Difference between revisions of "Sequence Search and Multiple Sequence Alignment (PKU)"

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Our favorite tools for this task was probably T-Coffee, due to good results and the wide range of option. Clustal produces less gaps, but misaligned at least one sequence. Muscle does not perform much different from T-Coffee, but provides less functionality. The advantage of muscle, its emphasis on speed, is, as stated before, not taken into account here.
  
Our favorite tools for this task was probably T-Coffee, due to good results and the wide range of option. Clustal produces less gaps, but misaligned at least one sequence. Muscle does not perform much different from T-Coffee, but provides less functionality. The advantage of muscle, its emphasis on speed, is, as stated before, not taken into account here.
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[[Category: Phenylketonuria 2012]]

Latest revision as of 11:47, 29 August 2012

Short Task Description

The goal of this task is to learn a bit about our protein of interest (phenylalanine hydroxylase/PheOH) from existing databases and comparing it to similar (and probably related) sequences. To do this, we searched the big80 database (7.73 mio sequences), which contains a subset of swissprot and pdb with sequence similarity of 80% or less, with blast (sequence comparison) and psi-blast (profile comparison) and searched a preprocessed and clustered version of uniprot (44,757 clusters) with the more advanced HHblits method (HMM comparison).

From the search results, we created several datasets sorted by sequence similarity (highly conserved, less conserved, a little conserved, ...) and created multiple aligmnents from these sets with four different tools. The results are listed below, the commands, programs and scripts we used are listed at the appropriate places in the text or linked to their own site.

Reference Sequence of PAH

This is the sequence of the fully functional PheOH used as query and reference sequence in all our work. 

>sp|P00439|PH4H_HUMAN Phenylalanine-4-hydroxylase OS=Homo sapiens GN=PAH PE=1 SV=1
MSTAVLENPGLGRKLSDFGQETSYIEDNCNQNGAISLIFSLKEEVGALAKVLRLFEENDV
NLTHIESRPSRLKKDEYEFFTHLDKRSLPALTNIIKILRHDIGATVHELSRDKKKDTVPW
FPRTIQELDRFANQILSYGAELDADHPGFKDPVYRARRKQFADIAYNYRHGQPIPRVEYM
EEEKKTWGTVFKTLKSLYKTHACYEYNHIFPLLEKYCGFHEDNIPQLEDVSQFLQTCTGF
RLRPVAGLLSSRDFLGGLAFRVFHCTQYIRHGSKPMYTPEPDICHELLGHVPLFSDRSFA
QFSQEIGLASLGAPDEYIEKLATIYWFTVEFGLCKQGDSIKAYGAGLLSSFGELQYCLSE
KPKLLPLELEKTAIQNYTVTEFQPLYYVAESFNDAKEKVRNFAATIPRPFSVRYDPYTQR
IEVLDNTQQLKILADSINSEIGILCSALQKI

Database Searches

To increase productivity and reproducibility we used a variety of scripts here. Those which are not just basic R or bashprograms will be listed here 

getEvalues.pl to derive the eValues of all Blast, PSIBlast or HHBlits outputfiles
HHextract.py to extract all UNiprotIds from the clusters of a HHblits output

Blast

time blast2 -p blastp -d /mnt/project/pracstrucfunc12/data/big/big_80 -i Dropbox/Phenylketonuria/Task2/PAH.fasta -o results_blast2_standard -v 2000 -b 2000

real	1m34.784s
user	1m22.930s
sys	0m8.400s

time blast2 -p blastp -d /mnt/project/pracstrucfunc12/data/big/big_80 -i Dropbox/Phenylketonuria/Task2/PAH.fasta -o results_blast2_e-10 -e 0.0000000001 -v 2000 -b 2000

real	1m34.145s
user	1m23.380s
sys	0m9.270s

<figtable id="hhblitsevalue">

Histogram of the logarithmic E-values standard BLAST run
Histogram of the sequenceidentities for a standard BLAST run

</figtable>

PSIBlast

In order to display the runtime PSIBlast uses in order to get results you asked for we used a little skript which uses PSIblast with a variety of parameter settings. The overall message is that the more iterations one uses the more run-time PSIBlast needs. And the harder (lower) you choose your e-Value cutoff the less results you get. A general Message cant be more precise, because all the results depend mostly on the data you feed the programm.

Iterations

If you change the number of iterations (parameter -j) your runtime increases according to the parameter. To show this we used the default settings for anything and increased the iterations from 1 to 10 (see <xr id="fig:runtimeIterations"/>)

<figure id="fig:runtimeIterations">

more iterations result in a higher runtime, but a change in -e or -h do not mean a higher or lower runtime by themselves. A combination of more iterations and a lower integrations rate might be faster as less iterations though

</figure>


<figure id="fig:overlap_psiblast_iterations">

Overlap of PSIBlast (with 1, 3, 5, 7 and 10 iterations) search results in big80

</figure>

<xr id="fig:overlap_psiblast_iterations"/> shows the overlap of five PSIBlast searches with different number of iterations. It seems that more than five iterations scarcely result in more hits and of the nine proteins added from 7 to 10 iterations, a high number (33%) are again uncharacterized proteins.

e-Value cutoff

A change in the -e parameter does not change your runtime (see <xr id=fig:runtimeIterations />) but in- or decreases the amount of sequences you derive from your search. This is shown in <xr id="fig:cutoffSize"/> <figure id="fig:cutoffSize">

Change in the -e parameter results in a lower resultsize

</figure>

Plots

All of the following plots emphasize for our example how a change of parameter changes the outcome of the searchtool.

e-Values

<figtable id="psiblasteValue">

log e-Values with almost no restriction
log e-Values with more restriction
log e-Values with strong restriction

</figtable>


<xr id="psiblasteValue" /> shows that the distribution of the e-Values one derives from their search changes if you restrict them. Which also decreases the size of your result as shown above. <figtable id="psiblasteValueit">

log e-Values after one iterations
log e-Values after five iterations
log e-Values after ten iterations

</figtable> In <xr id="psiblasteValue" /> one can clearly see that further iterations of the algorithm add some low e-Value sequences but the majority of the profile is getting fuzzy because the number of sequences with a e-Value around 0 increases the most.

identities

<figtable id="psiblastidentity">

identities of sequences with a low restriction to the e-Values
identities of sequences with a moderate restriction to the e-Values
identities of sequences with a high restriction to the e-Values

</figtable>

<xr id="psiblastidentity"/> shows that the identity of the sequences peak at about 30% with restriction to lower e-Values one removes not only low-identity-sequences, but also some with higher identity.


<figtable id="psiblastidentityit">

identities of sequences with a low restriction to the e-Values
identities of sequences with a moderate restriction to the e-Values
identities of sequences with a high restriction to the e-Values

</figtable> One can clearly see in <xr id="psiblastidentityit"/> that the distribution of identities shifts its peak from 0.4 to about 0.25 by adding more and more sequences

HHBlits

time hhblits -i Dropbox/Phenylketonuria/Task2/PAH.fasta -d /mnt/project/pracstrucfunc12/data/hhblits/uniprot20_current -o results_hhblits_standard  -E 1 -e 0.001 -Z 2000 -B 2000

real	6m10.059s
user	3m15.640s
sys	0m40.220s

hhblits -i Dropbox/Phenylketonuria/Task1/PAH.fasta -d /mnt/project/pracstrucfunc12/data/hhblits/uniprot20_current -o results_hhblits_n4_e-7 -n 4 -e 0.0000001 -o results_hhblits_n4_e-7

real	8m32.287s
user	4m40.113s
sys	1m58.910s

results

<figtable id="hhblitsevalue">

Histogram of the logarithmic E-values of the HHBlits hits with standard parameter
Histogram of the logarithmic E-values of the HHBlits hits with restricted parameter

</figtable>


<figtable id="hhblitsident">

Histogram of the identities in percent of a standard HHBlits run
Histogram of the identities in percent HHBlits run with restricted parameters

</figtable>



<figure id="fig:overlap_hhblits_iterations">

Overlap of HHBlits (with 4 (blue) and 2 (red) iterations) search results in big80

</figure>

<xr id="fig:overlap_hhblits_iterations"/> shows the overlap between the results of different searches with HHBlits with two and four iterations respectively. The large number of unique sequences simply comes from two large clusters and one smaller cluster that are found and lost respectively when the number of iterations is doubled.

Comparison of the Searchtools

<figure id="fig:overlap_blast_psiblast">

Overlap of PSIBlast (with 1, 5 and 10 iterations from left to right) and Blast (rightmost) search results in big80

</figure>

Figure <xr id="fig:overlap_blast_psiblast"/> shows the overlap between Blast and PSIBlast with different numbers of iterations. PSIBlast finds as expected much more results at the same default E-value. Much more important is the quality of the results of PSIBlast. A look in the results shows indeed a high number of uncharacterized proteins but still dehydratases and hydroxlyases with annotations similar to our PheOH.

For HHBlits we can compare the results to the other tools indirectly: (PSI)Blast searched in the 'clustered' big_80, which yielded only sequences from Uniprot during our searches, HHBlits searched in a clustered Uniprot release. Therefore we simply check, which clusters of HHBlits were also hit by (PSI)Blast, assuming that the clusters of HHBlits and the representatives in big_80 cover a similar spectrum of sequences.

HHBlits with standard parameters finds 242 clusters of sequences significantly (with E-Value<0.01) similar to PheOH. Only 120 clusters contain sequences also found with Blast with standard parameters. Depending on the number of iterations of PSIBlast, only 86 (with 1 iteration) to 94 (with 10 iterations) clusters found with HHBlits contain sequences found with PSIBlast. From this, we conclude that HHBlits searches much more sensitive than the traditional Blast algorithm, but with considerable costs in execution time.

GOTerms

After analyzing the output of each of those tools through standard numeric methods we wanted to see if one can see a quality difference with something more concrete than e-Values and identities. So we used GOAnnotations. In order to compare the results from HHBlits with BLAST and PSIBlast we downloaded the annotations for each hit and compared them with the annotations of the reference sequence. We used the RESTful webservice of http://www.ebi.ac.uk/
Here you can get the .jar containing source and class files of the program.
Our reference sequence has the following annotated GO:Terms


<figtable id="goidentity">

GoTerm identity comparsion between BLAST PSIBLAST and HHBlits
<figure id="fig:identityblast">
a) Histogramm of the Gotermidentity gained from BLAST hits
</figure>
b) Histogramm of the Gotermidentity gained from PSIBLAST hits
c) Histogramm of the Gotermidentity gained from HHBlits hits

</figtable> In <xr id="goidentity"/> one can see the performance increase from pure BLAST to PSIBlast but due to the increased hitcount on HHBLits with its clusters one can barely compare a result where single sequences are checked versus a cluster to a pairwise check. This check seems to be a further step in order to compare results from those three search tools.
In conclusion I would prefer PSIBLast due to its rather easy usage. But a distinct "best" search tool could not be found for our data. It still depends on rather subjective measurements, than real objective proof.

PDB

Instead of mapping hits in big_80 to PDB to get structural information for the alignments, we performed an additional search in PDB at NCBI, with parameters equivalent to our blast search in big_80: The scoring matrix used is Blosum62, gap open = 10, gap extend = 1 with composition based statistics. The results yielded no hits in the range of 80%-99% identity but still helped to complete our datasets as shown below.

MSA

Datasets

We tried to create datasets of 4 sequences + reference sequence for the identity ranges (1: 90%-99%, 2: 60%-89%, 3: 40%-59%, 4: 20%-39%) from our search in the big_80 database. Since these results do not contain PDB structures, we additionally searched PDB directly for proteins in the required range and added them to the dataset. For the most conserved range, there is only 1 sequence in big_80, so we experimentally created the best possible highly conserved dataset in the range 80%-90% and used the structure of the reference sequence for 3D-coffee. The resulting datasets are shown in the following tables, the alignments in the figures <xr id="fig:ClustalW_set4"/> to <xr id="fig:3d_coffee_set1"/>. All sequences have roughly the same length as the reference sequence except for G5AMD7 in the first set. G5AMD7 contains an insertion of 162 aa, that is easily identified in the alignment, but shows a very high similarity in the other sections.

80-90% Sequence Identity
Sequence Identity ID Comment
90% G1P4I7 Uncharacterized protein OS=Myotis lucifugus
89% G5AMD7 Phenylalanine-4-hydroxylase OS=Heterocephalus glaber
80% G1KSL1 Uncharacterized protein OS=Anolis carolinensis
100% 1PAH used as 3D-Template only
60-89% Sequence Identity
Sequence Identity ID Comment
80% G1KSL1 Uncharacterized protein OS=Anolis carolinensis
76% Q4VBE2 Putative uncharacterized protein mgc108157 OS=Xenopus tropicalis
70% H2UJM8 Uncharacterized protein OS=Takifugu rubripes
63% D1LXB2 Phenylalanine hydroxlase OS=Saccoglossus kowalevskii
61% 2XSN_A human Tyrosine 3-Monooxygenase, also used as 3D-Template
40-59% Sequence Identity
Sequence Identity ID Comment
58% O96947 Phenylalanine hydroxylase OS=Geodia cydonium
55% D3BKZ8 Phenylalanine 4-monooxygenase OS=Polysphondylium pallidum
49% Q5RHI3 Novel protein similar to tyrosine hydroxylase OS=Danio rerio
44% A6P4D3 Tyrosine hydroxylase OS=Dugesia japonica
59% 1TOH_A Tyrosine hydroxylase from rattus norvegicus, also used as 3D-Template
20-39% Sequence Identity
Sequence Identity ID Comment
37% F4WGX3 Phenylalanine-4-hydroxylase OS=Acromyrmex echinatior
35% Q23A76 Biopterin-dependent aromatic amino acid hydroxylase family protein OS=Tetrahymena thermophila
29% D0I5S9 Phenylalanine-4-hydroxylase OS=Grimontia hollisae
28% F6G5X4 Phenylalanine-4-hydroxylase OS=Ralstonia solanacearum
31% 1LTU_A Phenylalanine-4-hydroxylase from Chromobacterium violaceum, also used as 3D-Template

Raw Results

We present the alignments and basic statistics for the different tools. Images have been created in Jalview. To count gaps and conserved columns a simple python script (Collection of scripts) was used.

ClustalW

Command used to create the alignments: 

clustalw -align -infile=NN.fasta -outfile=clustalW_NN.aln

<figure id="fig:ClustalW_set4">

Alignment of set 4 created with ClustalW

</figure>

20-39% Sequence Identity
Sequence ID Gaps
P00439 83
Q23A76 87
D0I5S9 272
F4WGX3 69
F6G5X4 181
1LTU_A 238
Conserved columns 19

<figure id="fig:ClustalW_set3">

Alignment of set 3 created with ClustalW

</figure>

40-59% Sequence Identity
Sequence ID Gaps
P00439 37
A6P4D3 1
Q5RHI3 14
D3BKZ8 45
O96947 39
1TOH_A 146
Conserved columns 109


<figure id="fig:ClustalW_set2">

Alignment of set 2 created with ClustalW

</figure>

60-89% Sequence Identity
Sequence ID Gaps
P00439 17
H2UJM8 7
Q4VBE2 24
G1KSL1 16
D1LXB2 18
2XSN_A 126
Conserved columns 159

<figure id="fig:ClustalW_set1">

Alignment of set 1 created with ClustalW

</figure>

80-90% Sequence Identity
Sequence ID Gaps
P00439 165
G1P4I7 162
G1KSL1 164
G5AMD7 4
Conserved columns 329

Muscle

Command used to create the alignments: 

muscle -in NN.fasta -out muscle_NN.fasta


<figure id="fig:Muscle_set4">

Alignment of set 4 created with Muscle

</figure>

20-39% Sequence Identity
Sequence ID Gaps
P00439 94
Q23A76 98
D0I5S9 283
F4WGX3 80
F6G5X4 192
1LTU_A 249
Conserved columns 22


<figure id="fig:Muscle_set3">

Alignment of set 3 created with Muscle

</figure>

40-59% Sequence Identity
Sequence ID Gaps
P00439 45
A6P4D3 9
Q5RHI3 22
D3BKZ8 53
O96947 47
1TOH_A 154
Conserved columns 109

<figure id="fig:Muscle_set2">

Alignment of set 2 created with Muscle

</figure>

60-89% Sequence Identity
Sequence ID Gaps
P00439 17
H2UJM8 7
Q4VBE2 24
G1KSL1 16
D1LXB2 18
2XSN_A 126
Conserved columns 159

<figure id="fig:Muscle_set1">

Alignment of set 1 created with Muscle

</figure>

80-90% Sequence Identity
Sequence ID Gaps
P00439 167
G1P4I7 164
G1KSL1 166
G5AMD7 6
Conserved columns 328

T-coffee

Command used to create the alignments: 

t_coffee NN.fasta


<figure id="fig:t_coffee_set4">

Alignment of set 4 created with T-Coffee

</figure>

20-39% Sequence Identity
Sequence ID Gaps
P00439 103
Q23A76 107
D0I5S9 292
F4WGX3 89
F6G5X4 201
1LTU_A 258
Conserved columns 22

<figure id="fig:t_coffee_set3">

Alignment of set 3 created with T-Coffee

</figure>

40-59% Sequence Identity
Sequence ID Gaps
P00439 53
A6P4D3 17
Q5RHI3 30
D3BKZ8 61
O96947 55
1TOH_A 162
Conserved columns 109

<figure id="fig:t_coffee_set2">

Alignment of set 2 created with T-Coffee

</figure>

60-89% Sequence Identity
Sequence ID Gaps
P00439 18
H2UJM8 8
Q4VBE2 25
G1KSL1 17
D1LXB2 19
2XSN_A 127
Conserved columns 159

<figure id="fig:t_coffee_set1">

Alignment of set 1 created with T-Coffee

</figure>

80-90% Sequence Identity
Sequence ID Gaps
P00439 165
G1P4I7 162
G1KSL1 164
G5AMD7 4
Conserved columns 329

3D-coffee

Command to create the alignments: 

t_coffee NN.fasta -method sap_pair,slow_pair -template_file EXPRESSO


<figure id="fig:3d_coffee_set4">

Alignment of set 4 created with 3D-Coffee

</figure>

20-39% Sequence Identity
Sequence ID Gaps
P00439 96
Q23A76 100
D0I5S9 285
F4WGX3 82
F6G5X4 194
1LTU_A 251
Conserved columns 22

<figure id="fig:3d_coffee_set3">

Alignment of set 3 created with 3D-Coffee

</figure>

40-59% Sequence Identity
Sequence ID Gaps
P00439 51
A6P4D3 15
Q5RHI3 28
D3BKZ8 59
O96947 53
1TOH_A 160
Conserved columns 109

<figure id="fig:3d_coffee_set2">

Alignment of set 2 created with 3D-Coffee

</figure>

60-89% Sequence Identity
Sequence ID Gaps
P00439 21
H2UJM8 11
Q4VBE2 28
G1KSL1 20
D1LXB2 22
2XSN_A 130
Conserved columns 159

<figure id="fig:3d_coffee_set1">

Alignment of set 1 created with 3D-Coffee

</figure>

80-90% Sequence Identity
Sequence ID Gaps
P00439 165
G1P4I7 162
G1KSL1 164
G5AMD7 4
Conserved columns 329

Discussion of the alignment results and alignment tools

Looking at the used tools, for these small datasets (4-6 sequences) there is not much of a difference in execution time, so we do not use time as a factor in our assessment. Muscle, ClustalW and T-Coffee are similar and easy to use, but T-Coffee offers the most options and different modes (we used only two here) and provides the most detailed output. To assess the alignments, we looked also at the number of gaps and conserved residues, but consider the most important factor in alignment quality the proper alignment of the functional sequence parts.


Comparing the results, the tools also perform quite similar, but can be sorted by the basic statistics of introduced gaps and found conserved columns in the following order: ClustalW> Muscle>3D-Coffee>T-Coffee.

T-Coffee using the additional 3D information performs minimally better in terms of gaps, more notably in the less conserved datasets. An interesting point that stands out might be that 3D-Coffe always places the first residues -- mostly methionines -- together, even if for a shorter sequence missing e.g. an N-terminal part this means a very long gap between this first residue and the rest of the sequence (see figure <xr id="fig:3d_coffee_set2"/> for an example). This behaviour does not influence the alignment score much, might even be a 'better' alignment as the first methionines belong together, but the single residue still feels a little misplaced to us.


In the most conserved dataset the aligments differ only marginally and due to the high overall conservation, the functional domains are obviously aligned correctly as well. For the other sets of sequences, we looked more closely at the central catalytic and the N-terminal regulatory domain:

The catalytic domain of PheOH appears around 280aa-340aa, with the iron-coordinating His285, His290 and Glu330. This part of the sequence is recognized by all tools in all datasets and the coordinating residues are aligned correctly. In all but the least conserved sets, the alignments are virtually identical in the functional parts and differ only in the placement of gaps. The most differences appear at the N-terminus and likely stem from the different lengths of the sequences that have to be reconciled in the alignment.

A real difference in the results of the tools can be seen near the N-terminus of set 4. T-Coffe, 3D-Coffee and Muscle find a conserved region around Trp187, while ClustalW misaligns D0I5S9, which is the shortest sequence of the set. Look at positions 190-210 of the alignment by ClustalW and 210-230 by the others.

Our favorite tools for this task was probably T-Coffee, due to good results and the wide range of option. Clustal produces less gaps, but misaligned at least one sequence. Muscle does not perform much different from T-Coffee, but provides less functionality. The advantage of muscle, its emphasis on speed, is, as stated before, not taken into account here.

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