Multiple sequence alignment

Conserved blocks are recognized

Different degrees of similarity are marked

Multiple Sequence Alignment

VTISCTGSSSNIGAG-NHVKWYQQLPG

VTISCTGTSSNIGS--ITVNWYQQLPG

LRLSCSSSGFIFSS--YAMYWVRQAPG

LSLTCTVSGTSFDD--YYSTWVRQPPG

PEVTCVVVDVSHEDPQVKFNWYVDG--

ATLVCLISDFYPGA--VTVAWKADS--

AALGCLVKDYFPEP--VTVSWNSG---

VSLTCLVKGFYPSD--IAVEWESNG--

The purpose of multiple sequence alignments is to place homologous positions of homologous sequences into the same column.

ClustalW

• Based on phylogenetic analysis• A phylogenetic tree is created using a pairwise

distance matrix and nearest-neighbor algorithm• The most closely-related pairs of sequences are

aligned using dynamic programming• Each of the alignments is analyzed and a profile

of it is created• Alignment profiles are aligned progressively for

a total alignment

Progressive multiple alignment

• Perform pairwise alignments for all sequencesAssume a match gives a score of 1, a mismatch is -0.25, indel is -0.5

1 -.25 1 1 1 1

Total Score: 4.75

Progressive multiple alignment

• Create guide tree from pairwise alignments

• Use tree to build multiple sequence alignment

• Align most similar sequences first (give the most reliable alignments)

• Align the profile to the next closest sequence

• Align profiles to each other Multiple sequence

alignment will be at the root of the tree

Progressive multiple alignment

Web ClustalW2 options:

Operational options

Output options

Output options, matrix choice, gap opening penalty

Gap penalties, output tree type

File input in GCG, FASTA, EMBL, GenBank, Phylip, or several other formats

Choose to run clustalw interactively or wait for results by email.

Interactive may take some time so be patient

Give your alignment a title.

You can choose between a fast or full alignment. Full is more accurate

and is what we will be using.

We will use this option

And this one

Alignment - considerations

• The programs simply try to maximize the number of matches– The “best” alignment may not be the

correct biological one• Multiple alignments are done progressively

– Such alignments get progressively worse as you add sequences

– Mistakes that occur during alignment process are frozen in.

• You will sometimes have to correct manually

Problem What to do

Many sequences Start with 10-15 sequences and avoid aligning more than 50sequences.

Very different sequences Sequences that are less than 30% identical with more thanhalf of the other sequences in the set often cause troubles.

Identical sequences They never help. Unless you have a very good reason to doso, avoid incorporating in your MSA any sequence that ismore than 90%identical to another sequence in the set.

Partial sequences MSA programs prefer sequences that are roughly the samelength. Programs often have difficulties comparing a mixtureof complete sequences and shorter fragments.

Repeated domains Sequences with repeated domains cause troubles to mostMSA programs, especially if the number of domains isdifferent.

Need more accuracy then Clustalw for low identity sequences?

PSI-BLAST

Position Specific Iterated BLAST: PSI-BLAST

The purpose of PSI-BLAST is to look deeperinto the database for matches to your queryprotein sequence by employing a scoringmatrix that is customized to your query.

PSI-BLAST is performed in five steps

[1] Select a query and search it against a protein database – REGULAR BLAST

[2] PSI-BLAST constructs a multiple sequence alignmentthen creates a “profile” or specialized position-specificscoring matrix (PSSM) – user-assisted – you can help choosing the candidates.

[3] The PSSM is used as a query against the database

[4] PSI-BLAST estimates statistical significance (E values)

[5] Repeat steps [3] and [4] iteratively, typically 5 times.At each new search, a new profile is used as the query.

PPSSSSMM

PSI-BLAST: self-positives

PSI-BLAST is useful to detect weak but biologically meaningful relationships between proteins.

The main source of false positives is the erroneous amplification of sequences not related to the query. For instance, a query with a coiled-coil motif may detect thousands of other proteins with this motifthat are not homologous.

Once even a single non-related protein is included in a PSI-BLAST search above threshold, it will not go away.

One way to check results: take newly found seqs and perform PSI-BLAST using them, then examine whether we ‘fish’ original seq (reciprocal identification)