Progressive MSA

• Do pair-wise alignment• Develop an evolutionary tree• Most closely related sequences are then

aligned, then more distant are added.• Genetic distance - number of mismatched

positions divided by the total number of matched positions (gaps not considered).

Example

• Domain: a segment of a protein that can fold to a 3D structure independent of other segments of the protein.

• Card Domain• Caspase recruitment domains (CARDs) are modules

of 90 - 100 amino acids involved in apoptosis signaling

pathways. • http://www.mshri.on.ca/pawson/card.html

A

B

C

B

A

C

C

A

B

C

B

A

These are equivalent trees

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

Previous tree was Rooted

These are Unrooted trees

Gaps

• Clustalw attempts to place gaps between conserved domains.

• In known sequences, gaps are preferentially found between secondary structure elements (alpha helices, beta strands).

• Clustalw attempts to place gaps between conserved domains.

• In known sequences, gaps are preferentially found between secondary structure elements (alpha helices, beta strands).

Problem with Progressive Alignment: Errors made in

early alignments are propagated throughout the

MSA

Profiles & Gaps

• From an MSA, a conserved region identified and a scoring matrix (profile) constructed for that region.

• Each position has a score associated with an amino acid substitution or gap.

• Blocks- also extracted from MSA, but no gaps are permitted.

• Block Server

• Results– TLE short form– TLEl Long form

Hidden Markov Models

• Probabilistic model of a Multiple sequence alignment.

• No indel penalties are needed• Experimentally derived information can

be incorporated• Parameters are adjusted to represent

observed variation.• Requires at least 20 sequences

The bottom line of states are the main states (M)•These model the columns of the alignment

The second row of diamond shaped states are called the insert states (I)•These are used to model the highly variable regions in the alignment.

The top row or circles are delete states (D)•These are silent or null states because they do not match any residues, they simply allow the skipping over of main states.

B M1 M2 M3 M4 E

I1 I2 I3 I4

D1 D2 D3 D4

M5 M6

D5 D6

I5 I6I0

The Evolution of a Sequence

• Over long periods of time a sequence will acquire random mutations.– These mutations may result in a new amino acid

at a given position, the deletion of an amino acid, or the introduction of a new one.

– Over VERY long periods of time two sequences may diverge so much that their relationship can not see seen through the direct comparison of their sequences.

Hidden Markov Models

• Pair-wise methods rely on direct comparisons between two sequences.

• In order to over come the differences in the sequences, a third sequence is introduced, which serves as an intermediate.

• A high hit between the first and third sequences as well as a high hit between the second and third sequence, implies a relationship between the first and second sequences. Transitive relationship

Introducing the HMM

• The intermediate sequence is kind of like a missing link.

• The intermediate sequence does not have to be a real sequence.

• The intermediate sequence becomes the HMM.

Introducing the HMM

• The HMM is a mix of all the sequences that went into its making.

• The score of a sequence against the HMM shows how well the HMM serves as an intermediate of the sequence.– How likely it is to be related to all the other

sequences, which the HMM represents.

B M1 M2 M3 M4 E

Match State with no Indels

MSGLMTNL

Arrow indicates transition probability.In this case 1 for each step

B M1 M2 M3 M4 E

Match State with no Indels

MSGLMTNL

Also have probability of Residue at each positon

M=1S=0.5T=0.5

B M1 M2 M3 M4 E

MSGLMTNL

M=1S=0.5T=0.5

Typically want to incorporate small probability for all other amino acids.

B M1 M2 M3 M4 E

I1 I2 I3 I4

MS.GLMT.NLMSANI

Permit insertion states

Transition probabilities may not be 1

I0

B M1 M2 M3 M4 E

I1 I2 I3 I4

MS..GLMT..NLMSA.NIMTARNL

Permit insertion states

I0

B M1 M2 M3 M4 E

I1 I2 I3 I4

MS..GL--MT..NLAGMSA.NIAGMTARNLAG

D1 D2 D3 D4

M5 M6

D5 D6

I5 I6

DELETE PERMITS INCORPORATION OFLAST TWO SITES OF SEQ1

I0

The bottom line of states are the main states (M)•These model the columns of the alignment

The second row of diamond shaped states are called the insert states (I)•These are used to model the highly variable regions in the alignment.

The top row or circles are delete states (D)•These are silent or null states because they do not match any residues, they simply allow the skipping over of main states.

B M1 M2 M3 M4 E

I1 I2 I3 I4

D1 D2 D3 D4

M5 M6

D5 D6

I5 I6I0

Dirichlet Mixtures• Additional information to expand

potential amino acids in individual sites.

• Observed frequency of amino acids seen in certain chemical environments– aromatic– acidic– basic– neutral– polar