multiple sequence alignment monday, december 8, 2008 introduction to bioinformatics me:800.707 j....

Multiple sequence alignment

Monday, December 8, 2008

Introduction to BioinformaticsME:800.707J. Pevsner

[email protected]

Many of the images in this powerpoint presentationare from Bioinformatics and Functional Genomicsby J Pevsner (ISBN 0-471-21004-8). Copyright © 2003 by Wiley.

These images and materials may not be usedwithout permission from the publisher.

Visit http://www.bioinfbook.org

Additional credit: some of these slides were made by Sarah Wheelan (Johns Hopkins Bloomberg School of Public Health) from the Intro to Bioinformatics course.

Copyright notice

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

19

Multiple sequence alignment: outline

[1] Introduction to MSAExact methodsProgressive (ClustalW)Iterative (MUSCLE)Consistency (ProbCons)Structure-based (Expresso)Conclusions: benchmarking studies

[3] Hidden Markov models (HMMs), Pfam and CDD

[4] MEGA to make a multiple sequence alignment

[5] Multiple alignment of genomic DNA

[6] Introduction to molecular evolution and phylogeny

Multiple sequence alignment: definition

• a collection of three or more protein (or nucleic acid) sequences that are partially or completely aligned

• homologous residues are aligned in columns across the length of the sequences

• residues are homologous in an evolutionary sense

• residues are homologous in a structural sense

Page 320

ClustalW

Note how the region of a conserved histidine (▼) varies depending on which algorithm is used

Praline

MUSCLE

Probcons

TCoffee

Multiple sequence alignment: properties

• not necessarily one “correct” alignment of a protein family

• protein sequences evolve...

• ...the corresponding three-dimensional structures of proteins also evolve

• may be impossible to identify amino acid residues that align properly (structurally) throughout a multiple sequence alignment

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

Page 320

Multiple sequence alignment: features

• some aligned residues, such as cysteines that form disulfide 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 of insertions or deletions (indels)

Page 320

Multiple sequence alignment: uses

• MSA is more sensitive than pairwise alignment to detect homologs

• BLAST output can take the form of a MSA, and can reveal conserved residues or motifs

• Population data can be analyzed in a MSA (PopSet)

• A single query can be searched against a database of MSAs (e.g. PFAM)

• Regulatory regions of genes may have consensus sequences identifiable by MSA

Page 321

Multiple sequence alignment: methods

Exact methods: dynamic programmingInstead of the 2-D dynamic programming matrix in theNeedleman-Wunsch technique, think about a 3-D,4-D or higher order matrix.

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

Still an extremely active field.


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)—they made one important contribution that got their names attached to thisalignment method.

Examples: CLUSTALW, MUSCLE


Example of 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 the FASTA format! (You can save them in a Word document)

Page 321

The input for ClustalW: a group of sequences(DNA or protein) in the FASTA format

Get sequences from Entrez Protein (or HomoloGene)

You can display sequences from Entrez Protein in the fasta format

When you get a DNA sequence from Entrez Nucleotide, you can click CDS to select only the

coding sequence.

This is very useful for phylogeny studies.

HomoloGene: an NCBI resource to obtain multiple related sequences

[1] Enter a query at NCBI such as globin[2] Click on HomoloGene (left side)[3] Choose a HomoloGene family, and view in the fasta format

Use ClustalW to do a progressive MSA

http://www2.ebi.ac.uk/clustalw/ Fig. 10.1

Page 321

Feng-Doolittle MSA occurs in 3 stages

[1] Do a set of global pairwise alignments (Needleman and Wunsch’s dynamic programming

algorithm)

[2] Create a guide tree

[3] Progressively align the sequences

Page 321

Progressive MSA stage 1 of 3:generate global pairwise alignments

Fig. 10.2Page 323

five distantly related lipocalins

best score

Progressive MSA stage 1 of 3:generate global pairwise alignments

Start of Pairwise alignmentsAligning...Sequences (1:2) Aligned. Score: 84Sequences (1:3) Aligned. Score: 84Sequences (1:4) Aligned. Score: 91Sequences (1:5) Aligned. Score: 92Sequences (2:3) Aligned. Score: 99Sequences (2:4) Aligned. Score: 86Sequences (2:5) Aligned. Score: 85Sequences (3:4) Aligned. Score: 85Sequences (3:5) Aligned. Score: 84Sequences (4:5) Aligned. Score: 96

Fig. 10.4Page 325

five closely related lipocalins

best score

Number of pairwise alignments needed

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

• A tree shows the distance between objects

• Use UPGMA (defined in the phylogeny lecture)

• 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

Fig. 10.2Page 323

Progressive MSA stage 2 of 3:generate guide tree

((gi|5803139|ref|NP_006735.1|:0.04284,(gi|6174963|sp|Q00724|RETB_MOUS:0.00075,gi|132407|sp|P04916|RETB_RAT:0.00423):0.10542):0.01900,gi|89271|pir||A39486:0.01924,gi|132403|sp|P18902|RETB_BOVIN:0.01902);

Fig. 10.4Page 325

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.”

4

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

Note that you can download CLUSTALX locally, rather than using a web-based program!

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

* asterisks indicate identity in a column


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

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 often 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

Page 324

Additional features of ClustalW improveits ability to generate accurate MSAs

• Individual weights are assigned to sequences; very closely related sequences are given less weight,while distantly related sequences are given more weight

• Scoring matrices are varied dependent on the presenceof conserved or divergent sequences, e.g.:

PAM20 80-100% idPAM60 60-80% idPAM120 40-60% idPAM350 0-40% id

• Residue-specific gap penalties are applied


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

MUSCLE: next-generation progressive MSA

[1] Build a draft progressive alignment

Determine 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 alignment

Compute 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 MSA

Split tree in half by deleting one edge

Make profiles of each half of the tree

Re-align the profiles

Accept/reject the new alignment

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

MUSCLE output (formatted with SeaView)

SeaView is a graphical multiple sequence alignment editor available at http://pbil.univ-lyon1.fr/software/seaview.html

Praline output for the same alignment: pure iterative approach

Boxes highlight a region that is difficult to align

Iterative approaches: MAFFT

• Uses Fast Fourier Transform to speed up profile alignment

• Uses fast two-stage method for building alignments using k-mer frequencies

• Offers many different scoring and aligning techniques• One of the more accurate programs available• Available as standalone or web interface• Many output formats, including interactive

phylogenetic trees

Iterative approaches: MAFFT

Has about 1000 advanced settings!


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

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)

2e Fig. 5.12

ProbCons—consistency-based approach

Sequence x xi

Sequence y yj

Sequence z zk

If xi aligns with zk

and zk aligns with yj

then xi should align with yj

ProbCons incorporates evidence from multiple sequences to guide the creation of a pairwise alignment.

ProbCons—consistency-based approachhttp://probcons.stanford.edu/

ProbCons output for the same alignment: consistency iteration helps

http://tcoffee.org

Make an MSAMSA w. structural dataCompare MSA methodsMake an RNA MSACombine MSA methods

Consistency-basedStructure-based

Back translate protein MSA

APDB ClustalW output


How do we know which program 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.

Some programs have interfaces that are 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. For example, try Expresso at http://www.tcoffee.org.

[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

BaliBase: comparisonof multiple sequencealignment algorithms

Fig. 10.30Page 349


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 is the most popular program. It has a nice interface (especially with ClustalX) and is easy to use.

Multiple sequence alignment to profile HMMs

► 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

► HMMs may give more sensitive alignmentsthan traditional techniques such as progressive alignment

Page 325

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

PositionProb. 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)(0.5) = 0.08

log odds score = ln(1.0) + ln(0.2) + ln(1.0) + ln(0.8) + ln(0.5) = -2.53

G:1.0 W:1.0

T:0.4

L:0.2

R:0.2

E:0.2

Y:0.8

F:0.2

A:0.5

E:0.5

S:1.0

Structure of a hidden Markov model (HMM)

M

Iy

Ix

p1

p7

p6

p5

p3

p2

p4


Fig. 10.7Page 328


main state

insert state

delete state

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| [Mus] (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|[Homo] (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|[Salmonella] (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

eNkelcADkvGTvtqiEGeasevfLtadPaklklKyaGvaSflqpGfddy Nk++++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+AK gi|5803139 1 MKWVWALLLLAAW--AAAERDCRVSSFRVKENFDKARFSGTWYAMAK 45

KDPEGLFLqDnivAEFsvDEkGhmsAtAKGRvRLLnnWdvCADmvGtFtD KDPEGLFLqDnivAEFsvDE+G+msAtAKGRvRLLnnWdvCADmvGtFtD gi|5803139 46 KDPEGLFLQDNIVAEFSVDETGQMSATAKGRVRLLNNWDVCADMVGTFTD 95

tEDPAKFKmKYWGvAsFLqkGnDDHWiiDtDYdtfAvqYsCRLlnLDGtC tEDPAKFKmKYWGvAsFLqkGnDDHWi+DtDYdt+AvqYsCRLlnLDGtC gi|5803139 96 TEDPAKFKMKYWGVASFLQKGNDDHWIVDTDYDTYAVQYSCRLLNLDGTC 145

Fig. 10.9Page 330

Two kinds of multiple sequence alignment resources

Text-based or query-based searches:CDD, Pfam (profile HMMs), PROSITE

[2] Multiple sequence alignment by manual input

Muscle, ClustalW, ClustalX

[1] Databases of multiple sequence alignments

Page 329

BLOCKSCDD Pfam SMARTDOMO (Gapped MSA)INTERPROiProClassMetaFAMPRINTSPRODOM (PSI-BLAST)PROSITE

Databases of multiple sequence alignments

TheseUseHMMs

PFAM (protein family) database:http://pfam.sanger.ac.uk/

Fig. 10.11Page 331

PFAM (protein family) text search result

Fig. 10.12Page 334

PFAM HMM for lipocalins

20 amino acids

position

PFAM HMM for lipocalins: GXW motif

G W

20 amino acids

PFAM GCG MSF format

Fig. 10.13Page 335

Pfam (protein family) database

PFAM JalView viewer

Fig. 10.14Page 336

PFAM JalView viewer

Fig. 10.15Page 336

PFAM JalView viewer:principalcomponentsanalysis(PCA)

Fig. 10.16Page 337

Fig. 10.17Page 337

SMART: Simple ModularArchitecture Research Tool(emphasis on cell signaling)

Page 338

SMART: lipocalin result

Fig. 10.18Page 338

BLOCKSCDD Pfam SMARTDOMO (Gapped MSA)INTERPROiProClassMetaFAMPRINTSPRODOM (PSI-BLAST)PROSITE

Databases of multiple sequence alignments

ConservedDomainDatabase(CDD) at NCBI = PFAM + SMART

[1] Go to NCBI Structure[2] Click CDD[3] Enter a text query, or a protein sequence

CDD: Conserved domain database

CDD: Conserved domain database

CDD=

PFAM+

SMART

Fig. 10.20Page 339

Purpose: to find conserved domainsin the query sequence

Query = your favorite protein

Database = set of many position-specificscoring matrices (PSSMs), i.e. a set of MSAs

CDD is related to PSI-BLAST, but distinct

CDD searches against profiles generatedfrom pre-selected alignments

CDD uses RPS-BLAST: reverse position-specific

Page 333

MEGA version 4: Molecular Evolutionary Genetics Analysis

Download from www.megasoftware.net


Two ways to create a multiple sequence alignment1. Open the Alignment Explorer, paste in a FASTA MSA2. Select a DNA query, do a BLAST search

Once your sequences are in MEGA, you can run ClustalWthen make trees and do phylogenetic analyses

1

2

[1] Open the Alignment Explorer

[2] Select “Create a new alignment”

[3] Click yes (for DNA) or no (for protein)

[4] Find, select, and copy a multiple sequence alignment (e.g. from Pfam; choose FASTA with dashes for gaps)

[5] Paste it into MEGA

[6] If needed, run ClustalW to align the sequences

[7] Save (Ctrl+S) as .masthen exit and save as .meg

Multiple sequence alignment of genomic DNA

There are typically few sequences (up to several dozen, each having up to millions of base pairs. Adding more species improves accuracy.

Alignment of divergent sequences often reveals islands of conservation (providing “anchors” for alignment).

Chromosomes are subject to inversions, duplications, deletions, and translocations (often involving millions of base pairs). E.g. human chromosome 2 is derived from the fusion of two acrocentric chromosomes.

There are no benchmark datasets available.

Introduction to evolution and phylogeny

Nomenclature of trees

Four stages of molecular phylogeny:[1] selecting sequences[2] multiple sequence alignment[3] tree-building[4] tree evaluation

Practical approaches to making trees

Goal of the phylogeny lecture

Charles Darwin’s 1859 book (On the Origin of SpeciesBy Means of Natural Selection, or the Preservationof Favoured Races in the Struggle for Life) introducedthe theory of evolution.

To Darwin, the struggle for existence induces a naturalselection. Offspring are dissimilar from their parents(that is, variability exists), and individuals that are morefit for a given environment are selected for. In this way,over long periods of time, species evolve. Groups of organisms change over time so that descendants differstructurally and functionally from their ancestors.

Introduction

Page 357

Darwin did not understand the mechanisms by whichhereditary changes occur. In the 1920s and 1930s, a synthesis occurred between Darwinism and Mendel’s principles of inheritance.

The basic processes of evolution are[1] mutation, and also[2] genetic recombination as two sources of variability;[3] chromosomal organization (and its variation);[4] natural selection [5] reproductive isolation, which constrains the effects of selection on populations

Introduction

Page 357(See Stebbins, 1966)

At the molecular level, evolution is a process ofmutation with selection.

Molecular evolution is the study of changes in genesand proteins throughout different branches of the tree of life.

Phylogeny is the inference of evolutionary relationships.Traditionally, phylogeny relied on the comparisonof morphological features between organisms. Today,molecular sequence data are also used for phylogeneticanalyses.

Introduction

Page 358

Studies of molecular evolution began with the firstsequencing of proteins, beginning in the 1950s.

In 1953 Frederick Sanger and colleagues determinedthe primary amino acid sequence of insulin.

(The accession number of human insulin is NP_000198)

Historical background

Page 358

Fig. 11.1Page 359

Mature insulin consists of an A chain and B chainheterodimer connected by disulphide bridges

The signal peptide and C peptide are cleaved,and their sequences display fewerfunctional constraints.

Fig. 11.1Page 359

Fig. 11.1Page 359

Note the sequence divergence in the disulfide loop region of the A chain

By the 1950s, it became clear that amino acid substitutions occur nonrandomly. For example, Sanger and colleagues noted that most amino acid changes in the insulin A chain are restricted to a disulfide loop region.Such differences are called “neutral” changes(Kimura, 1968; Jukes and Cantor, 1969).

Subsequent studies at the DNA level showed that rate ofnucleotide (and of amino acid) substitution is about six-to ten-fold higher in the C peptide, relative to the A and Bchains.

Historical background: insulin

Page 358

Fig. 11.1Page 359Number of nucleotide substitutions/site/year

0.1 x 10-9

0.1 x 10-91 x 10-9

Surprisingly, insulin from the guinea pig (and from the related coypu) evolve seven times faster than insulinfrom other species. Why?

The answer is that guinea pig and coypu insulindo not bind two zinc ions, while insulin molecules frommost other species do. There was a relaxation on thestructural constraints of these molecules, and so the genes diverged rapidly.

Historical background: insulin

Page 360

Fig. 11.1Page 359

Guinea pig and coypu insulin have undergone anextremely rapid rate of evolutionary change

Arrows indicate positions at which guinea pig insulin (A chain and B chain) differs from both human and mouse

Fig. 11.2Page 360

Historical background

Oxytocin CYIQNCPLGVasopressin CYFQNCPRG

In the 1950s, other labs sequenced oxytocin and vasopressin. These peptides differ at only two aminoacid residues, but they have distinctly different functions.It became clear that there are significant structural andfunctional consequences to changes in primary amino acid sequence.

In the 1960s, sequence data were accumulated forsmall, abundant proteins such as globins,cytochromes c, and fibrinopeptides. Some proteinsappeared to evolve slowly, while others evolved rapidly.

Linus Pauling, Emanuel Margoliash and others proposed the hypothesis of a molecular clock:

For every given protein, the rate of molecular evolution is approximately constant in all evolutionary lineages

Molecular clock hypothesis

Page 360

As an example, Richard Dickerson (1971) plotted datafrom three protein families: cytochrome c, hemoglobin, and fibrinopeptides.

The x-axis shows the divergence times of the species,estimated from paleontological data. The y-axis showsm, the corrected number of amino acid changes per 100 residues.

n is the observed number of amino acid changes per100 residues, and it is corrected to m to account forchanges that occur but are not observed.

Molecular clock hypothesis

Page 360

N100

= 1 – e-(m/100)

Fig. 11.3Page 361Millions of years since divergence

corr

ecte

d a

min

o a

cid

ch

ang

es

per

100

res

idu

es (

m)

Dickerson (1971)

Dickerson drew the following conclusions:

• For each protein, the data lie on a straight line. Thus, the rate of amino acid substitution has remained constant for each protein.

• The average rate of change differs for each protein. The time for a 1% change to occur between two lines of evolution is 20 MY (cytochrome c), 5.8 MY (hemoglobin), and 1.1 MY (fibrinopeptides).

• The observed variations in rate of change reflect functional constraints imposed by natural selection.

Molecular clock hypothesis: conclusions

Page 361

multiple sequence alignment monday, december 8, 2008 introduction to bioinformatics me:800.707 j....

Documents

correct alignment

conserved residues

sequences residues

homologous residues

aligned residues

amino acid residues

conserved motifs

outline1 introduction