hit with an E-Value of 0.34 leading to an Uncharacterized protein (Q57696)
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.
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:
- 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.
(see also Output format)
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 |
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. Table ... shows the results of the N-terminal run only.
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
... GOPET
Sequence ID: 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 |
In a GOPET run the confidence threshold can be chosen. On default it is at 60%, but a threshold of 50% was tried. However, there were only 4 more predictions which are not found at QuickGo for this protein and therefore not mentioned.
Besides of the four GOs marked with * all were found at QuickGO (see also GO-terms)
Pfam
... Pfam P00439
References
<references/>