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

In general secondary structure of a protein is the 3D form formed by inter-residue interactions like hydrogen bonds. Most commen are alpha-helices and beta-strands, but also loops or turns can be formed.

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.
- The PDB IDs are:
  - P10775 :2BNH (Ribonuclease inhibitor, eukaryote: sus scrofa (pig))
  - Q9X0E6: 1VHF (Divalent-cation tolerance protein CutA, bacterium: thermotoga maritima)
  - Q08209: 1AUI (Serine/threonine-protein phosphatase 2B catalytic subunit alpha isoform, eukaryote: homo sapiens)
  - P00439: 1PAH (Phenylalanine-4-hydroxylase, eukaryote: homo sapiens)

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.

"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 (CUTA) and Q08209 (PP2BA) as well as for our protein P00439 (PAH). 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.

P00439 sequences

Here the amino acid sequence of our protein (PAH) as well as its secondary structure predictions and records 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.

Other tools are GOR4 or CFSSP. However, GOR4 prediciton is not that similar to the other secondary structures above.

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 want to analyse the disordered regions of our protein (P00439) as well as of 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>

Before using this tool for the prediction, we had to compile 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.

For the graphical output, one can use the IUPred webserver. Since there can only accessed short and long disorder separately, we wrote an R-script, where one can plot both disorder forms into one graphic. The resulting images as well as the images for the globular domains of the proteins are shown below:

Long and short disorder comparison of protein P00439 (PAH)	Globular domain of protein P00439 (PAH)
Long and short disorder comparison of protein P10775 (RNH1)	Globular domain of protein P10775 (RNH1)
Long and short disorder comparison of protein Q9X0E6 (CUTA)	Globular domain of protein Q9X0E6 (CUTA)
Long and short disorder comparison of protein Q08209 (PP2BA)	Globular domain of protein Q08209 (PP2BA)

</figtable>

...

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

...

MetaDisorder P00439 (PAH)	P10775 (RNH1)
Q9X0E6 (CUTA)	Q08209 (PP2BA)

</figtable>

...

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.

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.

</figtable>

As we were not able to find exact matches here, we can not compare the results to the ones we got from IUPred and MetaDisorder. Q08209 is the only protein with an exact match, where five different ordered regions were found. This result does overlap with the one we got from the IUPred prediction, as one can see in the lowest figure in <xr id="iupred"/>. Here, the ordered regions are located on the beginning and the end of the sequence. The same can be said about the MetaDisorder prediction. The only tool, which does not predict the disordered region as well is PROFbval.

A list of different disorder prediction methods with short definition can be found on this wiki page or on the paper "A Practical Overview of Protein Disorder Prediction Methods". Some example tools are:

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:

P00439 (PAH)
P35462 (DRD3)
Q9YDF8 (KVAP)
P47863 (AQP4)

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>

Before using PolyPhobius, we had to do some steps:

We generated a fasta file with all homologous sequences to the query sequence inside. Therefore, we used the blastget perl script and the swissprot database as followed:

 /mnt/project/pracstrucfunc13/polyphobius/blastget -db /mnt/project/pracstrucfunc13/data/swissprot/uniprot_sprot 
 -ix /mnt/project/pracstrucfunc13/data/index_pp/uniprot_sprot.idx sequence.fasta > sequence-blast.fasta

Afterwards, we used kalign for the MSA generation as shown here:

 /mnt/opt/T-Coffee/bin/kalign -i sequence-blast.fasta -o sequence-kalign.fasta -f fasta

Now, we can run PolyPhobius with following command:

 /mnt/project/pracstrucfunc13/polyphobius/jphobius -poly sequence-kalign.fasta > sequence-polyphobius.txt

For the prediction as well as a graphical output one can use the PolyPhobius webserver as well. The graphical outputs for the four proteins on the webserver are shown in the <xr id="polyphobius"/> below.

PolyPhobius webserver result for P00439 (PAH)	PolyPhobius webserver result for P35462 (DRD3)
PolyPhobius webserver result for Q9YDF8 (KVAP)	PolyPhobius webserver result for P47863 (AQP4)

Graphical results from the Polyphobius webserver for the proteins P00439 (PAH), P35462 (DRD3), Q9YDF8 (KVAP) and P47863 (AQP4). The grey regions show Transmembrane (TM) helices. </figtable>

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.

Transmembrane helices result from the MEMSAT-SVM webserver for P00439 (PAH)	Transmembrane helices result from the MEMSAT-SVM webserver for P35462 (DRD3)
Transmembrane helices result from the MEMSAT-SVM webserver for Q9YDF8 (KVAP)	Transmembrane helices result from the MEMSAT-SVM webserver for P47863 (AQP4)

Graphical results from the MEMSAT-SVM webserver for the proteins P00439 (PAH), P35462 (DRD3), Q9YDF8 (KVAP) and P47863 (AQP4). The yellow squares display Transmembrane (TM) helices. </figtable>

OPM

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 results can be found on the following links:

We could not find any entry for the protein P00439 (PAH)
3pbl for the protein P35462 (DRD3)
[ ] for the protein Q9YDF8 (KVAP)
[ ] for the protein P47863 (AQP4)

PDBTM

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>

The results can be found on the sites below:

We could not find any entry for the protein P00439 (PAH)
3pbl for the protein P35462 (DRD3)
[ ] for the protein Q9YDF8 (KVAP)
[ ] for the protein P47863 (AQP4)

Results and Discussion

In the <xr id="TM_PAH"/> one can see, that our protein P00439 (PAH) does not include any transmembrane helix in its structure. Only MEMSAT-SVM predicts one at the position of 287-302. On the PolyPhobius graphical output in <xr id="polyphobius"/>, one can see a slight curve on this part of the protein, too. But it is not very intensive (probability under 0.1), so most of the prediction methods will not predict any transmembrane region here. <figtable id="TM_PAH">

"Transmembrane(TM) helices of protein P00439 (PAH)"
TM	PolyPhobius	MEMSAT-SVM	OPM	PDBTM	Uniprot
1	-	287-302	-	-	-

    
     Number and position of the predicted transmembrane helices of PolyPhobius, MEMSAT-SVM, OPM and PDBTM for the protein P00439 (PAH).

</figtable>

Since our protein does not have any transmembrane helix, we will research the other proteins in more detail.

First, we want to look at the results of the protein P35462 (DRD3) shown in <xr id="TM_P35462"/>. Here, all methods except for MEMSAT-SVM predict seven TM helices. The positions of the TM helices change only slightly for the different prediction methods, but they are lying always in the same range. ...

"Transmembrane(TM) helices of protein P35462 (DRD3)"
TM	PolyPhobius	MEMSAT-SVM	OPM	PDBTM	Uniprot
1	30-55	32-55	34-52	35-52	33–55
2	66-88	65-88	67-91	68-84	66–88
3	105-126	101-129	101-126	109-123	105–126
4	150-170	151-169	150-170	152-166	150–170
5	188-212	188-209	187-209	191-206	188–212
6	329-352	331-354	330-351	334-347	330–351
7	367-386	-	363-386	368-382	367–388

    
     Number and position of the predicted transmembrane helices of PolyPhobius, MEMSAT-SVM, OPM and PDBTM for the protein P35462 (DRD3).

</figtable>

For the protein Q9YDF8 (KVAP) it is almost the same, but here UniProt and MEMSAT-SVM do only predict six TM helices. ... (which pdb entry and why?)

"Transmembrane(TM) helices of protein Q9YDF8 (KVAP)"
TM	PolyPhobius	MEMSAT-SVM	OPM	PDBTM	Uniprot
1	42-60	43-59	x	x	39–63
2	68-88	72-90	x	x	68–92
3	108-129	101-118	x	x	109–125
4	137-157	128-143	x	x	129-145
5	163-184	163-184	x	x	160-184
6	196-213	-	x	x	-
7	224-244	221-245	x	x	222-253

    
     Number and position of the predicted transmembrane helices of PolyPhobius, MEMSAT-SVM, OPM and PDBTM for the protein Q9YDF8 (KVAP).

</figtable>

In the last of the four proteins, called P47863 (AQP4), are six TM helices predicted in every prediction tool. Here, we got the same problem as above... (which pdb entry and why?)

"Transmembrane(TM) helices of protein P47863 (AQP4)"
TM	PolyPhobius	MEMSAT-SVM	OPM	PDBTM	Uniprot
1	34-58	35-56	x	x	37–57
2	70-91	71-89	x	x	65–85
3	115-136	113-136	x	x	116-136
4	156-177	157-178	x	x	156-176
5	188-208	190-205	x	x	185-205
6	231-252	232-252	x	x	232-252

    
     Number and position of the predicted transmembrane helices of PolyPhobius, MEMSAT-SVM, OPM and PDBTM for the protein P47863 (AQP4).

</figtable>

If we compare all results, we can say, that all used methods predict the positions very close to each other and it is very difficult to decide which method would be the best for transmembrane helices prediction.

A list with different prediction methods for transmembrane helices is shown here. Some examples will be:

and there can be found many more!

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.

UniProt		SignalP 3.0				SignalP 4.1	SignalP (website)
ID	description	SignalP-NN	SignalP-HMM	Max cleavage site	prediction	prediction	prediction
P00439	PAH (homo sapiena)	5 x NO	0.0 signal peptide, 0.0 signal anchor	0.000	non-secretory protein	no signal peptide	not in database
P02768	ALBU (homo sapiens)	5 x YES	1.0 signal peptide, 0.0 signal anchor	0.785	signal peptide	signal peptide	confirmed [1]
P11279	LAMP1 (homo sapiens)	5 x YES	1.0 signal peptide, 0.0 signal anchor	0.847	signal peptide	signal peptide	confirmed [2]
P47863	AQP4 (rattus norvegicus)	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"/>.

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 (Pfam output). Both domains are found with high posterior probability and therefore are correctly designated as significant. Furthermore for the ACT family the clan CL0070 is named. The e-value for ACT is 3.7e-08 and even 1.8e-192 for Biopterin_H. None active site is predicted for both domains. We also tried including Pfam-B families, but none were found.

Another Protein familiy search tool is DomainSweep, which scannes protein family databases. For our protein sequence it predicts aromatic amino acid hydroxylase, phenylalanine-4-hydroxylase and the ACT domain as significant DomainSweep output. So it seems to give correct predictions. In the output of DomainSweep all results of the different databases (BLOCKS, CDD, CATH, PFAMA, PRINTS, PRODOM, PROSITE, SMART, SUPERFAMILY, TIGRFAMS) can be viewed.

References

Sequence-based predictions (Phenylketonuria)

Contents

Summary

Secondary structure

P10775 (RNH1)

ReProf comparison (Q9X0E6, Q08209, P00439)

P00439 sequences

Disorder

IUPred

MD (MetaDisorder)

DisProt

Transmembrane helices

PolyPhobius

MEMSAT-SVM

OPM

PDBTM

Results and Discussion

Signal peptides

GO terms

GOPET

ProtFun

Pfam

References

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