1. #11 - MSAs; PSSMs & Psi-BLAST
BCB 444/544
9/17/07
Lecture 12
Multiple Sequence Alignment (MSA)
PSSMs & Psi-BLAST
#12_Sept17
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2. Required Reading (before lecture)
#11 - MSAs; PSSMs & Psi-BLAST
Required Reading (before lecture)
9/17/07
√Mon Sept Lecture 12
Position Specific Scoring Matrices & PSI-BLAST
Chp 6 - pp (but not HMMs)
Wed Sept 19 - Lecture (not covered on Exam 1)
Hidden Markov Models
Chp 6 - pp 79-84
Eddy: What is a hidden Markov Model?
2004 Nature Biotechnol 22:1315
Wed Sept 21 - EXAM 1
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3. Assignments & Announcements
#11 - MSAs; PSSMs & Psi-BLAST
Assignments & Announcements
9/17/07
Sun Sept Study Guide for Exam 1 was posted
Mon Sept 17 - Answers to HW#2
will be posted ~ Noon
Thu Sept 20 - Lab = Optional Review Session for Exam
Fri Sept Exam 1 - Will cover:
Lectures 2-12 (thru Mon Sept 17)
Labs 1-4
HW2
All assigned reading:
Chps 2-6 (but not HMMs)
Eddy: What is Dynamic Programming~
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4. Chp 5- Multiple Sequence Alignment
#11 - MSAs; PSSMs & Psi-BLAST
9/17/07
Chp 5- Multiple Sequence Alignment
SECTION II SEQUENCE ALIGNMENT
Xiong: Chp 5
Multiple Sequence Alignment
Scoring Function
Exhaustive Algorithms
Heuristic Algorithms
Practical Issues
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5. Multiple Sequence Alignments
#11 - MSAs; PSSMs & Psi-BLAST
9/17/07
Multiple Sequence Alignments
Credits for slides: Caragea & Brown, 2007;
Fernandez-Baca, Heber &Hunter
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6. #11 - MSAs; PSSMs & Psi-BLAST
Overview
9/17/07
What is a multiple sequence alignment (MSA)?
Where/why do we need MSA?
What is a good MSA?
Algorithms to compute a MSA
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7. Multiple Sequence Alignment
#11 - MSAs; PSSMs & Psi-BLAST
Multiple Sequence Alignment
9/17/07
Generalize pairwise alignment of sequences to include > 2 homologous sequences
Analyzing more than 2 sequences gives us much more information:
Which amino acids are required? Correlated?
Evolutionary/phylogenetic relationships
Similar to PSI-BLAST idea (not yet covered in lecture): use a set of homologous sequences to provide more "sensitivity"
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8. #11 - MSAs; PSSMs & Psi-BLAST
Definition: MSA
9/17/07
Given a set of sequences, a multiple sequence alignment is an assignment of gap characters, such that
resulting sequences have same length
no column contains only gaps
ATT-GC
AT-TGC
AT-T-GC
ATTTGC
ATTTGC
ATTT-GC
ATTTG
ATTTG-
ATTT-G- NO
YES NO
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9. Displaying MSAs: using CLUSTAL W
#11 - MSAs; PSSMs & Psi-BLAST
Displaying MSAs: using CLUSTAL W
9/17/07
RED: AVFPMILW (small)
BLUE: DE (acidic, negative chg)
MAGENTA: RHK (basic, positive chg)
GREEN: STYHCNGQ (hydroxyl + amine + basic)
* entirely conserved column
: all residues have ~ same size AND hydropathy
. all residues have ~ same size OR hydropathy
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10. What is a Consensus Sequence?
#11 - MSAs; PSSMs & Psi-BLAST
What is a Consensus Sequence?
9/17/07
A single sequence that represents most common residue of each column in a MSA
Example:
FGGHL-GF
F-GHLPGF
FGGHP-FG
Steiner consensus seqence: Given sequences s1,…, sk, find a sequence s* that maximizes Σi S(s*,si)
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11. #11 - MSAs; PSSMs & Psi-BLAST
Applications of MSA
9/17/07
Building phylogenetic trees
Finding conserved patterns, e.g.:
Regulatory motifs (TF binding sites)
Splice sites
Protein domains
Identifying and characterizing protein families
Find out which protein domains have same function
Finding SNPs (single nucleotide polymorphisms) & mRNA isoforms (alternatively spliced forms)
DNA fragment assembly (in genomic sequencing)
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12. Application: Recover Phylogenetic Tree
#11 - MSAs; PSSMs & Psi-BLAST
Application: Recover Phylogenetic Tree
9/17/07
What was series of events that led to current species?
NYLS
NFLS
NYLS
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13. Application: Discover Conserved Patterns
#11 - MSAs; PSSMs & Psi-BLAST
9/17/07
Application: Discover Conserved Patterns
Is there a conserved cis-acting regulatory sequence?
Rationale: if they are homologous (derived from a common ancestor),
they may be structurally equivalent
TATA box = transcriptional
promoter element
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14. Goal: Characterize Protein Families
#11 - MSAs; PSSMs & Psi-BLAST
9/17/07
Goal: Characterize Protein Families
Which parts of globin sequences are most highly conserved?
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15. Databases of Multiple Alignments
#11 - MSAs; PSSMs & Psi-BLAST
9/17/07
Databases of Multiple Alignments
Pfam (Protein Domain Families data base)
Contains alignments and HMMs of protein families
InterPro
Integrates: Prosite, Prints, ProDom, Pfam, and SMART
BLOCKS
Segments of highly conserved multiple alignments
Hovergen (Homologous Vertebrate Genes Database)
COGs (Clusters of Orthologous Groups)
BaliBASE (Benchmark alignments database)
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16. #11 - MSAs; PSSMs & Psi-BLAST
Scoring an Alignment
9/17/07
Goal: Align homologous positions.
But: Without knowledge of phylogenetic tree is this very hard (sometimes impossible) to achieve!
NYLS
NFLS
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17. #11 - MSAs; PSSMs & Psi-BLAST
Scoring an Alignment
9/17/07
In practice, simple scoring functions are used: usually, columns are scored independently, i.e.
ith column of alignment m
gap penalty A F P G Q I K Y D W -
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18. #11 - MSAs; PSSMs & Psi-BLAST
Sum of Pairs (SP) Score
9/17/07
SP = sum of scores of all possible pairs of sequences in an MSA based on a particular scoring matrix
Compute for each column c S(mi) = k<l s(mik,mil) F I - mi
residue l
PAM or BLOSUM score A F P G I Q W - D Y
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19. #11 - MSAs; PSSMs & Psi-BLAST
How Score Gaps in MSAs?
9/17/07
Want to align gaps with each other over all sequences. A gap in a pairwise alignment that “matches” a gap in another pairwise alignment should cost less than introducing a totally new gap.
Possible that a new gap could be made to “match” an older one by adjusting older pairwise alignment
Change gap penalty near conserved domains of various kinds (e.g. secondary structure elements, hydrophobic regions)
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20. #11 - MSAs; PSSMs & Psi-BLAST
Example: SP Score
9/17/07 F Y G D 5 -2 -1 7 1 -5 4 -3
F-G G D
F-G m=
FYD
Gap penalty: -8
s(-,-) = 0
BLOSUM 60
S(m) = S(m1) + S(m2) + S(m3)
= 3s(F,F) + 2s(-,Y) + s(-,-) + s(G,G) + 2s(G,D)
= = -3
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21. Overcoming problems with SP scoring
#11 - MSAs; PSSMs & Psi-BLAST
Overcoming problems with SP scoring
9/17/07
Use weights to incorporate evolution in sum of pairs scoring:
Some pairwise alignments are more important than others
e.g., more important to have a good alignment between mouse & human sequences than between mouse & bird
Assign different weights to different pairwise alignments
Weight decreases with evolutionary distance
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22. How Compute a Multiple Alignment?
#11 - MSAs; PSSMs & Psi-BLAST
9/17/07
How Compute a Multiple Alignment?
Algorithms for MSA:
Multidimensional dynamic programming
Optimal global alignment (time & space intensive!!!)
Progressive alignments (Star alignment, ClustalW)
Match closely-related sequences first using a guide tree
Iterative methods
Combined local alignments (Dialign)
Multiple re-building attempts to find best alignment
Partial order alignment (POA)
Local alignments
Profiles, Blocks, Patterns
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23. Dynamic Programming for MSA
#11 - MSAs; PSSMs & Psi-BLAST
Dynamic Programming for MSA
9/17/07
As with pairwise alignments, multiple sequence alignments can be computed by dynamic programming 3D F 2D
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24. Generalized Needleman-Wunsch Algorithm
#11 - MSAs; PSSMs & Psi-BLAST
Generalized Needleman-Wunsch Algorithm
9/17/07 3D
Given 3 sequences x, y, and z:
Main iteration loop:
F(i,j,k) = max ( F(i-1, j-1, k-1) + S(xi, yj, zk),
F(i-1, j-1, k ) + S(xi, yj, - ),
F(i-1, j , k-1) + S(xi, -, zk),
F(i-1, j , k ) + S(xi, -, - ),
F(i , j-1, k-1) + S( -, yj, zk),
F(i , j-1, k ) + S( -, yj, -),
F(i , j , k-1) + S( -, -, zk) )
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25. What Happens to Computational Complexity?
#11 - MSAs; PSSMs & Psi-BLAST
9/17/07
What Happens to Computational Complexity?
Given k sequences of length n:
Space for matrix: O(nk)
Neighbors/cell: 2k-1
Time to compute SP score: O(k2)
Overall runtime: O(k22knk)
Ouch!!! 3D
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26. What's so bad about those exponents? An example: Running Time of DP
#11 - MSAs; PSSMs & Psi-BLAST
What's so bad about those exponents? An example: Running Time of DP
9/17/07
Overall runtime: O(k22knk)
# sequences
running time 2
1 second 3
2 minutes 4
5 hours 5
3 weeks 6
9 years
Sequences: globins ( 150 aa)
But: There are fast heuristics.
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27. Progressive Alignment
#11 - MSAs; PSSMs & Psi-BLAST
Progressive Alignment
9/17/07
Heuristic procedure:
Align most similar sequences first
Add sequences progressively
Often: use guide tree to determine order of alignments
Examples: Star alignment
ClustalW
Multiple Alignment by adding sequences 1 2 3 4
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28. #11 - MSAs; PSSMs & Psi-BLAST
Guide Tree
9/17/07
Binary tree
Leaves correspond to sequences
Internal nodes represent alignments
Root corresponds to final MSA
-TCG
-TCC
ATC-
ATG-
ATC
TCG
ATG
TCC
ATC
ATG
TCG
TCC
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29. #11 - MSAs; PSSMs & Psi-BLAST
9/17/07
Star Alignment - will skip for now, come back to this on Wed Star alignment will NOT be covered on Exam 1
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30. Chp6 - Profiles & Hidden Markov Models
#11 - MSAs; PSSMs & Psi-BLAST
9/17/07
Chp6 - Profiles & Hidden Markov Models
SECTION II SEQUENCE ALIGNMENT
Xiong: Chp 6
Profiles & HMMs
Position Specific Scoring Matrices (PSSMs)
PSI-BLAST
Profiles
Markov Model & Hidden Markov Model
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31. #11 - MSAs; PSSMs & Psi-BLAST
9/17/07
Position Specific Iterated BLAST
Intuition: substitution matrices should be specific to a particular site: penalize alanine→glycine more in a helix
Basic idea:
Use BLAST with high stringency to get a set of closely related sequences
Align those sequences to create a new substitution matrix for each position
Then use that matrix (iteratively) to find additional sequences
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32. #11 - MSAs; PSSMs & Psi-BLAST
9/17/07
Query
PSSM
Multiple
alignment
Sequence
database
BLAST
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33. #11 - MSAs; PSSMs & Psi-BLAST
PSI-BLAST pseudocode
9/17/07
Convert query to PSSM
do {
BLAST database with PSSM
Stop if no new homologs are found
Add new homologs to PSSM }
Print current set of homologs
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34. #11 - MSAs; PSSMs & Psi-BLAST
PSI-BLAST pseudocode
9/17/07
Position-specific scoring matrix
Convert query to PSSM
do {
BLAST database with PSSM
Stop if no new homologs are found
Add new homologs to PSSM }
Print current set of homologs
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35. #11 - MSAs; PSSMs & Psi-BLAST
PSI-BLAST pseudocode
9/17/07
Convert query to PSSM
do {
BLAST database with PSSM
Stop if no new homologs are found
Add new homologs to PSSM }
Print current set of homologs
This step requires a user-defined threshold
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36. Position-specific scoring matrix - PSSM
#11 - MSAs; PSSMs & Psi-BLAST
Position-specific scoring matrix - PSSM
9/17/07 A -1 -2 R 5 1 -3 N 6 D C Q E 2 G H 8 I -4 L K M F P S T W Y 3 V
A PSSM is an n by m matrix, where n is the size of alphabet, and m is length of sequence
Entry at (i, j) is score assigned by PSSM to letter i at the jth position
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37. Position-specific scoring matrix
#11 - MSAs; PSSMs & Psi-BLAST
Position-specific scoring matrix
9/17/07 A -1 -2 R 5 1 -3 N 6 D C Q E 2 G H 8 I -4 L K M F P S T W Y 3 V
A PSSM is an n by m matrix, where n is the size of the alphabet, and m is the length of the sequence.
The entry at (i, j) is the score assigned by the PSSM to letter i at the jth position.
“K” at position 3 gets a score of 2
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38. Position-specific scoring matrix
#11 - MSAs; PSSMs & Psi-BLAST
Position-specific scoring matrix
9/17/07 A -1 -2 R 5 1 -3 N 6 D C Q E 2 G H 8 I -4 L K M F P S T W Y 3 V
This PSSM assigns sequence NMFWAFGH a score of:
= 12
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39. Position-specific scoring matrix
#11 - MSAs; PSSMs & Psi-BLAST
Position-specific scoring matrix
9/17/07 A -1 -2 R 5 1 -3 N 6 D C Q E 2 G H 8 I -4 L K M F P S T W Y 3 V
What score does this PSSM assign to KRPGHFLA?
= 6
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40. Position-specific iterated BLAST
#11 - MSAs; PSSMs & Psi-BLAST
Position-specific iterated BLAST
9/17/07 ?
Query
PSSM
Multiple
alignment
Sequence
database
BLAST
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41. Creating a PSSM from 1 sequence
#11 - MSAs; PSSMs & Psi-BLAST
Creating a PSSM from 1 sequence
9/17/07 L R A -1 -2 R 5 1 -3 N 6 D C Q E 2 G H 8 I -4 L K M F P S T W Y 3 V
RNRGQFGH R
BLOSUM62
matrix
20 by 20
20 by L
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42. Position-specific iterated BLAST
#11 - MSAs; PSSMs & Psi-BLAST
Position-specific iterated BLAST
9/17/07 ?
Query
PSSM
Multiple
alignment
Sequence
database
BLAST
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43. Creating a PSSM from multiple sequences
#11 - MSAs; PSSMs & Psi-BLAST
9/17/07
Creating a PSSM from multiple sequences
Discard columns that contain gaps in query
For each column C
Compute relative sequence weights
Compute PSSM entries, taking into account
Observed residues in this column
Sequence weights
Substitution matrix
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44. Discard query gap columns
#11 - MSAs; PSSMs & Psi-BLAST
Discard query gap columns
9/17/07
EEFG----SVDGLVNNA
QKYG----RLDVMINNA
RRLG----TLNVLVNNA
GGIG----PVD-LVNNA
KALG----GFNVIVNNA
ARFG----KID-LIPNA
FEPEGPEKGMWGLVNNA
AQLK----TVDVLINGA
EEFGSVDGLVNNA
QKYGRLDVMINNA
RRLGTLNVLVNNA
GGIGPVD-LVNNA
KALGGFNVIVNNA
ARFGKID-LIPNA
FEPEGMWGLVNNA
AQLKTVDVLINGA
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45. Compute sequence weights
#11 - MSAs; PSSMs & Psi-BLAST
Compute sequence weights
9/17/07
Low weights are assigned to redundant sequences
High weights are assigned to unique sequences
EEFGSVDGLVNNA 1.2
QKYGRLDVMINNA 1.2
RRLGTLNVLVNNA 0.8
GGIGPVDLLVNNA 0.8
KALGGFNVIVNNA 1.1
ARFGKIDTLIPNA 0.9
FEPEGMWGLVNNA 1.1
AQLKTVDVLINGA 1.3
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46. Compute PSSM entries (simplified version)
#11 - MSAs; PSSMs & Psi-BLAST
Compute PSSM entries (simplified version)
9/17/07
A 0.085
C 0.019
D 0.054
E 0.065
F 0.040
G 0.072
H 0.023
I 0.058
K 0.056
L 0.096
M 0.024
P 0.053
Q 0.042
R 0.054
S 0.072
T 0.063
V 0.073
W 0.016
Y 0.034 + =
PSSM
Observed
residues E Q R G K A F
Background
frequencies
PSSM
column
These are usually derived from a large sequence database
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47. #11 - MSAs; PSSMs & Psi-BLAST
Log-odds score
9/17/07
Estimate the probability of observing each residue
Divide by the background probability of observing the same residue
Take log so scores will be additive
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48. #11 - MSAs; PSSMs & Psi-BLAST
Log-odds score
9/17/07
Residue was generated by foreground model
(i.e., the PSSM)
Residue “A” is observed
Estimate the probability of observing each residue
Divide by the background probability of observing the same residue
Take log so scores will be additive
Residue was generated by the background model (i.e., randomly selected)
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49. #11 - MSAs; PSSMs & Psi-BLAST
Why (not) PSI-BLAST
9/17/07
Weights sequence according to observed diversity specific to family of interest
Advantage: If sequences used to construct Position Specific Scoring Matrices (PSSMs) are all homologous, sensitivity at a given specificity improves significantly
Disadvantage: However, if any non-homologous sequences are included in PSSMs, they are “corrupted.” Then they "pull in" addition non-homologous sequences, and become worse than generic
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50. #11 - MSAs; PSSMs & Psi-BLAST
How to use PSI BLAST
9/17/07
Set initial thresholds high
Inspect each iteration's result for suspicious sequences
Do several iterations (~5), or until no new sequences are found
Even if only looking for a small set of sequences, make initial search very broad
First, use NR (large, inclusive database) with up to 5 iterations to set PSSM
Then use that PSSM to search in restricted domain
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51. #11 - MSAs; PSSMs & Psi-BLAST
PSI-BLAST caveats
9/17/07
Goal: Increased ability to find distant homologs
Cost? additional care to prevent non-homologous sequences from being included in PSSM calculation
When in doubt, leave it out!
Examine sequences with moderate similarity carefully
Be particularly cautious about matches to sequences with highly biased amino acid content
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52. #11 - MSAs; PSSMs & Psi-BLAST
9/17/07
PSI-BLAST example
Query is human NF-Kappa-B sequence
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53. #11 - MSAs; PSSMs & Psi-BLAST
9/17/07
First Iteration … …
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54. #11 - MSAs; PSSMs & Psi-BLAST
Second iteration
9/17/07
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55. #11 - MSAs; PSSMs & Psi-BLAST
Summary
9/17/07
Dynamic programming is O(NM)
BLAST is O(M)
BLAST produces an index of query sequence that allows fast matching to the database
Target database is pre-indexed to indicate positions in all database sequences that match each possible search word above some score threshold
PSI-BLAST iterates BLAST, adding new homologs at each iteration
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