Multiple sequence alignment · 10/11/2006 · Multiple sequence alignment: today’s goals • to define what a multiple sequence alignment is and how it is generated; to describe

Multiple sequence alignment

Wednesday, October 11, 2006

Sarah [email protected]

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Multiple sequence alignment: today’s goals

• to define what a multiple sequence alignment is and how it is generated; to describe profile HMMs

• to introduce databases of multiple sequence alignments

• to introduce ways you can make your own multiple sequence alignments

• to show how a multiple sequence alignment provides the basis for phylogenetic trees

Page 319

Multiple sequence alignment: definition

Wallace, Blackshields, Higgins Curr Opp Struct Biol 2005 15:261-266

Multiple sequence alignment: properties

• for two proteins sharing 30% amino acid identity, about 50% of the individual amino acids are superposable in the two structures

Page 320

Generally align proteins:

nucleotides less well-conserved

nucleotide sequences are less informative -> harder to align with high confidence

How do you know if you have the “correct” alignment of a protein family? Is there one correct alignment?

Sequence identity (%)

Prop

ortio

n of

resi

dues

in c

omm

on c

ore

Proportion of structurally superposable residues in pairwise alignments

as a function of sequence identity

After Chothia & Lesk (1986)

GlobinCytochrome cSerine proteaseImmunoglobulin domain

0.5

0.25

0.75

100 75 50 25 0

Multiple sequence alignment: features

• some aligned residues, such as cysteines that formdisulfide bridges, may be highly conserved

• there may be conserved motifs such as a transmembrane domain

• there may be conserved secondary structure features

• there may be regions with consistent patterns ofinsertions or deletions (indels), often indicating the presence of subfamilies

Page 320

Multiple sequence alignment: uses

• MSA is more sensitive than pairwise alignmentin detecting homologs

• Detect conserved motifs and domains (from databasesearch or from alignment of a family)

• Phylogenetic analysis: MSA provides accurate estimationof evolutionary distances

• 2° and 3° structure prediction: MSA predictions significantlybetter than from a single sequence

• very few bioinformatics protocols bypass MSA stagePage 321

Multiple sequence alignment: methods

There are four main ways to make a multiple sequence alignment:

(1) Exact methods

(2) Progressive alignment (CLUSTALW, MUSCLE)

(3) Iterative approaches (Praline, IterAlign)

(4) Consistency-based methods (ProbCons)


Exact methods: dynamic programmingInstead of the 2-D dp matrix that you saw in theNeedleman-Wunsch technique, think about a 3-D,4-D etc matrix.

Exact methods give optimal alignments but are not feasible in time or space for more than ~10 sequences

Still an extremely active field though—the “holy grail”


Progressive methods: use a guide tree (a little like aphylogenetic tree but NOT a phylogenetic tree) to determine how to combine pairwise alignments one by oneto create a multiple alignment.

Making multiple alignments using trees was a verypopular subject in the ‘80s. Fitch and Yasunobu (1974)may have first proposed the idea, but Hogeweg andHesper (1984) and many others worked on the topic beforeFeng and Doolittle (1987) hit the scene—they made one important contribution that got their names attached to thisalignment method.

Examples: CLUSTALW, MUSCLE


Iterative methods: compute a sub-optimal solution and keep modifying that intelligently using dynamic programming or other methods until the solution converges.

Examples: IterAlign, Praline, MAFFT


Consistency-based algorithms: generally use a database of both local high-scoring alignments and long-range global alignments to create a final alignment

These are very powerful, very fast, and very accurate methods

Examples: T-COFFEE, Prrp, DiAlign, ProbCons

Multiple sequence alignment: methodsAaack! This is confusing! How do we know whichprogram to use?

There are benchmarking multiple alignment datasets that have been aligned painstakingly by hand, by structural similarity, or by extremely time- and memory-intensive automated exact algorithms.

Also, to be practical, some programs have interfaces that are much more user-friendly than others. And most programs are excellent so it depends on your preference.

If your proteins have 3D structures, USE THESE to help you judge your alignments.


Benchmarking tests suggest that ProbCons, a consistency-based/progressive algorithm, performs the best on the BAliBASE set, although MUSCLE, a progressive alignment package, is an extremely fast and accurate program.

CLUSTALW, everyone’s old favorite, continues to be a standout and is included in almost every MSA paper you will see. It has withstood the test of time. Plus, it has a nice interface (expecially with CLUSTALX) and is easy to use.


Example of progressive MSA using CLUSTALW: two data sets

Five distantly related lipocalins (human to E. coli)

Five closely related RBPs

When you do this, obtain the sequences of interest in FASTA format!

Page 321

Get sequences from GenBank

Get sequences from GenBank

Use Clustal W to do a progressive MSA

http://www.ebi.ac.uk/clustalw/

Feng-Doolittle MSA occurs in 3 stages

[1] Do a set of global pairwise alignments(Needleman and Wunsch)

[2] Create a guide tree

[3] Progressively align the sequences according to theguide tree

Page 321

Progressive MSA stage 1 of 3:generate global pairwise alignments

five distantly related lipocalins

best score

Progressive MSA stage 1 of 3:generate global pairwise alignments

five closely related lipocalins

best score

Number of pairwise alignments needed

Need to align every sequence to everyother sequence (handshake problem)

For N sequences, (N-1)(N)/2

For 5 sequences, (4)(5)/2 = 10

Page 322

Feng-Doolittle stage 2: guide tree

• Convert similarity scores to distance scores (seetext)

• A guide tree shows the distance between objects

• Use UPGMA (defined in Chapter 11)

• ClustalW provides a syntax to describe the tree

• A guide tree is not a phylogenetic tree

Page 323

Progressive MSA stage 2 of 3:generate a guide tree calculated from

the distance matrix

five distantly related lipocalins

Progressive MSA stage 2 of 3:generate guide tree

five closely related lipocalins

Feng-Doolittle stage 3: progressive alignment

• Make a MSA based on the order in the guide tree

• Start with the two most closely related sequences

• Then add the next closest sequence

• Continue until all sequences are added to the MSA

• Rule: “once a gap, always a gap.” <- this is what madethem famous!

Page 324

Page 324

Why “once a gap, always a gap”?

• There are many possible ways to make a MSA

• Where gaps are added is a critical question

• Gaps are first added to the first two (closest) sequences

• To change the initial gap choices later on would beto give more weight to distantly related sequences

• To maintain the initial gap choices is to trustthat those gaps are most believable (shouldn’t letthe final alignment be affected most by distantly related sequences!)

Page 324

Progressive MSA stage 3 of 3:progressively align the sequences

following the branch order of the tree

Fig. 10.3Page 324

Progressive MSA stage 3 of 3:CLUSTALX output

Clustal W alignment of 5 closely related lipocalins

CLUSTAL W (1.82) multiple sequence alignment

gi|89271|pir||A39486 MEWVWALVLLAALGSAQAERDCRVSSFRVKENFDKARFSGTWYAMAKKDP 50gi|132403|sp|P18902|RETB_BOVIN ------------------ERDCRVSSFRVKENFDKARFAGTWYAMAKKDP 32gi|5803139|ref|NP_006735.1| MKWVWALLLLAAW--AAAERDCRVSSFRVKENFDKARFSGTWYAMAKKDP 48gi|6174963|sp|Q00724|RETB_MOUS MEWVWALVLLAALGGGSAERDCRVSSFRVKENFDKARFSGLWYAIAKKDP 50gi|132407|sp|P04916|RETB_RAT MEWVWALVLLAALGGGSAERDCRVSSFRVKENFDKARFSGLWYAIAKKDP 50

********************:* ***:*****

gi|89271|pir||A39486 EGLFLQDNIVAEFSVDENGHMSATAKGRVRLLNNWDVCADMVGTFTDTED 100gi|132403|sp|P18902|RETB_BOVIN EGLFLQDNIVAEFSVDENGHMSATAKGRVRLLNNWDVCADMVGTFTDTED 82gi|5803139|ref|NP_006735.1| EGLFLQDNIVAEFSVDETGQMSATAKGRVRLLNNWDVCADMVGTFTDTED 98gi|6174963|sp|Q00724|RETB_MOUS EGLFLQDNIIAEFSVDEKGHMSATAKGRVRLLSNWEVCADMVGTFTDTED 100gi|132407|sp|P04916|RETB_RAT EGLFLQDNIIAEFSVDEKGHMSATAKGRVRLLSNWEVCADMVGTFTDTED 100

*********:*******.*:************.**:**************

gi|89271|pir||A39486 PAKFKMKYWGVASFLQKGNDDHWIIDTDYDTYAAQYSCRLQNLDGTCADS 150gi|132403|sp|P18902|RETB_BOVIN PAKFKMKYWGVASFLQKGNDDHWIIDTDYETFAVQYSCRLLNLDGTCADS 132gi|5803139|ref|NP_006735.1| PAKFKMKYWGVASFLQKGNDDHWIVDTDYDTYAVQYSCRLLNLDGTCADS 148gi|6174963|sp|Q00724|RETB_MOUS PAKFKMKYWGVASFLQRGNDDHWIIDTDYDTFALQYSCRLQNLDGTCADS 150gi|132407|sp|P04916|RETB_RAT PAKFKMKYWGVASFLQRGNDDHWIIDTDYDTFALQYSCRLQNLDGTCADS 150

****************:*******:****:*:* ****** *********

Fig. 10.5Page 326


following the branch order of the tree:Order matters

THE LAST FAT CAT THE FAST CAT THE VERY FAST CAT THE FAT CAT

THE LAST FAT CATTHE FAST CAT ---

THE LAST FA-T CATTHE FAST CA-T ---THE VERY FAST CAT THE LAST FA-T CAT

THE FAST CA-T ---THE VERY FAST CATTHE ---- FA-T CATAdapted from C. Notredame, Pharmacogenomics 2002


following the branch order of the tree:Order matters

THE FAT CAT THE FAST CAT THE VERY FAST CAT THE LAST FAT CAT

THE FA-T CATTHE FAST CAT

THE ---- FA-T CATTHE ---- FAST CATTHE VERY FAST CAT THE ---- FA-T CAT

THE ---- FAST CATTHE VERY FAST CATTHE LAST FA-T CATAdapted from C. Notredame, Pharmacogenomics 2002


following the branch order of the tree:CLUSTALW results

MUSCLE: next-generation progressive MSA

1) Build a draft progressive alignmentDetermine pairwise similarity through k-mer counting

(not by alignment)

Compute distance (triangular distance) matrix

Construct tree using UPGMA

Construct draft progressive alignment following tree


2) Improve the progressive alignmentCompute pairwise identity through current MSA

Construct new tree with Kimura distance measures

Compare new and old trees: if improved, repeat this step, if not improved, then we’re done


3) Refinement of the MSASplit tree in half by deleting one edge

Make profiles of each half of the tree

Re-align the profiles

Accept/reject the new alignment

MUSCLE output (formattedwith SeaView)

Praline output for thesame alignment: pure iterative

approach

ProbCons—consistency-based approach

Combines iterative and progressive approaches with a unique probabilistic model.

Uses Hidden Markov Models (more in a minute) to calculate probability matrices for matching residues, uses this to construct a guide tree

Progressive alignment hierarchically along guide tree

Post-processing and iterative refinement (a little like MUSCLE)

ProbCons—consistency-based approach

ProbCons output for thesame alignment: how consistency

iteration helps

Multiple sequence alignment to profile HMMs• in 90’s people began to see that aligning sequences to profiles gave much more information than pairwise alignment alone.

• Hidden Markov models (HMMs) are “states”that describe the probability of having aparticular amino acid residue at arrangedIn a column of a multiple sequence alignment

• HMMs are probabilistic models

• Like a hammer is more refined than a blast,an HMM gives more sensitive alignmentsthan traditional techniques such as progressive alignments

Page 325

Simple Markov Model

Rain = dog may not want to go outside

Sun = dog will probably go outside

R

S

0.15

0.85

0.2

0.8

Markov condition = no dependency on anything but nearest previous state(“memoryless”)

Simple Hidden Markov Model

Observation: YNNNYYNNNYN

(Y=goes out, N=doesn’t go out)

What is underlying reality (the hidden state chain)?

R

S

0.15

0.85

0.2

0.8

P(dog goes out in rain) = 0.1

P(dog goes out in sun) = 0.85

GTWYA (hs RBP)GLWYA (mus RBP)GRWYE (apoD)GTWYE (E Coli)GEWFS (MUP4)

An HMM is constructed from a MSA

Example: five lipocalins

Fig. 10.6Page 327

GTWYAGLWYAGRWYEGTWYEGEWFS

Prob. 1 2 3 4 5p(G) 1.0p(T) 0.4p(L) 0.2p(R) 0.2p(E) 0.2 0.4p(W) 1.0p(Y) 0.8p(F) 0.2p(A) 0.4p(S) 0.2

Fig. 10.6Page 327


Prob. 1 2 3 4 5p(G) 1.0p(T) 0.4p(L) 0.2p(R) 0.2p(E) 0.2 0.4p(W) 1.0p(Y) 0.8p(F) 0.2p(A) 0.4p(S) 0.2

P(GEWYE) = (1.0)(0.2)(1.0)(0.8)(0.4) = 0.064

log odds score = ln(1.0) + ln(0.2) + ln(1.0) + ln(0.8) + ln(0.4) = -2.75 Fig. 10.6Page 327


P(GEWYE) = (1.0)(0.2)(1.0)(0.8)(0.4) = 0.064

log odds score = ln(1.0) + ln(0.2) + ln(1.0) + ln(0.8) + ln(0.4) = -2.75

G:1.0T:0.4L:0.2R:0.2E:0.2

W:1.0Y:0.8F:0.2

E:0.4A:0.4S:0.2

Fig. 10.6Page 327

Structure of a hidden Markov model (HMM)

M

Iy

Ix

p1

p7

p6

p5

p3

p2

p4

Structure of a hidden Markov model (HMM):Trellis representation

Fig. 10.7Page 328

HMMER: build a hidden Markov model

Determining effective sequence number ... done. [4]Weighting sequences heuristically ... done.Constructing model architecture ... done.Converting counts to probabilities ... done.Setting model name, etc. ... done. [x]

Constructed a profile HMM (length 230)Average score: 411.45 bitsMinimum score: 353.73 bitsMaximum score: 460.63 bitsStd. deviation: 52.58 bits

Fig. 10.8Page 329

HMMER: calibrate a hidden Markov model

HMM file: lipocalins.hmmLength distribution mean: 325Length distribution s.d.: 200Number of samples: 5000random seed: 1034351005histogram(s) saved to: [not saved]POSIX threads: 2- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

HMM : xmu : -123.894508lambda : 0.179608max : -79.334000

Fig. 10.8Page 329

HMMER: search an HMM against GenBankScores for complete sequences (score includes all domains):Sequence Description Score E-value N-------- ----------- ----- ------- ---gi|20888903|ref|XP_129259.1| (XM_129259) ret 461.1 1.9e-133 1gi|132407|sp|P04916|RETB_RAT Plasma retinol- 458.0 1.7e-132 1gi|20548126|ref|XP_005907.5| (XM_005907) sim 454.9 1.4e-131 1gi|5803139|ref|NP_006735.1| (NM_006744) ret 454.6 1.7e-131 1gi|20141667|sp|P02753|RETB_HUMAN Plasma retinol- 451.1 1.9e-130 1..gi|16767588|ref|NP_463203.1| (NC_003197) out 318.2 1.9e-90 1

gi|5803139|ref|NP_006735.1|: domain 1 of 1, from 1 to 195: score 454.6, E = 1.7e-131*->mkwVMkLLLLaALagvfgaAErdAfsvgkCrvpsPPRGfrVkeNFDv

mkwV++LLLLaA + +aAErd Crv+s frVkeNFD+gi|5803139 1 MKWVWALLLLAA--W--AAAERD------CRVSS----FRVKENFDK 33

erylGtWYeIaKkDprFErGLllqdkItAeySleEhGsMsataeGrirVL+r++GtWY++aKkDp E GL+lqd+I+Ae+S++E+G+Msata+Gr+r+L

gi|5803139 34 ARFSGTWYAMAKKDP--E-GLFLQDNIVAEFSVDETGQMSATAKGRVRLL 80

eNkelcADkvGTvtqiEGeasevfLtadPaklklKyaGvaSflqpGfddy+N+++cAD+vGT+t++E dPak+k+Ky+GvaSflq+G+dd+

gi|5803139 81 NNWDVCADMVGTFTDTE----------DPAKFKMKYWGVASFLQKGNDDH 120

Fig. 10.9Page 330

HMMER: search an HMM against GenBankmatch to a bacterial lipocalin

gi|16767588|ref|NP_463203.1|: domain 1 of 1, from 1 to 177: score 318.2, E = 1.9e-90*->mkwVMkLLLLaALagvfgaAErdAfsvgkCrvpsPPRGfrVkeNFDv

M+LL+ +A a ++ Af+v++C++p+PP+G++V++NFD+gi|1676758 1 ----MRLLPVVA------AVTA-AFLVVACSSPTPPKGVTVVNNFDA 36

erylGtWYeIaKkDprFErGLllqdkItAeySleEhGsMsataeGrirVL+rylGtWYeIa+ D+rFErGL + +tA+ySl++ +G+i+V+

gi|1676758 37 KRYLGTWYEIARLDHRFERGL---EQVTATYSLRD--------DGGINVI 75

eNkelcADkvGTvtqiEGeasevfLtadPaklklKyaGvaSflqpGfddyNk++++D+ +++ +EG+a ++t+ P +++lK+ Sf++p++++y

gi|1676758 76 -NKGYNPDR-EMWQKTEGKA---YFTGSPNRAALKV----SFFGPFYGGY 116

Fig. 10.9Page 330

HMMER: search an HMM against GenBankScores for complete sequences (score includes all domains):Sequence Description Score E-value N-------- ----------- ----- ------- ---gi|3041715|sp|P27485|RETB_PIG Plasma retinol- 614.2 1.6e-179 1gi|89271|pir||A39486 plasma retinol- 613.9 1.9e-179 1gi|20888903|ref|XP_129259.1| (XM_129259) ret 608.8 6.8e-178 1gi|132407|sp|P04916|RETB_RAT Plasma retinol- 608.0 1.1e-177 1gi|20548126|ref|XP_005907.5| (XM_005907) sim 607.3 1.9e-177 1gi|20141667|sp|P02753|RETB_HUMAN Plasma retinol- 605.3 7.2e-177 1gi|5803139|ref|NP_006735.1| (NM_006744) ret 600.2 2.6e-175 1

gi|5803139|ref|NP_006735.1|: domain 1 of 1, from 1 to 199: score 600.2, E = 2.6e-175*->meWvWaLvLLaalGgasaERDCRvssFRvKEnFDKARFsGtWYAiAK

m+WvWaL+LLaa+ a+aERDCRvssFRvKEnFDKARFsGtWYA+AKgi|5803139 1 MKWVWALLLLAAW--AAAERDCRVSSFRVKENFDKARFSGTWYAMAK 45

KDPEGLFLqDnivAEFsvDEkGhmsAtAKGRvRLLnnWdvCADmvGtFtDKDPEGLFLqDnivAEFsvDE+G+msAtAKGRvRLLnnWdvCADmvGtFtD

gi|5803139 46 KDPEGLFLQDNIVAEFSVDETGQMSATAKGRVRLLNNWDVCADMVGTFTD 95

tEDPAKFKmKYWGvAsFLqkGnDDHWiiDtDYdtfAvqYsCRLlnLDGtCtEDPAKFKmKYWGvAsFLqkGnDDHWi+DtDYdt+AvqYsCRLlnLDGtC

gi|5803139 96 TEDPAKFKMKYWGVASFLQKGNDDHWIVDTDYDTYAVQYSCRLLNLDGTC 145

Fig. 10.9Page 330

BLOCKSCDDDOMOINTERPROiProClassMetaFAMPFAMPRINTSPRODOM PROSITESMART

Databases of multiple sequence alignments

Page 331-332

BLOCKS (HMM)CDD (HMM)DOMO (Gapped MSA)INTERPROiProClassMetaFAMPfam (profile HMM library)PRINTSPRODOM (PSI-BLAST)PROSITESMART


Page 331-332



Integrative resources

Page 331-332

Pfam (protein family) database:http://pfam.wustl.edu/ or

http://www.sanger.ac.uk/Software/Pfam/

http://pfam.wustl.edu/

Pfam (protein family) database




SMART: Simple ModularArchitecture Research Tool(emphasis on cell signaling)Search for conserved domains

Page 338



CDD at NCBI = PFAM + SMART

Page 333

Query = your favorite protein

Database = set of many PSSMs

CDD is related to PSI-BLAST, but distinct

CDD searches against profiles generatedfrom pre-selected alignments

Purpose: to find conserved domainsin the query sequence

You can access CDD via DART at NCBI

CDD uses RPS-BLAST: reverse position-specific

Page 333

CDART (fromNCBI) allowsCDD searches

BLOCKS server http://blocks.fhcrc.org/

BLOCKS: ungapped MSA

Very highly conserved regions

Fig. 10.22Page 341

See chapter 8

PROSITE has a syntax to define signatures

See chapter 8

PopSet: polymorphisms in population data

Fig. 10.23Page 342


Fig. 10.23Page 342


http://bioweb.pasteur.fr/seqanal/alignment/ReviewEdital/review-edital-desc.html

Nice overview site—lots of alignment utilities, including editors and viewers. Most are current.

http://www.public.iastate.edu/~pedro/research_tools.html

Pedro’s molecular biology tools. Most are current.

Big Picture sites–very useful



Manual curation:PfamPROSITEBLOCKSPRINTS

Automated curation:DOMOPRODOMMetaFam

+ comprehensive- alignment errors

MSA databases: manual vs. automated curation

AMASCINEMAClustalWClustalXDIALIGNHMMTMatch-BoxMultAlinMSAMuscaPileUpSAGAT-COFFEE

Multiple sequence alignment programs

Fig. 10.25Page 346

Selecting sequences for a PileUp in GCG

GCGPileUp

Fig. 10.25Page 346

GCGPileUp

Fig. 10.26Page 347

Multiple sequence alignment algorithms

Progressive

Iterative

Local Global

PIMA

DIALIGN SAGA

CLUSTALPileUpother

Fig. 10.27Page 348

[1] Create or obtain a database of protein sequencesfor which the 3D structure is known. Thus we candefine “true” homologs using structural criteria.

[2] Try making multiple sequence alignmentswith many different sets of proteins (very related,very distant, few gaps, many gaps, insertions,outliers).

[3] Compare the answers.

Strategy for assessment of alternativemultiple sequence alignment algorithms

Page 346

Commonly used benchmarking databases:

BAliBASESMARTSABmarkPREFAB (Protein Reference Alignment Benchmark)

Strategy for assessment of alternativemultiple sequence alignment algorithms

[1] As percent identity among proteins drops,performance (accuracy) declines also. This isespecially severe for proteins < 25% identity.

Proteins <25% identity: 65% of residues align well

Proteins <40% identity: 80% of residues align well

Conclusions: assessment of alternativemultiple sequence alignment algorithms

Page 350

[2] “Orphan” sequences are highly divergent members of a family. Surprisingly, orphans do notdisrupt alignments.


Page 350

[3] Separate multiple sequence alignments can be combined (e.g. RBPs and lactoglobulins).

Profile-based methods are very powerful but the strength of the profile depends on the quality ofthe initial alignment.


Page 350

[4] When proteins have large N-terminal or C-terminal extensions, local alignment algorithmsare superior. PileUp (global) is an exception.


Page 350

Documents

Multiple sequence alignment · 10/11/2006 · Multiple sequence alignment: today’s goals • to define what a multiple sequence alignment is and how it is generated; to describe