Multiple Sequence Alignments

1. Multiple Sequence Alignments Profiles and Progressive Alignment

2. C A A — G A A A — T A — Profiles for families of sequences can be built from MSAs 1 2 3 1 2 3 A C T G — 50% 25% 0% 0% 25% 75% 0% 0% 25% 0% 25% 0% 25% 0% 50% Note: While profiles can be used for any kind of sequence data, we’ll focus on protein sequences

3. Profiles • Profile: A table that lists the frequencies of each amino acid in each position of protein sequence. • Frequencies are calculated from a MSA containing a domain of interest • Allows us to identify consensus sequence • Derived scoring scheme allows us to align a new sequence to the profile • Profile can be used in database searches • Find new sequences that match the profile • Profiles also used to compute multiple alignments heuristically • Progressive alignment

4. Profiles: Position-Specific Scoring Matrix (PSSM) • To compare a sequence to a profile, need to assign a score for each amino acid • The score the profile for amino acid a at position p iswhere • f(p,b)= frequency of amino acid b in position p • s(a,b) is the score of (a,b) (from, e.g., BLOSUM or PAM)

5. Insertion/deletion penalty Profiles: PSSM Gribskov et al. PNAS. 84 (13): 4355 (1987)

6. Profiles: Consensus Sequence • A consensus residue C(p) is generated at each position of the profile to aid the display of alignments of target sequences with the profile. • The consensus residue c is the amino acid at p that has the highest score M(p,c). • c is the amino acid most mutationally similar to all the aligned residues of the probe sequences at p, rather than the most common one

7. 1 2 3 4 5 K L M - New sequence: K K L L M Align with profile: K K L - L M 1 - 2 3 4 5 Aligning a sequence to a profile K L M – K K L K L K K M M L – M L – L M K K L - L M K - L M – K K - L K L K K - M M L – M - L – L M

8. 1 2 3 4 5 K L M - Scoring a sequence-to-profile alignment • Score each column separately according to PSSM • Each character contributes to score, weighed by its frequency K K L - L M 1 - 2 3 4 5 Column 1 score: 0.75 s(K,K) + 0.25 s(K,M)

9. Profile-to-sequence alignments • Optimum alignment can be found by dynamic programming • Extension of Needleman-Wunsch • Spaces are only added to msa – never removed • Once a gap, always a gap • Can align profiles to profiles

10. Evolutionary Profiles • Profiles just seen are called average profiles • Generally perform well, but disregard some of the biology • How did each position evolve? • Amount of conservation varies from position to position • Type of conservation varies from position to position • Alternative: Evolutionary profiles • Gribskov, M. and Veretnik, S., Methods in Enzymology266, 198-212, 1996

11. Evolutionary Profiles • Idea: Fit a different model at each position • For each position i : • For each possible ancestor b for position i • Try various evolutionary distances x (assume PAM model), and choose the one that minimizes cross entropywhere • fa = observed frequency of a • pa= predicted frequency of a assuming b is the ancestor and x is the distance • This generates 20 distributions for position i

12. Evolutionary Profiles • For each position i • Compute “mixture coefficient,” Wai, measuring likelihood that the residue a generated observed distribution (see text) • Profile is given bywhere • paij = frequency of residue j in the ancestral residue distribution a at position i • prandom j = frequency of residue j in the database

13. Progressive multiple alignment • Feng & Doolittle 1987, Higgins and Sharp 1988 • Idea: Sequences to be aligned are phylogenetically related • these relationships are used to guide the alignment • Popular implementations: CLUSTALW, PILEUP, T-Coffee

14. CLUSTALW • Perform pair-wise alignments between all pairs of sequences (n x (n-1)/2 possibilities) • Generate distance matrix. • Distance between a pair = number of mismatched positions in alignment divided by total number of matched positions • Generate a Neighbor-Joining ‘guide tree’ from distance table • Use guide tree to progressively align sequences in pairs from tips to root of tree. • Actually, align profiles • “Once a gap, always a gap”

15. CLUSTALW

16. CLUSTALW Tree Tree calculated from an alignment of more than 1100 ring finger domains, using ClustalW 1.83.

17. CLUSTALW heuristics • Individual weights are assigned to each sequence in a partial alignment in order to downweight similar sequences and up-weight highly divergent ones. • Varying substitution matrices at different alignment stages according to sequence divergence. • Gaps • Positions in early alignments where gaps have been opened receive locally reduced gap penalties • Residue-specific gap penalties and locally reduced gap penalties in hydrophilic regions encourage new gaps in potential loop regions rather than regular secondary structure.

18. Progressive Alignment: Discussion • Strengths: • Speed • Progression biologically sensible (aligns using a tree) • Weaknesses: • No objective function. • No way of quantifying whether or not the alignment is good

19. Problems with CLUSTALW • Local minimum problem: • Alignment depends on sequence addition order. • With each alignment some proportion of residues are misaligned • Worse for divergent sequences • Errors get “locked in” and propagate as sequences are added • Can result in arbitrary and incorrect alignments • Clustal uses global alignment … may not be accurate for all parts of the sequence • T-Coffee considers local similarity as well as global

20. Iterative alignment • To avoid local minima, realign subgroups of sequences and then incorporate them into a growing multiple sequence alignment • Improves overall alignment score. • May involve rebuilding the guide tree • May be randomized • Programs: • MultAlin • PRRP • DIALIGN

21. GTGG GTGG CTGG CTGG CCGG CTAA GTAA CTTC Phylogenetic Alignment Given a tree for a set of species S, find ancestral species such that total distance is minimized.