Task 2 (MSUD)

Sequence searches

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Results

The query sequences for the 4 subunits of BCKDC locate at /mnt/home/student/weish/master-practical-2013/task01/.

Results for sequence search locate in the directory /mnt/home/student/weish/master-practical-2013/task02/01-seq-search/results. For BLAST and PSI-BLAST, statistics (such as E-value, probability and identity) are stored in *.tsv files. Detailed results are shown in xml files. For HHBlits, the *.hhr files contain information about statistics and hits.

BCKDHA

• E-value and identity distribution for different sequence search methods
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  E-value distribution of sequence search methods. (Query sequence is RefSeq of BCKDHA)

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  Indentity distribution of sequence search methods. (Query sequence is RefSeq of BCKDHA)

• Intersection of hits between different sequence search methods
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  Relative intersection of hits between BLAST and other sequence search methods.

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  Relative intersection between PSI-BLAST(iter. 2, E-value 0.002) and other sequence search methods.

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  Relative intersection between PSI-BLAST(iter. 2, E-value 10e-10) and other sequence search methods.

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  Relative intersection between PSI-BLAST(iter. 10, E-value 0.002) and other sequence search methods.

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  Relative intersection between PSI-BLAST(iter. 10, E-value 10e-10) and other sequence search methods.

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  Relative intersection between HHBlits and other sequence search methods.

BCKDHB

• E-value and identity distribution for different sequence search methods
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  E-value distribution of sequence search methods. (Query sequence is RefSeq of BCKDHB)

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  Indentity distribution of sequence search methods. (Query sequence is RefSeq of BCKDHB)

• Intersection of hits between different sequence search methods
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  Relative intersection of hits between BLAST and other sequence search methods.

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  Relative intersection between PSI-BLAST(iter. 2, E-value 0.002) and other sequence search methods.

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  Relative intersection between PSI-BLAST(iter. 2, E-value 10e-10) and other sequence search methods.

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  Relative intersection between PSI-BLAST(iter. 10, E-value 0.002) and other sequence search methods.

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  Relative intersection between PSI-BLAST(iter. 10, E-value 10e-10) and other sequence search methods.

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  Relative intersection between HHBlits and other sequence search methods.

DBT

• E-value and identity distribution for different sequence search methods
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  E-value distribution of sequence search methods. (Query sequence is RefSeq of DBT)

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  Indentity distribution of sequence search methods. (Query sequence is RefSeq of DBT)

• Intersection of hits between different sequence search methods
• Intersection to blast DBT.png

  Relative intersection of hits between BLAST and other sequence search methods.

• Intersection to psiblast(iter. 2, e-val. 0.002) DBT.png

  Relative intersection between PSI-BLAST(iter. 2, E-value 0.002) and other sequence search methods.

• Intersection to psiblast(iter. 2, e-val. 10e-10) DBT.png

  Relative intersection between PSI-BLAST(iter. 2, E-value 10e-10) and other sequence search methods.

• Intersection to psiblast(iter. 10, e-val. 0.002) DBT.png

  Relative intersection between PSI-BLAST(iter. 10, E-value 0.002) and other sequence search methods.

• Intersection to psiblast(iter. 10, e-val. 10e-10) DBT.png

  Relative intersection between PSI-BLAST(iter. 10, E-value 10e-10) and other sequence search methods.

• Intersection to hhblits DBT.png

  Relative intersection between HHBlits and other sequence search methods.

Multiple sequence alignments

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Results

The datsets can be found in /mnt/home/student/schillerl/MasterPractical/task2/datasets/.

The MSAs are located at /mnt/home/student/schillerl/MasterPractical/task2/MSAs/.

In the following sections the MSAs, visualised with Jalview, are shown.

BCKDHA

Low sequence identity

Mafft:

Muscle:

T-Coffee:

High sequence identity

Mafft:

Muscle:

T-Coffee:

Whole range sequence identity

Mafft:

Muscle:

T-Coffee:

BCKDHB

Low sequence identity

Mafft:

Muscle:

T-Coffee:

High sequence identity

Mafft:

Muscle:

T-Coffee:

Whole range sequence identity

Mafft:

Muscle:

T-Coffee:

DLD

Low sequence identity

Mafft:

Muscle:

T-Coffee:

High sequence identity

Mafft:

Muscle:

T-Coffee:

Whole range sequence identity

Mafft:

Muscle:

T-Coffee:

Discussion

For the datasets with high sequence identity the three MSA programs Mafft, Muscle and T-Coffee come to similar results and find almost the same conserved blocks. Sometimes T-Coffee arranges gaps differently than the others and so does not find as much conserved columns. Especially at the ends of the sequences, the results of the programs differ a little. This is due to different scoring schemes that are used in the programs.

For low sequence identity, the programs have problems to find the right alignment. They don't agree in the position of gaps and also sometimes find different conserved columns. They don't cope with low similarity and so one can't really rely on these results.

For whole range sequence identity the results are similar w. r. t. many and different gaps at the ends of the sequences, but the programs agree more in the conserved columns that they find.

The results of Muscle and Mafft seem more similar to each other than to those of T-Coffee. T-Coffee often treats the ends of the sequences, which have low sequence identity, differently than the others. It is striking that almost always the alignment of Muscle has the shortest and the one of T-Coffee has the highest length, especially in cases with low sequence identity. If an alignment is very long, this means there are many gaps and less aligned residues, this might be a sign of bad alignment quality.

Altogether, there appear regions with many conserved columns and those with many gaps. The conserved blocks or columns correspond to secondary structure elements and functionally important residues, respectively. Gaps in the alignment appear in regions where there are loops in the structure of the protein, so that insertions or deletions that occur during evolution don't alter the overall structure or function of the protein.

As criteria for a good alignment one could run different alignment algorithms like in this task and compare the results. If one of them finds more conserved columns, this might be better than another. Different programs can be better than others if different datasets are used, so it is always a good idea to try more than one algorithm and pick out the best result. Mafft is often a good choice because it generated relatively precise results but still is very fast.