Gaucher Disease: Task 02 - Alignments

From Bioinformatikpedia
Revision as of 22:58, 5 May 2013 by Gerkej (talk | contribs) (ClustalW)

Alignments allow a comparisons of Strings. In the field of bioinformatics, sequence alignments show the relation between two or more sequences.

Theoretical Background

Description

Pairwise or multiple alignments opten contain an aditional line below the proper alignment. This line gives a more accurate description of the relation of the aligned residues above. The symbols show if there is match between identical amino acids or if they are only similar.

Symbols for describing sequence alignments
Symbol Example Meaning
* blub identical residues
: similar residues
.

Sequence Searches

Sequence searches with our query protein sequence, P04062.fasta, were done with the following programs:

  • BLAST
    • using standard parameters
    • against big_80
    • against big
  • Psi-BLAST
    • with number of shown hits and alignments set to 10000 (-b, -v options), so that all the hits will be shown.
    • with all combinations of:
      • 2 iterations: 1 iterations against big_80 followed by 1 iteration against big
      • 10 iterations: 9 iterations against big_80 followed by 1 iteration against big
      • default E-value cutoff (0.002)
      • E-value cutoff 10E-10
    • other options leaved default
  • HHblits
    • with number of shown hits and alignments set to 10000 (-Z, -B options), as in Psi-BLAST
    • with all combinations of:
      • 2 iterations against uniprot_20
      • 10 iterations against uniprot_20
      • default E-value cutoff (0.002)
      • E-value cutoff 10E-10
    • other options leaved default

The script run.pl was written and used for the runs. PSSM files - a3m and hhr for HHblits, chk ("checkpoint") and PSSM for Psi-BLAST were created in order to start the search against another database, from big_80 to big for Psi-BLAST and later against a PDB database for the evaluation.

For Psi-BLAST, first a search against big_80 was done in order to create a good profile, then a last iteration against big was done with this profile. The idea was to get as many hits as possible, so that the results will be comparable with HHblits, where the runs were made against the clustered HMM database. All uniprot_20 cluster members were count in the following comparison and evaluation.

Comparison

Overlap of hits

Percentage identity distribution

E-value distribution

Evaluation

Validation against COPS L30 - L60 groups was made.

Multiple Sequence Alignment

For the multiple sequence alignments three sets were created. Therefore the results of the previous task were according to their sequnece identiy to the native protein sequence of glucocerebrosidase. Set 1 and set 2 contains 10 sequences, including the native protein sequence to keep the alignments in relation to the Gaucher's disease causing protein. For the remainig 9 sequences have a sequence identity to the glucocerebrosidase of more than 60% in set 1 and less than 30% in set 2. In set 3, the sequences are over the whole range of sequence identity. The multiple sequence alignments were made with Clustalw, Muscle and T-Coffee.

Set 1: sequence identity >60%
ID Identity in %
P04062 100
A9UD35 84.1
D1L2S0 83.0
3gxi_A 99.8
2nt1_A 99.8
F6WDY8 90.7
Q2KHZ8 89.2
F5CB27 81.8
F5H241 98.2
B7Z6S9 99.8
Set 2: sequence identity <30%
ID Identity in %
P04062 100
H6CEV7 26.5
Q21GD0 24.3
I1WBF3 23.7
I9HH59 29.4
D0TN48 25.4
B1VPJ0 27.5
E2LY19 24.8
K9HBW2 25.9
B5QQZ8 27.1
Set 3: sequence identity over all
ID Identity in % ID Identity in %
P04062 100 B5DYA3 32.0
B4JTN5 34.1 F5CB37 81.8
E1ZYU8 42.7 J2STU0 34.2
F4E6W5 26.4 G9BHQ3 80.7
F4X220 41.7 2nsx_B 99.8
E5UPZ0 21.6 G9BHQ5 82.5
C6A5Q0 25.0 D0TIX6 25.8
B1QKT0 27.8 1y7v_A 99.8
D4SV88 34.6 A9UD58 80.4
A9UD54 81.8 G9MUP6 20.7

ClustalW

  • Results for Clustalw of  : Set1
Results of ClustalW
Set 1 Set 2 Set 3
Conserved Columns 11 8 0
Complete number of gaps in P0 226
Number of contiguous gaps

Muscle

T-Coffee

Alignment Comparison