Sequence and multiple alignments

Sequence Alignments

Database search

Identity distribution of blast, fasta and psiblast

score distribution of fasta

score distribution of blast and psiblast

We used different tools for the search in a non-reduntant(NR) database like BLAST, FASTA and PSI-BLAST. Because of the absence of the database in a hmm format, we decided not to integrate the hhpred web-results. These results are not comparable.

We called BLAST and FASTA with the standard parameters. blast -p blastp -i 1a6z.fasta -d /data/nr/nr -o blastp_1a6z_on_NR.txt runtime 5:34
../../Desktop/fasta-36.3.4/bin/fasta36 -q 1a6z.fasta /data/nr/nr >fasta_1a6z_on_nr.txt the runtime was 10:44.

PSI-BLAST was used with different parameter settings. blastpgp -i 1a6z.fasta -d /data/nr/nr runtime 21:40
blastpgp -i 1a6z.fasta -d /data/nr/nr -j 6 -e 10E-6 > psiblast_on_NR_itera3_e-val_10E-6.txt runtime 23:49
blastpgp -i 1a6z.fasta -d /data/nr/nr -j 3 -e 10E-6 > psiblast_on_NR_itera6_e-val_10E-6.txt runtime 15:08
blastpgp -i 1a6z.fasta -d /data/nr/nr -j 3 > psiblast_on_NR_itera3.txt runtime 9:41
blastpgp -i 1a6z.fasta -d /data/nr/nr -j 6 > psiblast_on_NR_itera6.txt runtime 23:30
We also compared the runtime of the different tools. Surprisingly for PSI-BLAST, in our case, the e-Value and the number of iterations had no impact on the results, so we used PSI-BLAST with standard parameters for the comparison.

As we looked for an overlap between the reference sequences given by HSSP, no overlap occured. The HSSP reference is given with UniProt AC, so we mapped the ID's on the GenBank ID's using PIR<ref>pir.georgetown.edu/pirwww/search/idmapping.shtml</ref>. Than we compared the ID's without any result. We also mapped the PDB ID's found by FASTA to UniProt. Here we found a small overlap of about 34 Proteins. But the issue is, that the highest identity given by a pdb entry is abput 40%.

overlap between all methods

overlap between fasta blast and psiblast

overlap with the reference id's

overlap with the mapped pdb id's

Multiple Alignments

Used Sequences

SeqIdentifier	Seq Identity	Protein description
99-90% Sequence Identity
AAG29574.1	94%	!EXCLUDED hemochromatosis splice variant
89-60% Sequence Identity
AAO47091.1	87%	hemochromatosis
NP_620576.1	86%	hereditary hemochromatosis protein isoform 7 precursor
NP_620577.1	86%	hereditary hemochromatosis protein isoform 8 precursor
AAH74721.1	84%	HFE protein
NP_620578.1	78%	hereditary hemochromatosis protein isoform 9 precursor
AAF01222.1	75%	!EXCLUDED hereditary haemochromatosis protein precursor
Q9GL42.1	68%	HFE_DICSU RecName
Q9GL43.1	68%	HFE_DICBI RecName
NP_001166905.1	63%	hereditary hemochromatosis protein homolog isoform 2 precursor
EDL86571.1	62%	hemochromatosis, isoform CRA_c
P70387.1	61%	HFE_MOUSE
59-40% Sequence Identity
XP_001511789.1	41%	PREDICTED: similar to MHC class I heavy chain antigen
XP_002713938.1	40%	PREDICTED: histocompatibility 2, Q region locus 10-like
39-20% Sequence Identity
AAR25266.1	37%	MHC class I heavy chain
XP_585880.4	37%	PREDICTED: histocompatibility 2, Q region locus 10-like
AAX51393.1	36%	MHC class I antigen alpha chain precursor
BAD23967.1	35.7%	MHC class I antigen
AAZ30022.1	34%	MHC class I antigen alpha chain

Because our first results were really bad, we are given the advice to include/exclude some of our sequences. We decided to exclude the two sequences, that cause the most gaps. Both had around ~120 aminoacids, so they caused the insertion of long strands of gaps that lead to bad conservated regions.

Excluded sequences from all MSAs:

• tr|Q9HC71|Q9HC71_HUMAN (AAG29574.1 / 105 aa)
• gi|6010711|gb|AAF01222.1|AF184234_1 (129 aa)

Thus we have no more sequences in the region of 99 to 90% sequenced identity, but the one we had was only a fragment and therefore only partial alignmentable. Without these sequences our alignments and other analysis should get quite better.

Programs

All programs except cobalt were preinstalled at our VM. Cobalt was downloaded from ftp://ftp.ncbi.nlm.nih.gov/pub/cobalt/executables/2.0.1/ (file depending on system architecture) and extracted. Afterwards the current directory was changed to ~/usr/bin and a symbolic link was created (ln -s <extraced-cobalt-runnable> cobalt).

Cobalt

msa using cobalt

call:

• cobalt -i hfe_fasta_multi.txt -norps T -outfmt clustalw > cobalt.txt

switches:

• -norps T: use no downloaded database
• -outfmt clustalw: use output formatting of clustalw

time:

• 0,55s user
• 0,02s system

ClustalW

msa using clustalW

call:

• clustalw hfe_fasta_multi.txt

time:

• 1,32s user
• 0,04s system

Muscle

msa using muscle

call:

• muscle -in hfe_fasta_multi.txt -clw -out muscle.txt

switches:

• -clw: use output formatting of clustalw
• -out muscle.txt: write output to muscle.txt

time:

• 1,25s user
• 0,00s system

T-Coffee

standard

msa using tcoffee (standard)

call:

• t_coffee hfe_fasta_multi.txt

time:

• 11,00s user
• 0,18s system

3dcoffee/expresso

Unfortunately were we not able to run the 3dcoffee, because it crashed everytime. Therefore we used -mode expresso.

msa using expresso mode of t-coffee

call:

• t_coffee hfe_fasta_multi.txt -mode expresso

switches:

• -mode expresso: use special mode with 3d structure supported alignment

time:

• 4m22s user
• 0,11s system

Alignments

All programs worked fine and produced a good aligned part from the mid to the end (position ~170 to 390). The use of the structure supported alignment at t-coffee resulted in an extremely gapped first part of the alignment. This is probably caused by non similar structures of our input sequences and the need to incorporate the structure information in the alignment. That can only be done by inserting many gaps as placeholders for region of no or very less structural similarity (which was necessary, because our best pdb entry was at ~40% sequence identity). This problem affects also the following sections, like the amount and quality of conserved regions or the amount of introduced gaps.

All programs except the special mode of t-coffee have an clearly obvious gap between ~160 and ~170. That gap is caused by insertions at the hfe protein in the rat and mouse.

Residues

But in order to present a visible result, we used the conserved region calculation of JalView. The values ranges from 0 (no conservation at all) to 11 (full conserved row).

name	0 (bad)	>=5	>=8	11 (very good)
cobalt	~100	~2	~0	~0
clustalW	~113	~12	~3	~1
muscle	~120	~7	~3	~1
t-coffee (std)	~127	~3	~0	~0
t-coffee (expresso)	~130	~3	~0	~0

It is clearly visible that our alignments have almost no conserved rows, except two hits at clustalW and cobalt alignments. We still do not know the reason for that but we think, it derives from our input sequences and their length and sequence identity difference. Therefore the non-conserved rows are about .12% to .25% length of our complete msas, which is a too high fraction.

Functional Important

We used the information available at UniProt for determining functional important residues and regions.

key	positions	length	description	cobalt	clustalW	muscle	t-coffee (std)	t-coffee (3d)
Glycosylation	110	1	N-linked (GlcNAc...) Potential	yes - c2	yes - c1	yes - c2	yes - c2	yes - c2
Glycosylation	130	1	N-linked (GlcNAc...) Potential	no - c0	yes - c4	yes - c4	no - c0	yes - c4
Glycosylation	234	1	N-linked (GlcNAc...) Potential	yes - c1	yes - c1	yes - c1	yes - c1	no - c0
Disulfide bond	124 ↔ 187	1
Disulfide bond	225 ↔ 282	1

We excluded all domain and other regions, because they are of no use this time. The only important functional positions are the position of the glycosylation and disulfide bond.

We are using the conserved region index from JalView again. One can see, that the conservation of the glycosylation is only adequate at position 130 with clustalW, muscle and t-coffee (3d). Their alignments achieve an index of 4, that is not good but somekind conserved. All other positions are less or equal an conversation index of 2, which is almost no conservation. The both position of the disulfid bond

Gaps

Counting and analyzing the gaps is quite useless in this actual view because all results will be redone and corrected.