ICP-TROP Molecular Evolution of Proteins and Phylogenetic Analysis Fred R. Opperdoes Christian de...

ICP-TROP

Molecular Evolution of Proteins Molecular Evolution of Proteins and Phylogenetic Analysisand Phylogenetic Analysis Fred R. OpperdoesFred R. Opperdoes

Christian de Duve Institute of Cellular Pathology Christian de Duve Institute of Cellular Pathology (ICP) and Laboratory of Biochemistry, Université (ICP) and Laboratory of Biochemistry, Université catholique de Louvain, Brussels, Belgiumcatholique de Louvain, Brussels, Belgium

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Contents (1)Contents (1)

Arguments in favour of a phylogenetic analysis of the Arguments in favour of a phylogenetic analysis of the corresponding protein rather than the DNAcorresponding protein rather than the DNA

Codon biasCodon biasThe long time horizonThe long time horizonIntronsIntronsMultigene families Multigene families Protein is the unit of selectionProtein is the unit of selectionRNA editingRNA editing

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Contents (2)Contents (2)

Methods for the Multiple Alignment of Protein SequencesMethods for the Multiple Alignment of Protein SequencesTwo sequencesTwo sequencesMultiple sequences (automatic)Multiple sequences (automatic)Manual alignmentManual alignment

Methods for the inference of protein phylogenyMethods for the inference of protein phylogenyDistance methodsDistance methodsMaximum parsimonyMaximum parsimonyReliability and rooting of treesReliability and rooting of trees

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What is a phylogenetic tree What is a phylogenetic tree and what does it tell you?and what does it tell you?

A

B

C

D

E

F

G

H

I

OTUs

Root

External nodes

Internalnodes

A-E are external nodes (extant)F-I are internal (ancestral) nodes

OTUs are operational taxonomic unitsThey can be: species

populationsindividualsgenesproteinsThey are the extant (existing) OTUs

Internal nodes represent ancestralunits.

Topology: order of the nodes on the tree

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Amitochondriates

Mitochondriates

Kinetoplastida

Eubacteria

Animals

Fungi

Eukaryota

Plants

Microsporidia

Diplomonads

Euglena

Parabasalia

Archaebacteria

Algae

Cilates

The ‘tree of life’ based on rRNA The ‘tree of life’ based on rRNA sequencessequences

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Common ancestor?

Energy metabolism

Genetic machinery

The fusion hypothesis: the eukaryotic cell The fusion hypothesis: the eukaryotic cell is a chimaera of eubacterial and is a chimaera of eubacterial and

archaebacterial traitsarchaebacterial traits

Kinetoplastida

Eubacteria

Animals

Fungi

Eukaryota

Plants

Microsporidia

Diplomonads

Euglena

Parabasalia

Archaebacteria

Algae

Cilates

Root?

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TPIS HUMANTPIS MACMUTPIS RABITTPIS MOUSETPIS RAT

TPIS LATCHTPIS CHICKTPIS SCHJA

TPIS SCHMATPIS AEDTOTPIS CULPITPIS CULTA

TPIS ANOMETPIS DROMETPIS HELVITPIS CAEEL

TPIS GRAVETPIS ARATH

TPIS PETHYTPIS COPJATPIS LACSA

TPIS HORVUTPIS SECCE

TPIS MAIZETPIS ORYSA

TPIC SPIOLTPIC SECCETPIS STELP

TPIS TRYBBTPIS TRYCRTPIS LEIME

TPI1 GIALATPI2 GIALA

TPIS EMENITPIS SCHPO

TPIS YEASTTPIS COPCI

TPIS BACSUTPIS STAAU

TPIS BACMETPIS BACSTTPIS LACDE

TPIS LACLATPIS CLOAB

TPIS BORBUTPIS SYNY3

TPIS PLAFATPIS MYCHR

TPIS MYCFLTPIS MYCHY

TPIS MYCGETPIS MYCPN

TPIS TREPATPIS MYCLE

TPIS MYCTUTPIS CORGL

TPIS STRCOTPIS XANFL

TPIS CHLAUTPIS RHIET

PGKT THEMATPIS AQUAE

TPIS VIBSATPIS PSESY

TPIS CHLPNTPIS CHLTR

TPIS ECOLITPIS ENTCL

TPIS HAEINTPIS VIBMA

TPIS BUCAPTPIS HELPJTPIS HELPY

TPIS FRATUTPIS MORSP TPIS PYRHO

TPIS PYRWOTPIS METTH

TPIS ARCFUTPIS METJA

TPIS METBR

Animalia

Planta

Protists

Fungi

Eubacteria

Archaebacteria

Triosephosphate Triosephosphate isomeraseisomerase

Triosephosphate isomerase of eukaryotes is of typical eubacterial origin and probably has entered the eukaryotic cell together with the bacterial endosymbiont that gave rise to the formation of the mitochondrion

Root?

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What is requiredWhat is required

A DNA or protein sequenceA DNA or protein sequence A set of homologous sequencesA set of homologous sequences A good multiple sequence alignmentA good multiple sequence alignment Several programs to create a Several programs to create a

phylogenetic treephylogenetic tree

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DNA or protein ?DNA or protein ?>TBTIM T.brucei TIM gene for microbody triosephosphate isomerase.>TBTIM T.brucei TIM gene for microbody triosephosphate isomerase.CTGCAGCAACTTACTGGGGACGCTGCTATCCTTTCTTCTTCATATTTCTCGTTTACCTACCTGCAGCAACTTACTGGGGACGCTGCTATCCTTTCTTCTTCATATTTCTCGTTTACCTACGTTTAGAGTCTCTGAGATCATTACTAGCAAGCAAACAAGAAGCCATTTGAGTTTCAAGCAGTTTAGAGTCTCTGAGATCATTACTAGCAAGCAAACAAGAAGCCATTTGAGTTTCAAGCAAAGTCTACCAAAAAACAAACTCTTATTATACCGTGCCAAATTATGTCCAAGCCACAACCCAAGTCTACCAAAAAACAAACTCTTATTATACCGTGCCAAATTATGTCCAAGCCACAACCCATCGCAGCAGCCAACTGGAAGTGCAACGGCTCCCAACAGTCTTTGTCGGAGCTTATTGATATCGCAGCAGCCAACTGGAAGTGCAACGGCTCCCAACAGTCTTTGTCGGAGCTTATTGATCTGTTTAACTCCACAAGCATCAACCACGACGTGCAATGCGTAGTGGCCTCCACCTTTGTTCTGTTTAACTCCACAAGCATCAACCACGACGTGCAATGCGTAGTGGCCTCCACCTTTGTTCACCTTGCCATGACGAAGGAGCGTCTTTCACACCCCAAATTTGTGATTGCGGCGCAGAACCACCTTGCCATGACGAAGGAGCGTCTTTCACACCCCAAATTTGTGATTGCGGCGCAGAACGCCATTGCAAAGAGCGGTGCCTTCACCGGCGAAGTCTCCCTGCCCATCCTCAAAGATTTCGCCATTGCAAAGAGCGGTGCCTTCACCGGCGAAGTCTCCCTGCCCATCCTCAAAGATTTCGGTGTCAACTGGATTGTTCTGGGTCACTCCGAGCGCCGCGCATACTATGGTGAGACAAACGGTGTCAACTGGATTGTTCTGGGTCACTCCGAGCGCCGCGCATACTATGGTGAGACAAACGAGATTGTTGCGGACAAGGTTGCCGCCGCCGTTGCTTCTGGTTTCATGGTTATTGCTTGCGAGATTGTTGCGGACAAGGTTGCCGCCGCCGTTGCTTCTGGTTTCATGGTTATTGCTTGCATCGGCGAAACGCTGCAGGAGCGTGAATCAGGTCGCACCGCTGTTGTTGTGCTCACACAGATCGGCGAAACGCTGCAGGAGCGTGAATCAGGTCGCACCGCTGTTGTTGTGCTCACACAGATCGCTGCTATTGCTAAGAAACTGAAGAAGGCTGACTGGGCCAAAGTTGTCATCGCCTACATCGCTGCTATTGCTAAGAAACTGAAGAAGGCTGACTGGGCCAAAGTTGTCATCGCCTACGAACCCGTTTGGGCCATTGGTACCGGCAAGGTGGCGACACCACAGCAAGCGCAGGAAGCCGAACCCGTTTGGGCCATTGGTACCGGCAAGGTGGCGACACCACAGCAAGCGCAGGAAGCCCACGCACTCATCCGCAGCTGGGTGAGCAGCAAGATTGGAGCAGATGTCGCGGGAGAGCTCCACGCACTCATCCGCAGCTGGGTGAGCAGCAAGATTGGAGCAGATGTCGCGGGAGAGCTCCGCATTCTTTACGGCGGTTCTGTTAATGGAAAGAATGCGCGCACTCTTTACCAACAGCGACGCATTCTTTACGGCGGTTCTGTTAATGGAAAGAATGCGCGCACTCTTTACCAACAGCGAGACGTCAACGGCTTCCTTGTTGGTGGTGCCTCACTTAAGCCAGAATTTGTGGACATCATCGACGTCAACGGCTTCCTTGTTGGTGGTGCCTCACTTAAGCCAGAATTTGTGGACATCATCAAAGCCACTCAGTGATTTTCCTTCATGTGTCAATGAGGTTTGGTGCTTTTGCCGTTGAGTAAAGCCACTCAGTGATTTTCCTTCATGTGTCAATGAGGTTTGGTGCTTTTGCCGTTGAGTGGGTGAAGATAGCGGTATATATATATATATATATATATATATATGCGCAAGTGAATATAAGGGTGAAGATAGCGGTATATATATATATATATATATATATATATGCGCAAGTGAATATAAAAAAGATGTAAAGACAGGTAGCAGGGAGAAAACCTCGCATAACATTATAAAAGGGAGTGTAAAAGATGTAAAGACAGGTAGCAGGGAGAAAACCTCGCATAACATTATAAAAGGGAGTGTAACTGGAGTGGGAAAACAAAGGAAAGGGGGATTCGTGTATTGAGCATATGAGAAAAAAAAAACTGGAGTGGGAAAACAAAGGAAAGGGGGATTCGTGTATTGAGCATATGAGAAAAAAAAAAGAAATTATGTTGTATGTTTTTACCTATAATTTATGCGAAGTGAATGACAAAACAAAAAAAGAAATTATGTTGTATGTTTTTACCTATAATTTATGCGAAGTGAATGACAAAACAAAAACCAAAAGGATATCATCATATGCTTTGTTTCATCCAAATGGTTGTTTCTTCCGTACCTCAGCCAAAAGGATATCATCATATGCTTTGTTTCATCCAAATGGTTGTTTCTTCCGTACCTCAGGGTCACTACTTCGTTGAGTGTGGTTTTAGCGAGGAGAGGGAACAATAGGGGGTGTTGTATGGTCACTACTTCGTTGAGTGTGGTTTTAGCGAGGAGAGGGAACAATAGGGGGTGTTGTATACATTTACACGTACGTATCTTCCTTTACTCTCTCTTGCCTTCATTATATTCCCCCTTTTTACATTTACACGTACGTATCTTCCTTTACTCTCTCTTGCCTTCATTATATTCCCCCTTTTTCTGGGAGAGGAAAAGAGAGTGTAGAATGAGGGGAGTACGTGTACGGAATTTTAACGATTACTGGGAGAGGAAAAGAGAGTGTAGAATGAGGGGAGTACGTGTACGGAATTTTAACGATTACCCCCTTTTTTTTCTTTGAACTATTATTTTTAGAATTCCCCCCTTTTTTTTCTTTGAACTATTATTTTTAGAATTC

>P04789|TPIS_TRYBB Triosephosphate isomerase, glycosomal (TIM) (Triose-phosphate isomerase)>P04789|TPIS_TRYBB Triosephosphate isomerase, glycosomal (TIM) (Triose-phosphate isomerase)MSKPQPIAAANWKCNGSQQSLSELIDLFNSTSINHDVQCVVASTFVHLAMTKERLSHPKFMSKPQPIAAANWKCNGSQQSLSELIDLFNSTSINHDVQCVVASTFVHLAMTKERLSHPKFVIAAQNAIAKSGAFTGEVSLPILKDFGVNWIVLGHSERRAYYGETNEIVADKVAAAVASGVIAAQNAIAKSGAFTGEVSLPILKDFGVNWIVLGHSERRAYYGETNEIVADKVAAAVASGFMVIACIGETLQERESGRTAVVVLTQIAAIAKKLKKADWAKVVIAYEPVWAIGTGKVATPFMVIACIGETLQERESGRTAVVVLTQIAAIAKKLKKADWAKVVIAYEPVWAIGTGKVATPQQAQEAHALIRSWVSSKIGADVAGELRILYGGSVNGKNARTLYQQRDVNGFLVGGASLKPQQAQEAHALIRSWVSSKIGADVAGELRILYGGSVNGKNARTLYQQRDVNGFLVGGASLKPEFVDIIKATQEFVDIIKATQ

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The universal genetic codeThe universal genetic codeFirst Second Position Third First Second Position Third Position ------------------------------------ PositionPosition ------------------------------------ Position | U(T) C A G || U(T) C A G | U(T) Phe Ser Tyr Cys U(T)U(T) Phe Ser Tyr Cys U(T) Phe Ser Tyr Cys CPhe Ser Tyr Cys C Leu Ser STOP STOP ALeu Ser STOP STOP A Leu Ser STOP Trp GLeu Ser STOP Trp G

C Leu Pro His Arg U(T)C Leu Pro His Arg U(T) Leu Pro His Arg CLeu Pro His Arg C Leu Pro Gln Arg ALeu Pro Gln Arg A Leu Pro Gln Arg GLeu Pro Gln Arg G

A Ile Thr Asn Ser U(T)A Ile Thr Asn Ser U(T) Ile Thr Asn Ser CIle Thr Asn Ser C Ile Thr Lys Arg AIle Thr Lys Arg A Met Thr Lys Arg GMet Thr Lys Arg G

G Val Ala Asp Gly U(T)G Val Ala Asp Gly U(T) Val Ala Asp Gly CVal Ala Asp Gly C Val Ala Glu Gly AVal Ala Glu Gly A Val Ala Glu Gly GVal Ala Glu Gly G

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CODON BIAS : CODON BIAS : 64 different possible triplet codes encode 20 amino acids. One 64 different possible triplet codes encode 20 amino acids. One

amino acid may be encoded by 1 to 6 different triplet codes, and amino acid may be encoded by 1 to 6 different triplet codes, and 3 of the 64 codes, called stop (or termination) codons, specify 3 of the 64 codes, called stop (or termination) codons, specify "end of peptide sequence" "end of peptide sequence"

The different codons are used with unequal frequency and this The different codons are used with unequal frequency and this distribution of frequency is referred to as "distribution of frequency is referred to as "codon usagecodon usage" "

Codon usage varies between species. Amino-acid codons have Codon usage varies between species. Amino-acid codons have been degenerated with been degenerated with wobblewobble in the third position. in the third position.

Arguments in favour of protein rather than Arguments in favour of protein rather than DNA sequencesDNA sequences

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CODON BIAS : CODON BIAS : 64 different possible triplet codes encode 20 amino acids. One amino acid 64 different possible triplet codes encode 20 amino acids. One amino acid

may be encoded by 1 to 6 different triplet codes, and 3 of the 64 codes, may be encoded by 1 to 6 different triplet codes, and 3 of the 64 codes, called stop (or termination) codons, specify "end of peptide sequence" called stop (or termination) codons, specify "end of peptide sequence"

The different codons are used with unequal frequency and this distribution The different codons are used with unequal frequency and this distribution of frequency is referred to as "of frequency is referred to as "codon usagecodon usage" "

Codon usage varies between species. Amino-acid codons have been Codon usage varies between species. Amino-acid codons have been degenerated with degenerated with wobblewobble in the third position. in the third position.

Arguments in favour of a phylogenetic Arguments in favour of a phylogenetic analysis of the corresponding protein rather analysis of the corresponding protein rather

than the DNAthan the DNA

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The universal genetic codeThe universal genetic codeFirst Second Position Third First Second Position Third

Position ------------------------------------ PositionPosition ------------------------------------ Position

| U(T) C A G || U(T) C A G |

U(T) Phe Ser Tyr Cys U(T)U(T) Phe Ser Tyr Cys U(T)

Phe Ser Tyr Cys CPhe Ser Tyr Cys C

Leu Ser STOP STOP ALeu Ser STOP STOP A

Leu Ser STOP Trp GLeu Ser STOP Trp G

C Leu Pro His Arg U(T)C Leu Pro His Arg U(T)

Leu Pro His Arg CLeu Pro His Arg C

Leu Pro Gln Arg ALeu Pro Gln Arg A

Leu Pro Gln Arg GLeu Pro Gln Arg G

A Ile Thr Asn Ser U(T)A Ile Thr Asn Ser U(T)

Ile Thr Asn Ser CIle Thr Asn Ser C

Ile Thr Lys Arg AIle Thr Lys Arg A

Met Thr Lys Arg GMet Thr Lys Arg G

G Val Ala Asp Gly U(T)G Val Ala Asp Gly U(T)

Val Ala Asp Gly CVal Ala Asp Gly C

Val Ala Glu Gly AVal Ala Glu Gly A

Val Ala Glu Gly GVal Ala Glu Gly G

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Yeasts, protozoa, and animals have different codon preferences,

This would result in differences in DNA sequence related to codon bias and not to evolution.

Arguments in favour of ... (codon bias 2)Arguments in favour of ... (codon bias 2)

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Different species use different Different species use different codonscodons

Homo sapiens [gbmam]: 1 CDS's (389 codons)Homo sapiens [gbmam]: 1 CDS's (389 codons)

--------------------------------------------------------------------------------------------------------------------------------------------------------

fields: [triplet] [frequency: per thousand] ([number])fields: [triplet] [frequency: per thousand] ([number])

--------------------------------------------------------------------------------------------------------------------------------------------------------

UUU 20.6( 8) UCU 5.1( 2) UAU 7.7( 3) UGU 7.7( 3)UUU 20.6( 8) UCU 5.1( 2) UAU 7.7( 3) UGU 7.7( 3)

UUC 12.9( 5) UCC 20.6( 8) UAC 30.8( 12) UGC 0.0( 0)UUC 12.9( 5) UCC 20.6( 8) UAC 30.8( 12) UGC 0.0( 0)

UUA 10.3( 4) UCA 18.0( 7) UAA 0.0( 0) UGA 0.0( 0)UUA 10.3( 4) UCA 18.0( 7) UAA 0.0( 0) UGA 0.0( 0)

UUG 10.3( 4) UCG 0.0( 0) UAG 2.6( 1) UGG 15.4( 6)UUG 10.3( 4) UCG 0.0( 0) UAG 2.6( 1) UGG 15.4( 6)

Saccharomyces cerevisiae [gbpln]: 9295 CDS's (4586264 codons)----------------------------------------------------------------------------fields: [triplet] [frequency: per thousand] ([number])----------------------------------------------------------------------------

UUU 25.9(118900) UCU 23.6(108308) UAU 18.7( 85651) UGU 8.0( 36624)UUC 18.3( 83880) UCC 14.3( 65421) UAC 14.7( 67599) UGC 4.6( 21255)UUA 26.3(120698) UCA 18.7( 85618) UAA 1.0( 4476) UGA 0.6( 2742)UUG 27.2(124967) UCG 8.5( 39137) UAG 0.4( 2058) UGG 10.4( 47694)

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Differences between the “Universal” and Differences between the “Universal” and Mitochondrial Genetic CodesMitochondrial Genetic Codes

CodonCodon Universal code Universal code mitochondrial code mitochondrial code

UGAUGA StopStop TrpTrp

AGAAGA ArgArg StopStop

AGGAGG ArgArg StopStop

AUAAUA IleIle MetMet

Modified from: Li and Graur, 1991, Fundamentals of Molecular Evolution , Modified from: Li and Graur, 1991, Fundamentals of Molecular Evolution ,

Sinauer PublSinauer Publ..

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Arguments in favour... (codon bias)Arguments in favour... (codon bias)

Also, the protozoa use the codons Also, the protozoa use the codons TAA TAA and and TGATGA to to encode encode glutamineglutamine, rather than STOP, rather than STOP

In mitochondria the codon In mitochondria the codon TGATGA encodes encodes tryptophanetryptophane, , rather than STOP rather than STOP

The inclusion of unique codons in a subset of the The inclusion of unique codons in a subset of the sequences will tend to make that subset appear more sequences will tend to make that subset appear more divergent than they really aredivergent than they really are

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Arguments in favour... (codon bias 2)Arguments in favour... (codon bias 2)

High GC content of DNA seems to be associated with High GC content of DNA seems to be associated with aerobiosis in prokaryotes (Naya et al., 2002)aerobiosis in prokaryotes (Naya et al., 2002)

In all major groups both organisms with AT rich and GC In all major groups both organisms with AT rich and GC rich DNA can be found.rich DNA can be found.

The inclusion of unique codons in a subset of the The inclusion of unique codons in a subset of the sequences will tend to make that subset appear more sequences will tend to make that subset appear more divergent than they really aredivergent than they really are

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GC content of DNA in aerobic and GC content of DNA in aerobic and anaerobic prokaryotesanaerobic prokaryotes

Anaerobic

Aerobic

From Naya et al., J. Mol. Evol. 55 (2002) 260-264

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The use of protein sequences in The use of protein sequences in phylogeny requires knowledge of phylogeny requires knowledge of the properties of the amino acids the properties of the amino acids

and their single letter codesand their single letter codes

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AlanineAlanine AA LeucineLeucine LL

ArginineArginine RR LysineLysine KK

AsparagineAsparagine NN MethionineMethionine MM

Aspartic acidAspartic acid DD PhenylalaninePhenylalanine FF

CysteineCysteine CC ProlineProline PP

Glutamic acidGlutamic acid EE SerineSerine SS

GlutamineGlutamine QQ ThreonineThreonine TT

GlycineGlycine GG TryptophaneTryptophaneWW

HistidineHistidine HH TyrosineTyrosine YY

IsoleucineIsoleucine II ValineValine VV

The use of protein sequences in phylogeny The use of protein sequences in phylogeny requires knowledge of the properties of the requires knowledge of the properties of the

amino acids and their single letter codes amino acids and their single letter codes

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LONG TIME HORIZON : LONG TIME HORIZON :

When comparing sequences that have diverged for When comparing sequences that have diverged for possibly a billion years or more, it is very likely that the possibly a billion years or more, it is very likely that the wobble bases in the codons will have become randomized. wobble bases in the codons will have become randomized. By excluding the wobble bases (a general technique), one By excluding the wobble bases (a general technique), one is actually looking at amino acid sequences.is actually looking at amino acid sequences.

So why not taking a protein sequence directly?So why not taking a protein sequence directly?


than the DNAthan the DNA

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Advantages of the translation Advantages of the translation of DNA into protein (1)of DNA into protein (1)

DNA is composed of only four kinds of unit: A, G, C and TDNA is composed of only four kinds of unit: A, G, C and T

If gaps are not allowed, on the average, 25% of residues in two If gaps are not allowed, on the average, 25% of residues in two randomly chosen aligned sequences would be identicalrandomly chosen aligned sequences would be identical

If gaps are allowed, as much as 50 % of residues in two randomly If gaps are allowed, as much as 50 % of residues in two randomly chosen aligned sequences can be identical. Such a situation chosen aligned sequences can be identical. Such a situation may obscure any genuine relationship that may exist. Especially may obscure any genuine relationship that may exist. Especially when comparing distantly related or rapidly evolving gene when comparing distantly related or rapidly evolving gene sequencessequences

Moreover, it is easier to translate a gene sequence into its Moreover, it is easier to translate a gene sequence into its corresponding protein than to remove the third wobble base from corresponding protein than to remove the third wobble base from each of the codons in the geneeach of the codons in the gene

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Alignment of two random DNA Alignment of two random DNA sequencessequences

Without indels19% identity

Indels allowed56% identity

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Translation of DNA into 21 different types of codon (20 amino acids and Translation of DNA into 21 different types of codon (20 amino acids and a terminator) allows the information to sharpen up considerably. Wrong a terminator) allows the information to sharpen up considerably. Wrong frame information is set asideframe information is set aside

Third-base degeneracies are consolidatedThird-base degeneracies are consolidated

After insertion of gaps to align two random protein sequences it can be After insertion of gaps to align two random protein sequences it can be expected that they are between 10-20% identicalexpected that they are between 10-20% identical

As a result of the translation procedure the protein sequences with their As a result of the translation procedure the protein sequences with their 20 amino acids are much more easy to align than the corresponding 20 amino acids are much more easy to align than the corresponding DNA sequences with only 4 nucleotidesDNA sequences with only 4 nucleotides


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Alignment of two random Alignment of two random protein sequencesprotein sequences

Without indels 7% identity

Indels allowed22% identity

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If, after this, you still want to align distantly If, after this, you still want to align distantly related gene sequences, you better prepare first related gene sequences, you better prepare first a protein alignment and then base yourself on a protein alignment and then base yourself on this alignment for the alignment of the gene this alignment for the alignment of the gene sequences and the precise placement of indels sequences and the precise placement of indels in the aligned sequences.in the aligned sequences.

Conclusion: The signal to noise ratio is greatly Conclusion: The signal to noise ratio is greatly improved when using protein sequences over improved when using protein sequences over DNA sequences!DNA sequences!


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TBLASTNTBLASTN

The blast algorithm TBLASTN allows The blast algorithm TBLASTN allows the use of translated protein sequence the use of translated protein sequence information to search for distant information to search for distant relationships between genes relationships between genes

A protein sequence is compared with all A protein sequence is compared with all the translated sequences from a the translated sequences from a nucleotide databasenucleotide database

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Nature of Sequence Nature of Sequence Divergence in proteinsDivergence in proteins

The observed sequence difference of two diverging The observed sequence difference of two diverging sequences takes the course of a negative exponential. sequences takes the course of a negative exponential. This is the result of the fact that each position is subject to This is the result of the fact that each position is subject to reverse changes ("back mutations") and multiple hitsreverse changes ("back mutations") and multiple hits

Thus the observed percentage of difference between the Thus the observed percentage of difference between the protein sequences is not proportional to the actual protein sequences is not proportional to the actual evolutionary difference between two homologous evolutionary difference between two homologous sequencessequences

The evolutionary distance between two proteins is The evolutionary distance between two proteins is expressed in PAM units. PAM (Dayhoff and Eck, 1968) expressed in PAM units. PAM (Dayhoff and Eck, 1968) stands for "accepted point mutation"stands for "accepted point mutation"

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Relation between % Relation between % distance and PAM distance distance and PAM distance PAM PAM DistanceDistance

valuevalue (%) (%)

80 80 5050

100100 6060

200200 7575

250250 8585 Twilight zone Twilight zone

300300 92 92

(From Doolittle, 1987, Of URFs and ORFs, University Science Books)(From Doolittle, 1987, Of URFs and ORFs, University Science Books)

As the evolutionary distance increases, the probability of super-As the evolutionary distance increases, the probability of super-imposed mutations becomes greater resulting in a lower observed imposed mutations becomes greater resulting in a lower observed percent difference. percent difference.

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Relation between % Relation between % distance and PAM distance distance and PAM distance

Distance %

4003002001000

510152025303540455055606570758085

Pam value

Twilight zone

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The Kimura correction for The Kimura correction for multiple substitutionsmultiple substitutions

The formula used to correct for multiple hits is from Motoo Kimura (Kimura, M. The neutral Theory of Molecular Evolution, Camb.Univ.Press, 1983, page 75) :

K = -Ln(1 - D - (D.D)/5) where D is the observed distance and K is corrected distance.

This formula gives mean number of estimated substitutions per site and, in contrast to D (the observed number), can be greater than 1 i.e. more than one substitution per site, on average. For example, if you observe 0.8 differences per site (80% difference; 20% identity), then the above formula predicts that there have been 2.5 substitutions per site over the course of evolution since the 2 sequences diverged.

This can also be expressed in PAM units by multiplying by 100 (mean number of substitutions per 100 residues).

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Proteins evolve at highly Proteins evolve at highly different ratesdifferent rates

Rate of Change TheoreticalPAMs / 108 yrs Lookback Time

Pseudogenes 400 45 x 106 yrsFibrinopeptides 90 200 "Lactalbumins 27 670 "Lysozymes 24 850 "Ribonucleases 21 850 "Haemoglobins 12 1500 "Acid proteases 8 2300 "Cytochrome c 4 5000 "Glyceraldehyde-P dehydrogenase 2 9000 "Glutamate dehydrogenase 1 18000 "

PAM = number of Accepted Point Mutations per 100 amino acids. Useful lookback time = 360 PAMs

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Some Important Dates in Some Important Dates in HistoryHistory

EventEvent Number of years agoNumber of years ago

Origin of the UniverseOrigin of the Universe 15 ± 415 ± 4 101099 yrs yrs

Formation of the Solar SystemFormation of the Solar System 4.64.6 " "

First Self-replicating SystemFirst Self-replicating System 3.5 ± 0.5 3.5 ± 0.5 " "

Prokaryotic-Eukaryotic DivergenceProkaryotic-Eukaryotic Divergence 2.0 ± 0.52.0 ± 0.5 " "

Plant-Animal Divergence Plant-Animal Divergence ~1.0 ~1.0 " "

Invertebrate-Vertebrate DivergenceInvertebrate-Vertebrate Divergence0.50.5 " "

Mammalian RadiationMammalian Radiation Beginning ~ 0.1Beginning ~ 0.1 " "

From Doolittle, Of URFs and ORFs, 1987From Doolittle, Of URFs and ORFs, 1987

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Construction of a phylogenetic tree from Construction of a phylogenetic tree from phosphoglycerate kinase sequencesphosphoglycerate kinase sequences

0.1

Rat

Mouse

Human

Horse

Drosophila

Schistosoma

Kluyveromyces

Yeast

Neurospora

Yarli

Plasmodium

Leishmania

Crithidia

Trypanosoma 1

Trypanosoma 2

Wheat

Zymomonas

Escherichia

Methanobacter

Bacillus

Human Mouse Horse DrosophilaSchistosoma Wheat Yarli Yeast NeurosporaKluyveromycesPlasmodiumTrypanosoma Crithidia LeishmaniaBacillus Escherichia Mycobacter Zymomonas Methanobacter

G L D C G P E S S K K Y A E A V T R A K Q I V W N G PG L D C G T E S S K K Y A E A V G R A K Q I V W N G PG L D C G T E S S K K Y A E A V A R A K Q I V W N G PG L D V G P K T R E L F A A P I A R A K L I V W N G PG L D I G P K T I E E F S K V I S R A K T I V W N G PG L D I G P D S V K T F N D A L D T T Q T I I W N G PG L D C G P K S I E E F Q K V I G E S K T I L W N G PG L D N G P E S R K L F A A T V A K A K T I V W N G PG L D C G E E S V K L F T Q A I N E S Q T I L W N G PG L D N G P E S R K A F A A T V A E A K T I V W N G PG L D A G P K S I E N Y K D V I L T S K T V I W N G PA L D I G P K T I E K Y V Q T I G K C K S A I W N G PA L D I G P K T I K I Y E D V I A K C K S T I W N G PA L D I G P R T I H M Y E E V I G R C K S A I W N G PA L D I G P K T R E L Y R D V I R E S K L V V W N G PI L D I G D A S A Q E L A E I L K N A K T I L W N G PS L D V G S K T I A L F E S Y L K T A K T I F W N G PI L D V G P K A V A A L T E V L K A S K T L V W N G PI Y D I G T N T I T E Y A K F I R D A K T I F A N G P

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INTRONS : INTRONS : A study of the evolution of a protein using its DNA A study of the evolution of a protein using its DNA

sequence should only include coding sequences sequence should only include coding sequences

This requires that in every DNA sequence all the This requires that in every DNA sequence all the introns are being edited out. This may be introns are being edited out. This may be cumbersome and time consumingcumbersome and time consuming

An easier approach would be the direct translation of An easier approach would be the direct translation of the cDNA sequence into its corresponding protein the cDNA sequence into its corresponding protein sequencesequence


than the DNA (3)than the DNA (3)

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Typical structure of a Typical structure of a eukaryotic geneeukaryotic gene

TATA box

Transcription initiation

Initiation codon

Stop codon

AATAA

Poly (A)addition site

Exon 1 Exon 2 Exon 3 Flanking regionFlanking region

5' 3'

Intron I Intron II

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MULTIGENE FAMILIES :MULTIGENE FAMILIES : Organisms may contain many highly similar genes, Organisms may contain many highly similar genes,

while only one peptide sequence can be identified (e.g. while only one peptide sequence can be identified (e.g. histones, tubulins and GAPDH in humans). histones, tubulins and GAPDH in humans).

Using these DNA sequences, it would be difficult to Using these DNA sequences, it would be difficult to decide which are expressed and which not and thus decide which are expressed and which not and thus which genes to include in the analysis. which genes to include in the analysis.

Moreover, if all the genes that are expressed encode Moreover, if all the genes that are expressed encode the same protein, then DNA differences are not the same protein, then DNA differences are not significantsignificant



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PROTEIN IS THE UNIT OF SELECTION :PROTEIN IS THE UNIT OF SELECTION :

For protein-encoding genes, the object on which For protein-encoding genes, the object on which natural selection acts is the protein itself. natural selection acts is the protein itself.

The underlying DNA sequence reflects this process in The underlying DNA sequence reflects this process in combination with species-specific pressures on DNA combination with species-specific pressures on DNA sequence (like the need for aerophiles to have DNA sequence (like the need for aerophiles to have DNA that is GC richer). that is GC richer).

If function demands that a protein maintains a specific If function demands that a protein maintains a specific sequence, there still is room for the DNA sequence to sequence, there still is room for the DNA sequence to change. change.



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RNA EDITING :RNA EDITING : The DNA sequence doesn't always translate into amino The DNA sequence doesn't always translate into amino

acid sequence. acid sequence.

In post-translational editing non-coded amino acids are In post-translational editing non-coded amino acids are added or coded amino acids are removed in the editing added or coded amino acids are removed in the editing process. process.

This could lead to major differences in DNA sequence This could lead to major differences in DNA sequence (sometimes more than 50%) that nevertheless leads to (sometimes more than 50%) that nevertheless leads to roughly the same protein sequence after final editingroughly the same protein sequence after final editing



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Pan-editing of mitochondrial Pan-editing of mitochondrial RNA in KinetoplastidaRNA in Kinetoplastida

UCCuAuuA*AuUUUUUGuUA**UAuAGuuuuuuAA*UGUUGuuuGGuGuA*uuuuuuuAuUG*UGuuuAGuuuuGuuuuGuuGuuGuuuGuuuG****GUGuGuuAuuG**UUUUGAGAuuGuuGnote that the mature mRNA would not be able to hybridise with the gene present in the kinetoplast DNA and thus cannot be detected as such.

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Some good advice (1)Some good advice (1)

It is recommended to prepare the phylogenetic trees It is recommended to prepare the phylogenetic trees both ways (DNA and Protein) and see how they lookboth ways (DNA and Protein) and see how they look

For a group of species that are relatively close in time For a group of species that are relatively close in time and closely related (like viral proteins or vertebrate and closely related (like viral proteins or vertebrate enzymes), DNA-based analysis is probably a good enzymes), DNA-based analysis is probably a good way to go, since you avoid problems of codon bias way to go, since you avoid problems of codon bias and randomization of wobble bases. But check the and randomization of wobble bases. But check the protein anyway protein anyway

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Some good advice (2)Some good advice (2)

Be aware of the problems of multigene families (for instance Be aware of the problems of multigene families (for instance coding for isoenzymes) coding for isoenzymes)

Be careful when you decide to exclude or include such Be careful when you decide to exclude or include such sequences (you may compare paralogous rather than sequences (you may compare paralogous rather than orthologous sequences)orthologous sequences)

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Text available from: Text available from: [email protected] [email protected]

Text and slides:Text and slides: http://www.icp.be/~opperd/chapter8/http://www.icp.be/~opperd/chapter8/

Website:Website: http://www.icp.be/~opperd/private/proteins.htmlhttp://www.icp.be/~opperd/private/proteins.html

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For the creation of a phylogenetic tree a good alignment For the creation of a phylogenetic tree a good alignment of protein sequences is of vital importanceof protein sequences is of vital importance

Only homologous residues should be aligned with each Only homologous residues should be aligned with each otherother

Doubtful regions should not be included in the alignmentDoubtful regions should not be included in the alignment

Aligned sequences should have similar lengthsAligned sequences should have similar lengths

Alignment of two protein Alignment of two protein sequences (1)sequences (1)

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Dot-Matrix plotsDot-Matrix plots

Two homologous sequences with 81% identity Two homologous sequences with 50% identity

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Pair-wise alignment of two protein Pair-wise alignment of two protein sequences according to the ‘Dot-Matrix’ sequences according to the ‘Dot-Matrix’

methodmethodC D E G L D P G S E R K

CDEGLDPGSERK

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C D E P L D P G S Q R K

CDEGLDPGSERK

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•

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•

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•

•

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C D E L D P G S Q R K

CDEGLDPGSERK

••

•

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•

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•

•

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C D E D G L S Q L K

CDEGLDPLSERK

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A B

C D

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Alignment requires the user to make assumptions Alignment requires the user to make assumptions regarding relative costs of substitution versus insertions regarding relative costs of substitution versus insertions and deletions (indels).and deletions (indels).

If substitution cost >> gap penalty: there will be many If substitution cost >> gap penalty: there will be many short gaps and no phylogenetic information.short gaps and no phylogenetic information.

In general: search for maximum identity and minimize In general: search for maximum identity and minimize the number of insertions and deletions.the number of insertions and deletions.

Exclude regions that cannot be aligned unambiguously! Exclude regions that cannot be aligned unambiguously! Visual alignment is possible using the "dot-matrix Visual alignment is possible using the "dot-matrix

method"method"


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Identity matrix as used in Identity matrix as used in ClustalClustalC10,C10,

S 0, 10,S 0, 10,

T 0, 0, 10,T 0, 0, 10,

P 0, 0, 0, 10,P 0, 0, 0, 10,

A 0, 0, 0, 0, 10,A 0, 0, 0, 0, 10,

G 0, 0, 0, 0, 0, 10,G 0, 0, 0, 0, 0, 10,

N 0, 0, 0, 0, 0, 0, 10,N 0, 0, 0, 0, 0, 0, 10,

D 0, 0, 0, 0, 0, 0, 0, 10,D 0, 0, 0, 0, 0, 0, 0, 10,

E 0, 0, 0, 0, 0, 0, 0, 0, 10,E 0, 0, 0, 0, 0, 0, 0, 0, 10,

Q 0, 0, 0, 0, 0, 0, 0, 0, 0, 10,Q 0, 0, 0, 0, 0, 0, 0, 0, 0, 10,

H 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10,H 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10,

R 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10,R 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10,

K 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10,K 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10,

M 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10,M 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10,

I 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10,I 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10,

L 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10,L 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10,

V 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10,V 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10,

F 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10,F 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10,

Y 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10,Y 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10,

W 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10,W 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10,

C S T P A G N D E Q H R K M I L V F Y WC S T P A G N D E Q H R K M I L V F Y W

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Distance matrix withDistance matrix withmutation costs for amino acidsmutation costs for amino acids

A S G L K V T P E D N I Q R F Y C H M W Z B XA S G L K V T P E D N I Q R F Y C H M W Z B X

Ala = A 0 1 1 2 2 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2Ala = A 0 1 1 2 2 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2

Ser = S 1 0 1 1 2 2 1 1 2 2 1 1 2 1 1 1 1 2 2 1 2 2 2Ser = S 1 0 1 1 2 2 1 1 2 2 1 1 2 1 1 1 1 2 2 1 2 2 2

Gly = G 1 1 0 2 2 1 2 2 1 1 2 2 2 1 2 2 1 2 2 1 2 2 2Gly = G 1 1 0 2 2 1 2 2 1 1 2 2 2 1 2 2 1 2 2 1 2 2 2

Leu = L 2 1 2 0 2 1 2 1 2 2 2 1 1 1 1 2 2 1 1 1 2 2 2Leu = L 2 1 2 0 2 1 2 1 2 2 2 1 1 1 1 2 2 1 1 1 2 2 2

Lys = K 2 2 2 2 0 2 1 2 1 2 1 1 1 1 2 2 2 2 1 2 1 2 2Lys = K 2 2 2 2 0 2 1 2 1 2 1 1 1 1 2 2 2 2 1 2 1 2 2

Val = V 1 2 1 1 2 0 2 2 1 1 2 1 2 2 1 2 2 2 1 2 2 2 2Val = V 1 2 1 1 2 0 2 2 1 1 2 1 2 2 1 2 2 2 1 2 2 2 2

Thr = T 1 1 2 2 1 2 0 1 2 2 1 1 2 1 2 2 2 2 1 2 2 2 2Thr = T 1 1 2 2 1 2 0 1 2 2 1 1 2 1 2 2 2 2 1 2 2 2 2

Pro = P 1 1 2 1 2 2 1 0 2 2 2 2 1 1 2 2 2 1 2 2 2 2 2Pro = P 1 1 2 1 2 2 1 0 2 2 2 2 1 1 2 2 2 1 2 2 2 2 2

Glu = E 1 2 1 2 1 1 2 2 0 1 2 2 1 2 2 2 2 2 2 2 1 2 2Glu = E 1 2 1 2 1 1 2 2 0 1 2 2 1 2 2 2 2 2 2 2 1 2 2

Asp = D 1 2 1 2 2 1 2 2 1 0 1 2 2 2 2 1 2 1 2 2 2 1 2Asp = D 1 2 1 2 2 1 2 2 1 0 1 2 2 2 2 1 2 1 2 2 2 1 2

Asn = N 2 1 2 2 1 2 1 2 2 1 0 1 2 2 2 1 2 1 2 2 2 1 2Asn = N 2 1 2 2 1 2 1 2 2 1 0 1 2 2 2 1 2 1 2 2 2 1 2

Ile = I 2 1 2 1 1 1 1 2 2 2 1 0 2 1 1 2 2 2 1 2 2 2 2Ile = I 2 1 2 1 1 1 1 2 2 2 1 0 2 1 1 2 2 2 1 2 2 2 2

Gln = Q 2 2 2 1 1 2 2 1 1 2 2 2 0 1 2 2 2 1 2 2 1 2 2Gln = Q 2 2 2 1 1 2 2 1 1 2 2 2 0 1 2 2 2 1 2 2 1 2 2

Arg = R 2 1 1 1 1 2 1 1 2 2 2 1 1 0 2 2 1 1 1 1 2 2 2Arg = R 2 1 1 1 1 2 1 1 2 2 2 1 1 0 2 2 1 1 1 1 2 2 2

Phe = F 2 1 2 1 2 1 2 2 2 2 2 1 2 2 0 1 1 2 2 2 2 2 2Phe = F 2 1 2 1 2 1 2 2 2 2 2 1 2 2 0 1 1 2 2 2 2 2 2

Tyr = Y 2 1 2 2 2 2 2 2 2 1 1 2 2 2 1 0 1 1 3 2 2 1 2Tyr = Y 2 1 2 2 2 2 2 2 2 1 1 2 2 2 1 0 1 1 3 2 2 1 2

Cys = C 2 1 1 2 2 2 2 2 2 2 2 2 2 1 1 1 0 2 2 1 2 2 2Cys = C 2 1 1 2 2 2 2 2 2 2 2 2 2 1 1 1 0 2 2 1 2 2 2

His = H 2 2 2 1 2 2 2 1 2 1 1 2 1 1 2 1 2 0 2 2 2 1 2His = H 2 2 2 1 2 2 2 1 2 1 1 2 1 1 2 1 2 0 2 2 2 1 2

Met = M 2 2 2 1 1 1 1 2 2 2 2 1 2 1 2 3 2 2 0 2 2 2 2Met = M 2 2 2 1 1 1 1 2 2 2 2 1 2 1 2 3 2 2 0 2 2 2 2

Trp = W 2 1 1 1 2 2 2 2 2 2 2 2 2 1 2 2 1 2 2 0 2 2 2Trp = W 2 1 1 1 2 2 2 2 2 2 2 2 2 1 2 2 1 2 2 0 2 2 2

Glx = Z 2 2 2 2 1 2 2 2 1 2 2 2 1 2 2 2 2 2 2 2 1 2 2Glx = Z 2 2 2 2 1 2 2 2 1 2 2 2 1 2 2 2 2 2 2 2 1 2 2

Asx = B 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 1 2 1 2 2 2 1 2Asx = B 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 1 2 1 2 2 2 1 2

??? = X 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2??? = X 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

The distance table is generated by calculating the minimum number of base mutations required The distance table is generated by calculating the minimum number of base mutations required to convert an amino acid in row i to an amino acid in column j. Note Met->Tyr is the only to convert an amino acid in row i to an amino acid in column j. Note Met->Tyr is the only change that requires all 3 codon positions to change.change that requires all 3 codon positions to change.

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Hydrophobicity matrixHydrophobicity matrix R K D E B Z S N Q G X T H A C M P V L I Y F WR K D E B Z S N Q G X T H A C M P V L I Y F W

Arg = R 10 10 9 9 8 8 6 6 6 5 5 5 5 5 4 3 3 3 3 3 2 1 0Arg = R 10 10 9 9 8 8 6 6 6 5 5 5 5 5 4 3 3 3 3 3 2 1 0

Lys = K 10 10 9 9 8 8 6 6 6 5 5 5 5 5 4 3 3 3 3 3 2 1 0Lys = K 10 10 9 9 8 8 6 6 6 5 5 5 5 5 4 3 3 3 3 3 2 1 0

Asp = D 9 9 10 10 8 8 7 6 6 6 5 5 5 5 5 4 4 4 3 3 3 2 1Asp = D 9 9 10 10 8 8 7 6 6 6 5 5 5 5 5 4 4 4 3 3 3 2 1

Glu = E 9 9 10 10 8 8 7 6 6 6 5 5 5 5 5 4 4 4 3 3 3 2 1Glu = E 9 9 10 10 8 8 7 6 6 6 5 5 5 5 5 4 4 4 3 3 3 2 1

Asx = B 8 8 8 8 10 10 8 8 8 8 7 7 7 7 6 6 6 5 5 5 4 4 3Asx = B 8 8 8 8 10 10 8 8 8 8 7 7 7 7 6 6 6 5 5 5 4 4 3

Glx = Z 8 8 8 8 10 10 8 8 8 8 7 7 7 7 6 6 6 5 5 5 4 4 3Glx = Z 8 8 8 8 10 10 8 8 8 8 7 7 7 7 6 6 6 5 5 5 4 4 3

Ser = S 6 6 7 7 8 8 10 10 10 10 9 9 9 9 8 8 7 7 7 7 6 6 4Ser = S 6 6 7 7 8 8 10 10 10 10 9 9 9 9 8 8 7 7 7 7 6 6 4

Asn = N 6 6 6 6 8 8 10 10 10 10 9 9 9 9 8 8 8 7 7 7 6 6 4Asn = N 6 6 6 6 8 8 10 10 10 10 9 9 9 9 8 8 8 7 7 7 6 6 4

Gln = Q 6 6 6 6 8 8 10 10 10 10 9 9 9 9 8 8 8 7 7 7 6 6 4Gln = Q 6 6 6 6 8 8 10 10 10 10 9 9 9 9 8 8 8 7 7 7 6 6 4

Gly = G 5 5 6 6 8 8 10 10 10 10 9 9 9 9 8 8 8 8 7 7 6 6 5Gly = G 5 5 6 6 8 8 10 10 10 10 9 9 9 9 8 8 8 8 7 7 6 6 5

??? = X 5 5 5 5 7 7 9 9 9 9 10 10 10 10 9 9 8 8 8 8 7 7 5??? = X 5 5 5 5 7 7 9 9 9 9 10 10 10 10 9 9 8 8 8 8 7 7 5

Thr = T 5 5 5 5 7 7 9 9 9 9 10 10 10 10 9 9 8 8 8 8 7 7 5Thr = T 5 5 5 5 7 7 9 9 9 9 10 10 10 10 9 9 8 8 8 8 7 7 5

His = H 5 5 5 5 7 7 9 9 9 9 10 10 10 10 9 9 9 8 8 8 7 7 5His = H 5 5 5 5 7 7 9 9 9 9 10 10 10 10 9 9 9 8 8 8 7 7 5

Ala = A 5 5 5 5 7 7 9 9 9 9 10 10 10 10 9 9 9 8 8 8 7 7 5Ala = A 5 5 5 5 7 7 9 9 9 9 10 10 10 10 9 9 9 8 8 8 7 7 5

Cys = C 4 4 5 5 6 6 8 8 8 8 9 9 9 9 10 10 9 9 9 9 8 8 5Cys = C 4 4 5 5 6 6 8 8 8 8 9 9 9 9 10 10 9 9 9 9 8 8 5

Met = M 3 3 4 4 6 6 8 8 8 8 9 9 9 9 10 10 10 10 9 9 8 8 7Met = M 3 3 4 4 6 6 8 8 8 8 9 9 9 9 10 10 10 10 9 9 8 8 7

Pro = P 3 3 4 4 6 6 7 8 8 8 8 8 9 9 9 10 10 10 9 9 9 8 7Pro = P 3 3 4 4 6 6 7 8 8 8 8 8 9 9 9 10 10 10 9 9 9 8 7

Val = V 3 3 4 4 5 5 7 7 7 8 8 8 8 8 9 10 10 10 10 10 9 8 7Val = V 3 3 4 4 5 5 7 7 7 8 8 8 8 8 9 10 10 10 10 10 9 8 7

Leu = L 3 3 3 3 5 5 7 7 7 7 8 8 8 8 9 9 9 10 10 10 9 9 8Leu = L 3 3 3 3 5 5 7 7 7 7 8 8 8 8 9 9 9 10 10 10 9 9 8

Ile = I 3 3 3 3 5 5 7 7 7 7 8 8 8 8 9 9 9 10 10 10 9 9 8Ile = I 3 3 3 3 5 5 7 7 7 7 8 8 8 8 9 9 9 10 10 10 9 9 8

Tyr = Y 2 2 3 3 4 4 6 6 6 6 7 7 7 7 8 8 9 9 9 9 10 10 8Tyr = Y 2 2 3 3 4 4 6 6 6 6 7 7 7 7 8 8 9 9 9 9 10 10 8

Phe = F 1 1 2 2 4 4 6 6 6 6 7 7 7 7 8 8 8 8 9 9 10 10 9Phe = F 1 1 2 2 4 4 6 6 6 6 7 7 7 7 8 8 8 8 9 9 10 10 9

Trp = W 0 0 1 1 3 3 4 4 4 5 5 5 5 5 6 7 7 7 8 8 8 9 10Trp = W 0 0 1 1 3 3 4 4 4 5 5 5 5 5 6 7 7 7 8 8 8 9 10

Hydrophobicity scoring matrix constructed from hydrophilicity data (M.Levitt, J. Mol. Hydrophobicity scoring matrix constructed from hydrophilicity data (M.Levitt, J. Mol. Biol. 104, 59 [1976]), derived by George et al. 1990, Mutation Data Matrix and Its Biol. 104, 59 [1976]), derived by George et al. 1990, Mutation Data Matrix and Its Uses, Methods in Enzymology 183, 333.Uses, Methods in Enzymology 183, 333.

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1 PAM evolutionary distance1 PAM evolutionary distance

Ala Arg Asn Asp Cys Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr ValAla Arg Asn Asp Cys Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val

A R N D C Q E G H I L K M F P S T W Y VA R N D C Q E G H I L K M F P S T W Y V

Ala A 9867 2 9 10 3 8 17 21 2 6 4 2 6 2 22 35 32 0 2 18Ala A 9867 2 9 10 3 8 17 21 2 6 4 2 6 2 22 35 32 0 2 18

Arg R 1 9913 1 0 1 10 0 0 10 3 1 19 4 1 4 6 1 8 0 1Arg R 1 9913 1 0 1 10 0 0 10 3 1 19 4 1 4 6 1 8 0 1

Asn N 4 1 9822 36 0 4 6 6 21 3 1 13 0 1 2 20 9 1 4 1Asn N 4 1 9822 36 0 4 6 6 21 3 1 13 0 1 2 20 9 1 4 1

Asp D 6 0 42 9859 0 6 53 6 4 1 0 3 0 0 1 5 3 0 0 1Asp D 6 0 42 9859 0 6 53 6 4 1 0 3 0 0 1 5 3 0 0 1

Cys C 1 1 0 0 9973 0 0 0 1 1 0 0 0 0 1 5 1 0 3 2Cys C 1 1 0 0 9973 0 0 0 1 1 0 0 0 0 1 5 1 0 3 2

Gln Q 3 9 4 5 0 9876 27 1 23 1 3 6 4 0 6 2 2 0 0 1Gln Q 3 9 4 5 0 9876 27 1 23 1 3 6 4 0 6 2 2 0 0 1

Glu E 10 0 7 56 0 35 9865 4 2 3 1 4 1 0 3 4 2 0 1 2Glu E 10 0 7 56 0 35 9865 4 2 3 1 4 1 0 3 4 2 0 1 2

Gly G 21 1 12 11 1 3 7 9935 1 0 1 2 1 1 3 21 3 0 0 5Gly G 21 1 12 11 1 3 7 9935 1 0 1 2 1 1 3 21 3 0 0 5

His H 1 8 18 3 1 20 1 0 9912 0 1 1 0 2 3 1 1 1 4 1His H 1 8 18 3 1 20 1 0 9912 0 1 1 0 2 3 1 1 1 4 1

Ile I 2 2 3 1 2 1 2 0 0 9872 9 2 12 7 0 1 7 0 1 33Ile I 2 2 3 1 2 1 2 0 0 9872 9 2 12 7 0 1 7 0 1 33

Leu L 3 1 3 0 0 6 1 1 4 22 9947 2 45 13 3 1 3 4 2 15Leu L 3 1 3 0 0 6 1 1 4 22 9947 2 45 13 3 1 3 4 2 15

Lys K 2 37 25 6 0 12 7 2 2 4 1 9926 20 0 3 8 11 0 1 1Lys K 2 37 25 6 0 12 7 2 2 4 1 9926 20 0 3 8 11 0 1 1

Met M 1 1 0 0 0 2 0 0 0 5 8 4 9874 1 0 1 2 0 0 4Met M 1 1 0 0 0 2 0 0 0 5 8 4 9874 1 0 1 2 0 0 4

Phe F 1 1 1 0 0 0 0 1 2 8 6 0 4 9946 0 2 1 3 28 0Phe F 1 1 1 0 0 0 0 1 2 8 6 0 4 9946 0 2 1 3 28 0

Pro P 13 5 2 1 1 8 3 2 5 1 2 2 1 1 9926 12 4 0 0 2Pro P 13 5 2 1 1 8 3 2 5 1 2 2 1 1 9926 12 4 0 0 2

Ser S 28 11 34 7 11 4 6 16 2 2 1 7 4 3 17 9840 38 5 2 2Ser S 28 11 34 7 11 4 6 16 2 2 1 7 4 3 17 9840 38 5 2 2

Thr T 22 2 13 4 1 3 2 2 1 11 2 8 6 1 5 32 9871 0 2 9Thr T 22 2 13 4 1 3 2 2 1 11 2 8 6 1 5 32 9871 0 2 9

Trp W 0 2 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 9976 1 0Trp W 0 2 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 9976 1 0

Tyr Y 1 0 3 0 3 0 1 0 4 1 1 0 0 21 0 1 1 2 9945 1Tyr Y 1 0 3 0 3 0 1 0 4 1 1 0 0 21 0 1 1 2 9945 1

Val V 13 2 1 1 3 2 2 3 3 57 11 1 17 1 3 2 10 0 2 9901Val V 13 2 1 1 3 2 2 3 3 57 11 1 17 1 3 2 10 0 2 9901

[top row shows original amino acid; left column shows replacement amino acid][top row shows original amino acid; left column shows replacement amino acid]

Mutation probability matrix for the evolutionary distance of 1 PAM (i.e., one Accepted Point Mutation per 100 amino acids).Mutation probability matrix for the evolutionary distance of 1 PAM (i.e., one Accepted Point Mutation per 100 amino acids).

An element of this matrix, [Mij], gives the probability that the amino acid in column j will be replaced by the amino acid inAn element of this matrix, [Mij], gives the probability that the amino acid in column j will be replaced by the amino acid in

row i after a given evolutionary interval, in this case 1 PAM. Thus, there is a 0.56% probability that Asp will be replaced byrow i after a given evolutionary interval, in this case 1 PAM. Thus, there is a 0.56% probability that Asp will be replaced by

Glu. To simplify the appearance, the elements are shown multiplied by 10,000. (Adapted from Figure 82. Atlas of ProteinGlu. To simplify the appearance, the elements are shown multiplied by 10,000. (Adapted from Figure 82. Atlas of Protein

Sequence and Structure, Suppl 3, 1978, M.O. Dayhoff, ed. National Biomedical Research Foundation, 1979.)Sequence and Structure, Suppl 3, 1978, M.O. Dayhoff, ed. National Biomedical Research Foundation, 1979.)

PAM 1 mutation matrix PAM 1 mutation matrix

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PAM 100 matrix as used in PAM 100 matrix as used in ClustalClustalC 14,C 14,

S -1, 6,S -1, 6,

T -5, 2, 7,T -5, 2, 7,

P -6, 1, -1, 10,P -6, 1, -1, 10,

A -5, 2, 2, 1, 6,A -5, 2, 2, 1, 6,

G -8, 1, -3, -3, 1, 8,G -8, 1, -3, -3, 1, 8,

N -8, 2, 0, -3, -1, -1, 7,N -8, 2, 0, -3, -1, -1, 7,

D -11, -1, -2, -4, -1, -1, 4, 8,D -11, -1, -2, -4, -1, -1, 4, 8,

E -11, -2, -3, -3, 0, -2, 1, 5, 8,E -11, -2, -3, -3, 0, -2, 1, 5, 8,

Q -11, -3, -3, -1, -2, -5, -1, 1, 4, 9,Q -11, -3, -3, -1, -2, -5, -1, 1, 4, 9,

H -6, -4, -5, -2, -5, -7, 2, -1, -2, 4, 11,H -6, -4, -5, -2, -5, -7, 2, -1, -2, 4, 11,

R -6, -1, -4, -2, -5, -8, -3, -6, -5, 1, 1, 10,R -6, -1, -4, -2, -5, -8, -3, -6, -5, 1, 1, 10,

K -11, -2, -1, -4, -4, -5, 1, -2, -2, -1, -3, 3, 8,K -11, -2, -1, -4, -4, -5, 1, -2, -2, -1, -3, 3, 8,

M -11, -4, -2, -6, -3, -8, -5, -8, -6, -2, -7, -2, 1, 13,M -11, -4, -2, -6, -3, -8, -5, -8, -6, -2, -7, -2, 1, 13,

I -5, -4, -1, -6, -3, -7, -4, -6, -5, -5, -7, -4, -4, 2, 9,I -5, -4, -1, -6, -3, -7, -4, -6, -5, -5, -7, -4, -4, 2, 9,

L -12, -7, -5, -5, -5, -8, -6, -9, -7, -3, -5, -7, -6, 4, 2, 9,L -12, -7, -5, -5, -5, -8, -6, -9, -7, -3, -5, -7, -6, 4, 2, 9,

V -4, -4, -1, -4, 0, -4, -5, -6, -5, -5, -6, -6, -6, 1, 5, 1, 8,V -4, -4, -1, -4, 0, -4, -5, -6, -5, -5, -6, -6, -6, 1, 5, 1, 8,

F -10, -5, -6, -9, -7, -8, -6,-11,-11,-10, -4, -7,-11, -2, 0, 0, -5, 12,F -10, -5, -6, -9, -7, -8, -6,-11,-11,-10, -4, -7,-11, -2, 0, 0, -5, 12,

Y -2, -6, -6,-11, -6,-11, -3, -9, -7, -9, -1,-10,-10, -8, -4, -5, -6, 6, 13,Y -2, -6, -6,-11, -6,-11, -3, -9, -7, -9, -1,-10,-10, -8, -4, -5, -6, 6, 13,

W -13, -4,-10,-11,-11,-13, -8,-13,-14,-11, -7, 1, -9,-11,-12, -7,-14, -2, -2, 19,W -13, -4,-10,-11,-11,-13, -8,-13,-14,-11, -7, 1, -9,-11,-12, -7,-14, -2, -2, 19,


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PAM 250 matrix as used in PAM 250 matrix as used in ClustalClustal

C 12,C 12,

S 0, 2,S 0, 2,

T -2, 1, 3,T -2, 1, 3,

P -3, 1, 0, 6,P -3, 1, 0, 6,

A -2, 1, 1, 1, 2,A -2, 1, 1, 1, 2,

G -3, 1, 0,-1, 1, 5,G -3, 1, 0,-1, 1, 5,

N -4, 1, 0,-1, 0, 0, 2,N -4, 1, 0,-1, 0, 0, 2,

D -5, 0, 0,-1, 0, 1, 2, 4,D -5, 0, 0,-1, 0, 1, 2, 4,

E -5, 0, 0,-1, 0, 0, 1, 3, 4,E -5, 0, 0,-1, 0, 0, 1, 3, 4,

Q -5,-1,-1, 0, 0,-1, 1, 2, 2, 4,Q -5,-1,-1, 0, 0,-1, 1, 2, 2, 4,

H -3,-1,-1, 0,-1,-2, 2, 1, 1, 3, 6,H -3,-1,-1, 0,-1,-2, 2, 1, 1, 3, 6,

R -4, 0,-1, 0,-2,-3, 0,-1,-1, 1, 2, 6,R -4, 0,-1, 0,-2,-3, 0,-1,-1, 1, 2, 6,

K -5, 0, 0,-1,-1,-2, 1, 0, 0, 1, 0, 3, 5,K -5, 0, 0,-1,-1,-2, 1, 0, 0, 1, 0, 3, 5,

M -5,-2,-1,-2,-1,-3,-2,-3,-2,-1,-2, 0, 0, 6,M -5,-2,-1,-2,-1,-3,-2,-3,-2,-1,-2, 0, 0, 6,

I -2,-1, 0,-2,-1,-3,-2,-2,-2,-2,-2,-2,-2, 2, 5,I -2,-1, 0,-2,-1,-3,-2,-2,-2,-2,-2,-2,-2, 2, 5,

L -6,-3,-2,-3,-2,-4,-3,-4,-3,-2,-2,-3,-3, 4, 2, 6,L -6,-3,-2,-3,-2,-4,-3,-4,-3,-2,-2,-3,-3, 4, 2, 6,

V -2,-1, 0,-1, 0,-1,-2,-2,-2,-2,-2,-2,-2, 2, 4, 2, 4,V -2,-1, 0,-1, 0,-1,-2,-2,-2,-2,-2,-2,-2, 2, 4, 2, 4,

F -4,-3,-3,-5,-4,-5,-4,-6,-5,-5,-2,-4,-5, 0, 1, 2,-1, 9,F -4,-3,-3,-5,-4,-5,-4,-6,-5,-5,-2,-4,-5, 0, 1, 2,-1, 9,

Y 0,-3,-3,-5,-3,-5,-2,-4,-4,-4, 0,-4,-4,-2,-1,-1,-2, 7,10,Y 0,-3,-3,-5,-3,-5,-2,-4,-4,-4, 0,-4,-4,-2,-1,-1,-2, 7,10,

W -8,-2,-5,-6,-6,-7,-4,-7,-7,-5,-3, 2,-3,-4,-5,-2,-6, 0, 0,17, W -8,-2,-5,-6,-6,-7,-4,-7,-7,-5,-3, 2,-3,-4,-5,-2,-6, 0, 0,17,


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Matrices often used for the Matrices often used for the alignment of proteinsalignment of proteins

PAM 250 (Dayhoff et al., 1978)PAM 250 (Dayhoff et al., 1978) BLOSUM62 (Henikoff-Henikoff, 1992)BLOSUM62 (Henikoff-Henikoff, 1992) JTT (Jones et al., 1992)JTT (Jones et al., 1992) mtREV24 (Adachi-Hasegawa, 1996)mtREV24 (Adachi-Hasegawa, 1996) GONNET matrix (Gonnet et al., 1992)GONNET matrix (Gonnet et al., 1992)

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Multiple alignment of protein Multiple alignment of protein sequencessequences

For the construction of reliable phylogenetic trees the quality of a For the construction of reliable phylogenetic trees the quality of a multiple alignment is of the utmost importancemultiple alignment is of the utmost importance

There are many programs available for the multiple alignment of There are many programs available for the multiple alignment of proteins. proteins. – A good program in the public domain is: A good program in the public domain is: ClustalWClustalW – A similar program is A similar program is PileupPileup of the GCG package of the GCG package

They quickly align sequence pairs and roughly determine the degrees They quickly align sequence pairs and roughly determine the degrees of identity between each pairof identity between each pair

Then the sequences are aligned more precisely in a progressive way Then the sequences are aligned more precisely in a progressive way starting with the two closest sequencesstarting with the two closest sequences

Most programs work best when the sequences have similar length.Most programs work best when the sequences have similar length.

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Some rules of thumb for the Some rules of thumb for the manual alignment of proteins (1)manual alignment of proteins (1)

An automatically produced multiple alignment often An automatically produced multiple alignment often needs manual adjustment to improve the quality of needs manual adjustment to improve the quality of the alignment. the alignment.

Such improvement can be obtained by using all the Such improvement can be obtained by using all the knowledge that is available about a protein. knowledge that is available about a protein.

If a structure is available you should use the detailed If a structure is available you should use the detailed information about secondary structure for the information about secondary structure for the alignment. alignment.

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The rules for mutation of amino acids are dependent The rules for mutation of amino acids are dependent on their physicochemical properties.on their physicochemical properties.

SurfaceSurface residues ( residues (DRENKDRENK) are preferably mutated to ) are preferably mutated to residues of similar properties. Since they are not, or residues of similar properties. Since they are not, or less, involved in protein folding they mutate rather less, involved in protein folding they mutate rather easily.easily.

HydrophobicHydrophobic residues ( residues (FAMILYVWFAMILYVW) are preferentially ) are preferentially replaced by other hydrophobic ones. These residues replaced by other hydrophobic ones. These residues are mainly internal and determine the folding of the are mainly internal and determine the folding of the protein. They thus mutate rather slowly.protein. They thus mutate rather slowly.


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The residues The residues CHQSTCHQST are indifferent and may be are indifferent and may be replaced with any other type of residuereplaced with any other type of residue

The residues (The residues (DRENKDRENKCHQSTCHQST), when conserved ), when conserved throughout the alignment are very likely residues that throughout the alignment are very likely residues that are involved in the active site. So the multiple are involved in the active site. So the multiple alignment should be adjusted accordinglyalignment should be adjusted accordingly

Periodicity of charged residues may provide Periodicity of charged residues may provide information as to the presence of elements of information as to the presence of elements of secondary structure such as secondary structure such as -helices and -helices and -strands-strands


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-helix-helix

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-strand-strand

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IndelsIndels (insertions/deletions) are never found in (insertions/deletions) are never found in elements of secondary structure but only in loops. elements of secondary structure but only in loops.

ProPro and and GlyGly interfere with secondary structure interfere with secondary structure elements and thus have a preference for loopselements and thus have a preference for loops

HydrophobicityHydrophobicity (or hydropathy) profiles according to (or hydropathy) profiles according to Kyte and Doolittle of two homologous proteins are in Kyte and Doolittle of two homologous proteins are in general strikingly similargeneral strikingly similar


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Proline interferes with Proline interferes with -helix -helix and and -sheet formation-sheet formation

From Deber and Therien,2002

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Possible functions for proline Possible functions for proline in trans-membrane domainsin trans-membrane domains

From Deber and Therien,2002

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Alignment of malate dehydrogenase sequencesAlignment of malate dehydrogenase sequences

Slcl|CHR34_tmp.0150 ----MKPST--LSRFKVTVLGASGAIGQPLALALVQNKRVSEL-----ALYDIVQPR---lcl|CHR34_tmp.0140 ----MRRSQ--GCFFRVAVLGAAGGIGQPLSLLLKNNKYVKEL-----KLYDVKGGP---lcl|CHR34_tmp.0130 MGLLFRRSLTALKKGKVVLFGCSNAVGQPLSLLLKMNPHVEELVCCNTAADDDVPGS---lcl|CHR28_tmp.0050 -----------MSAVKVAVTGAAGQIGYALVPLIARGALLGPTTPVELRLLDIEPALKAL . . :*.: *.:. :* .* : . : : *

lcl|CHR34_tmp.0150 -GVAVDLSHFPRKVKVTGYPTKWIHK--ALDGADLVLMSAGMPRRPGMT-HDDLFNTNALlcl|CHR34_tmp.0140 -GVAADLSHICAPAKVTGYTKDELSR--AVENADVVVIPAGIPRKPGMT-RDDLFNTNASlcl|CHR34_tmp.0130 -GIAADLSHIDTLPKVH-YATDEGQWPALLRDAQLILVCFGSSFDLLREDRDIALKAAAPlcl|CHR28_tmp.0050 AGVEAELEDCAFPLLDKVVVTADPRV--AFDGVAIAIMCGAFPRKAGME-RKDLLEMNAR *: .:*.. . . .. : :: . . ::. :: *

lcl|CHR34_tmp.0150 TVNELSAAVARYAPKSV-LAIISNPLNSMVPVAAETLQRAGVYDPRKLFGIISLNMMRARlcl|CHR34_tmp.0140 IVRDLAIAVGTHAPKAI-VGIITNPVNSTVPVAAEALKKVGVYDPARLFGVTTLDVVRARlcl|CHR34_tmp.0130 TMRRVMAAVASSDTTGN-VAVVSSPVNALTPFCAELLKASGKFDPRKLFGVTTLDVIRTRlcl|CHR28_tmp.0050 IFKEQGEAIAAVAASDCRVVVVGNPANTNALILLKSAQ--GKLNPRHVTAMTRLDHNRAL .. *:. .. : :: .* *: . . : : * :* :: .: *: *:

lcl|CHR34_tmp.0150 KMLGDFTGQDPEMLDVPVIGGHSGQTIVPLFSHS--GVELRQEQVEYLTHRVR-------lcl|CHR34_tmp.0140 TFVAEALGASPYDVDVPVIGGHSGETIVPLLSG---FPSLSEEQVRQLTHRIQ-------lcl|CHR34_tmp.0130 KLVAGTLHMNPYDVNVPVVGGCGGVTACPLIAQT--GLRIPLDDIVRISGEVQSYGVLFElcl|CHR28_tmp.0050 SLLARKAGVPVSQVRNVIIWGNHSSTQVPDTDSAVIGTTPAREAIKDDALDDD-----FV .::. : :: * . * * : : : .

lcl|CHR34_tmp.0150 --VGGD-EVVKAKEGRGSSSLSMAFAAAEWADGVLRAMDGEKTLLQCSFVESPLFADKCRlcl|CHR34_tmp.0140 --FGGD-EVVKAKDGAGSATLSMAFAGNEWTTAVLRALSGEKGVVVCTYVQS-TVEPSCAlcl|CHR34_tmp.0130 AAVGADSHDALSTEVAPPVALGLAYAACDFSTSLLKALRGDVGIVECALVES-TMRSETPlcl|CHR28_tmp.0050 QVVRGRGAEIIQLRGLSSAMSAAKAAVDHVHDWIHGTPEGVYVSMGVYSDENPYGVPSGL . . . . * . : : * : :. .

lcl|CHR34_tmp.0150 FFGSTVEVCKEGIERVLPLPPLNEYEEEQLDRCLPDLEKN-IRKGLAFVAENAATSTPSTlcl|CHR34_tmp.0140 FFSSPVLLGNSGVEKIYPVPMLNAYEEKLMAKCLEGLQSN-ITKGIAFSNK---------lcl|CHR34_tmp.0130 FFSSRVELGREGVQRVFPMGALTSYEHELIETAVPELMRD-VQAGIEAATQF--------lcl|CHR28_tmp.0050 IFSFP-CTCHAGEWTVVSGKLNGDLGKQRLASTIAELQEERAQAGL-------------- :*. . * : . .: : : * : *: :

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Hydrophobicity profilesHydrophobicity profiles Profiles according to Kyte and Doolittle of homologous proteins are in Profiles according to Kyte and Doolittle of homologous proteins are in

general strikingly similar and may provide a tool in the alignment of general strikingly similar and may provide a tool in the alignment of two or more proteins. two or more proteins.

The two phosphoglycerate kinase sequences below share 50% The two phosphoglycerate kinase sequences below share 50% identical residues.identical residues.

Trypanosoma congolense PGK Euglena gracilis PGK

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Tree construction methods (1)Tree construction methods (1) Distance matrix methodsDistance matrix methods

– Cluster analysis (UPGMA, WPGMA, etc)Cluster analysis (UPGMA, WPGMA, etc)– Fitch & Margoliash (1967)Fitch & Margoliash (1967)– Transformed distance methods (eg. Li, 1981)Transformed distance methods (eg. Li, 1981)– Neighbor-joiningNeighbor-joining (Saitou & Nei, 1987) (Saitou & Nei, 1987)– ...many more...many more

Parsimony methodsParsimony methods– Maximum parsimonyMaximum parsimony

Other methodsOther methods– Maximum likelihoodMaximum likelihood (Felsenstein, 1981) (Felsenstein, 1981)– ... many more... many more

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Character-based methods: Character-based methods: – maximum parsimonymaximum parsimony – maximum likelihoodmaximum likelihood

Non-character-based methods: Non-character-based methods: – distance matrix methodsdistance matrix methods

Tree construction methods (2)Tree construction methods (2)

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Phylogeny (2)Phylogeny (2) Distance Matrix methods (in the public domain)Distance Matrix methods (in the public domain)

– Least squares method (Fitch and Margoliash)Least squares method (Fitch and Margoliash) —Fitch, KitschFitch, Kitsch of the Phylip package (Jo Felsentein, Univ. Washington) of the Phylip package (Jo Felsentein, Univ. Washington)

– Neighbor-joining methodNeighbor-joining method—NeighborNeighbor of the Phylip package (Jo Felsentein, Univ. Washington) of the Phylip package (Jo Felsentein, Univ. Washington) —ClustalClustal, or , or DistnjDistnj in Protml package (Adachi and Hasegawa, Univ. in Protml package (Adachi and Hasegawa, Univ.

Tokyo)Tokyo)—DarwinDarwin (Gaston Gonner, ETH, Zurich, via mailserver or WWW) (Gaston Gonner, ETH, Zurich, via mailserver or WWW)

Protein Maximum likelihood (in the public domain)Protein Maximum likelihood (in the public domain)– ProtmlProtml (Adachi and Hasegawa, Univ. Tokyo) (very cpu intensive) (Adachi and Hasegawa, Univ. Tokyo) (very cpu intensive)– TreePuzzleTreePuzzle (Strimmer and von Haeseler, 1997) (Strimmer and von Haeseler, 1997)

Protein maximal parsimony (in the public domain)Protein maximal parsimony (in the public domain)— ProtparsProtpars (Jo Felsentein, Univ. Washington) (Jo Felsentein, Univ. Washington) — PaupPaup (David Swofford, latest version will be commercial) (David Swofford, latest version will be commercial)

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Some useful information Some useful information about phylogenetic treesabout phylogenetic trees

A

B

C

D

E

F

G

H

I

OTUs

Root

External nodes

Internalnodes

A-E are external nodes (extant)F-I are internal (ancestral) nodes

OTUs are operational taxonomic unitsThey can be: species

populationsindividualsgenesproteinsThey are the extant (existing) or extinct

(ancestral) OTUs

Topology: order of the nodes on the tree

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Distance Matrix MethodsDistance Matrix Methods UPGMAUPGMA (Unweighted Pair Group with Arithmatic Mean) uses real (Unweighted Pair Group with Arithmatic Mean) uses real

(uncorrected) distance values and a sequential clustering (uncorrected) distance values and a sequential clustering algorithm. (Should only be used with closely related OTUs, or algorithm. (Should only be used with closely related OTUs, or when there is constancy of evolutionary rate)when there is constancy of evolutionary rate)

Transformed distance methodsTransformed distance methods. Corrections may be introduced . Corrections may be introduced to obtain trees with true evolutionary distances (PAM values, to obtain trees with true evolutionary distances (PAM values, Kimura), or corrections are carried out with reference to an Kimura), or corrections are carried out with reference to an outgroup (Farris, 1971; Klotz et al, 1979). Should be used when outgroup (Farris, 1971; Klotz et al, 1979). Should be used when evolutionary distant organisms are included in the datasetevolutionary distant organisms are included in the dataset

Neighbors relation methodsNeighbors relation methods

– FITCH (Fitch, 1981)FITCH (Fitch, 1981)

– Neighbor-Joining method, (Saitou and Nei, 1987) Neighbor-Joining method, (Saitou and Nei, 1987)

Should all be used with corrected (see above) distance Should all be used with corrected (see above) distance matricesmatrices

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Distance matrixDistance matrixUncorrected for Multiple Substitutions

1 2 3 4 5 0.00 0.63 0.63 22.88 18.50 AC007866_13 1 0.00 0.63 22.57 18.50 AC007866_17 2 0.00 22.88 17.87 AC007866_15 3 0.00 5.64 AC007866_9 4 0.00 AC007866_11 5Using the Kimura correction methodGap weighting is 0.000000

1 2 3 4 5 0.00 0.63 0.63 27.35 21.29 AC007866_13 1 0.00 0.63 26.90 21.29 AC007866_17 2 0.00 27.35 20.47 AC007866_15 3 0.00 5.88 AC007866_9 4 0.00 AC007866_11 5

Distance matrix as produced by the EMBOSS program distmat

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UPGMAUPGMA

UPGMAUPGMA (Unweighted Pair (Unweighted Pair Group with Arithmetic Mean) Group with Arithmetic Mean) uses real (uncorrected) uses real (uncorrected) distance values and a distance values and a sequential clustering sequential clustering algorithm. (Should only be algorithm. (Should only be used with closely related used with closely related OTUs, or when there is OTUs, or when there is constancy of evolutionary constancy of evolutionary rate)rate)

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Tree construction (UPGMA)Tree construction (UPGMA)

First cycle

A B C D E

B 2 C 4 4 D 6 6 6 E 6 6 6 4 F 8 8 8 8 8

Cluster the pair of OTUs with the smallest distance, being A and B, The branching point is positioned at a distance of 2 / 2 = 1 substitution.

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Following the first clustering A and B are considered as a single Following the first clustering A and B are considered as a single composite OTU(A,B) and we now calculate the new distance matrix composite OTU(A,B) and we now calculate the new distance matrix as follows:as follows:

dist(A,B),C = (distAC + distBC) / 2 = 4dist(A,B),C = (distAC + distBC) / 2 = 4

dist(A,B),D = (distAD + distBD) / 2 = 6dist(A,B),D = (distAD + distBD) / 2 = 6

dist(A,B),E = (distAE + distBE) / 2 = 6dist(A,B),E = (distAE + distBE) / 2 = 6

dist(A,B),F = (distAF + distBF) / 2 = 8dist(A,B),F = (distAF + distBF) / 2 = 8

In other words the distance between a simple OTU and a composite In other words the distance between a simple OTU and a composite OTU is the average of the distances between the simple OTU and OTU is the average of the distances between the simple OTU and the constituent simple OTUs of the composite OTU. Then a new the constituent simple OTUs of the composite OTU. Then a new distance matrix is recalculated using the newly calculated distances distance matrix is recalculated using the newly calculated distances and the whole cycle is being repeated:and the whole cycle is being repeated:


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Second cycleSecond cycle

A,BA,B C C D D E E

CC 4 4

DD 6 6 6 6

EE 6 6 6 6 44

FF 8 8 8 8 8 8 8 8

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Third cycleThird cycle

A,BA,B C C D,E D,E

CC 44

D,ED,E 6 6 6 6

FF 8 8 8 8 8 8

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Tree construction (UPGMA 4)Tree construction (UPGMA 4)

Fourth cycleFourth cycle

AB,CAB,C D,E D,E

D,ED,E 66

FF 8 8 8 8

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Fifth cycleFifth cycle

ABC,DEABC,DE

FF 88

The final step consists of clustering the last OTU, The final step consists of clustering the last OTU, F,with the composite OTU.F,with the composite OTU.

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Pitfalls of UPGMAPitfalls of UPGMA

The UPGMA clustering method is very The UPGMA clustering method is very sensitive to unequal evolutionary rates. sensitive to unequal evolutionary rates.

Clustering works only if the data are Clustering works only if the data are ultrametric ultrametric

Ultrametric distances are defined by the Ultrametric distances are defined by the satisfaction of the 'three-point condition'.satisfaction of the 'three-point condition'.

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The treepoint conditionThe treepoint condition For any three taxa: dist AC <= max (distAB, distBC) or, For any three taxa: dist AC <= max (distAB, distBC) or, in words: the two greatest distances are equal, or in words: the two greatest distances are equal, or UPGMA assumes that the evolutionary rate is the same for UPGMA assumes that the evolutionary rate is the same for

all branchesall branches If the assumption of rate constancy among lineages does If the assumption of rate constancy among lineages does

not hold UPGMA may give an erroneous topology.not hold UPGMA may give an erroneous topology.

Non-ultrametric tree

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Unequal rates of mutation Unequal rates of mutation lead to wrong treeslead to wrong trees

UPGMA tree construction based on the data of the UPGMA tree construction based on the data of the left tree would result in the erroneous tree at the left tree would result in the erroneous tree at the rightright

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UPGMA (conclusion)UPGMA (conclusion)

UPGMA uses real (uncorrected) distance values and UPGMA uses real (uncorrected) distance values and a sequential clustering algorithm. a sequential clustering algorithm.

This method of tree construction is very sensitive to This method of tree construction is very sensitive to differences in branch length or unequal rates of differences in branch length or unequal rates of evolution. evolution.

It should only be used with closely related OTUs, or It should only be used with closely related OTUs, or when there is constancy of evolutionary rate. when there is constancy of evolutionary rate.

The method is often used in combination with The method is often used in combination with isoenzyme or restriction site data or with isoenzyme or restriction site data or with morphological criteria morphological criteria

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Use Use sequence informationsequence information rather than distance rather than distance informationinformation

Calculate for Calculate for all possible treesall possible trees the tree that represents the tree that represents the the minimum number of substitutionsminimum number of substitutions at each informative at each informative sitesite

Maximum Parsimony MethodsMaximum Parsimony Methods

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Maximum Parsimony analysis (2)

Parsimony implies that simpler hypotheses are preferable to more complicated ones.

Maximum parsimony is a character-based method that infers a phylogenetic tree by minimizing the total number of evolutionary steps required to explain a given set of data, or in other words by minimizing the total tree length.

The steps may be base or amino-acid substitutions for sequence data, or gain and loss events for restriction site data.

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Maximum parsimony, when applied to protein sequence data either considers each site of the sequence as a multistate unordered characterd with 20 possible states (the amino-acids) (Eck and Dayhoff, 1966), or may take into account the genetic code and the number of mutations, 1, 2 or 3, that is required to explain an observed amino-acid substitution. The latter method is implemented in the PROTPARS program (Felsenstein, 1993).

The maximum parsimony method searches all possible tree topologies for the optimal (minimal) tree. However, the number of unrooted trees that have to be analysed rapidly increases with the number of OTUs.


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The number of rooted trees (Nr) for n OTUs is given by:Nr = (2n -3)!/(2exp(n -2)) (n -2)!

The number of unrooted trees (Nr) for n OTUs is given by:Nu = (2n -5)!/(2exp(n -3)) (n -3)!


Number of OTUs unrooted trees rooted trees 2 1 1 3 1 3 4 3 15 5 15 105 6 105 945 7 954 10,395 8 10,395 135,135 9 135,135 34,459,425 10 34,459,425 2.13E15 15 2.13E15 8.E21

This rapid increase in number of trees to be analysed may make it impossible to apply the method to very large datasets. In that case the parsimony method may become very time consuming, even on very fast computers.

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maximum parsimony method for 4 nucleic-acid sequences

Site _________________________ Sequence 1 2 3 4 5 6 7 8 9

1 A A G A G T G C A 2 A G C C G T G C G 3 A G A T A T C C A 4 A G A G A T C C G

For four OTUs there are three possible unrooted trees. The trees are then analysed by searching for the ancestral sequences and by counting the number of mutations required to explain the respective trees :

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(1) AAGAGTGCA AGATATCCA (3) \4 2/ Number of mutations \ 4 / AGCCGTGCG --- AGAGATCCG Tree I: 11 / \ /0 0\ (2) AGCCGTGCG AGAGATCCG (4)

(1) AAGAGTGCA AGCCGTGCG (2) \1 3/ \ 5 / AGGAGTGCA --- AGAGGTCCG Tree II: 14 / \ /4 1\ (3) AGATATCCA AGAGATCCG (4)

(1) AAGAGTGCA AGCCGTGCG (2) \1 3/ \ 5 / AGAAGTGCA --- AGATGTCCG Tree III: 16 / \ /5 2\ (4) AGAGATCCG AGATATCCA (3)

Tree I has the topology with the least number of mutations and thus is the most parsimonious tree.

Ancestral trees are calculated

This analysis includes both informative and non-informative sites in the sequence.

When only informative sites are included a much lesser number of sites can be analysed, which means in the case of large datasets a considerable gain in CPU time.

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Informative sitesInformative sitesA site is informative only when there are at least two different kinds of nucleotides at the site, each of which is represented in at least two of the sequences under study.

Site _________________________ Sequence 1 2 3 4 5 6 7 8 9

1 A A G A G T G C A 2 A G C C G T G C G 3 A G A T A T C C A 4 A G A G A T C C G * * *

Informative sites are indicated by an asterisk (*)

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1 GGA 2 GGG 3 ACA 4 ACG ***

(1) GGA ACA (3) \1 1/ Number of mutations \ 2 / GGG --- ACG Tree I: 4 / \ /0 0\ (2) GGG ACG (4)

(1) GGA GGG (2) \1 1/ \ 1 / GCA --- GCG Tree II: 5 / \ /1 1\ (3) ACA ACG (4)

(1) GGA GGG (2) \2 1/ \ 0 / GCG --- GCG Tree III: 6 / \ /1 2\ (4) ACG ACA (3)

To infer a maximum parsimony tree, for each possible tree we calculate the minimum number of substitutions at each informative site. In the above example, for sites 5, 7, and 9, tree I requires in total 4 changes, tree II requires 5 changes, and tree III requires 6 changes. In the final step, we sum the number of changes over all the informative sites for each tree and choose the tree associated with the smallest number of substitutions. In our case, tree I is chosen because it requires the smallest number of changes (4) at the informative sites.

Informative sites onlyInformative sites only

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How to find the best tree ?How to find the best tree ? Maximum parsimony searches for the optimal (minimal) tree. In this process more

than one minimal trees may be found. In order to guarantee to find the best possible tree an exhaustive evaluation of all possible tree topologies has to be carried out. However, this becomes impossible when there are more than 12 OTUs in a dataset.

Branch and Bound: is a variation on maximum parsimony that garantees to find the minimal tree without having to evaluate all possible trees. This way a larger number of taxa can be evaluated but the method is still limited.

Heuristic searches is a method with step-wise addition and rearrangement (branch swapping) of OTUs. Here it is not guaranteed to find the best tree.

Since, in view of the size of the dataset, it is often not possible to carry out an exhaustive or other search for the best tree, it is adviced to change the order of the taxa in the dataset and to repeat the analysis, or to indicate to the program to do this for you by providing a so-called jumble factor to the program.

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Consensus tree Since the Maximum Parsimony method may result in more than one equally

parsimonious tree, a consensus tree should be created. For the creation of a consensus tree see bootstrapping.

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Parsimony and branch lengthsParsimony and branch lengths(1) G A (3) \1 0/ \ 1 / C -----A / \ /0 1\ (2) C T (4)

(1) G A (3) \0 1/ \ 1 / G -----T / \ /1 0\ (2) C T (4)

(1) G A (3) \1 1/ \ 1 / C -----A / \ /0 0\ (2) C A (4)

3 possible trees for 4 OTUs, all describe the same final state by assuming a total of 3 steps.

Each final state is arrived at via a different route.

Each of the three trees is equally valid, but the number of steps along the indiviual branches (or the length of each branch) is not determined.

For this reason branch lengths are not given in parsimony, but only the total number of steps for a tree.

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Some final notes on maximum parsimony

Maximum Parsimony (positive points): – is based on shared and derived characters. It therefore is a cladistic rather

than a phenetic method – does not reduce sequence information to a single number – tries to provide information on the ancestral sequences – evaluates different trees

Maximum Parsimony (negative points): – does not assume an evolutionary model– is slow in comparison with distance methods – does not use all the sequence information (only informative sites are used) – does not correct for multiple mutations (does not imply a model of

evolution) – does not provide information on the branch lengths – is notorious for its sensitivity to codon bias

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How to root an unrooted tree?How to root an unrooted tree? The majority of methods yield unrooted treesThe majority of methods yield unrooted trees To root a tree one should add an outgroup to the dataset. An outgroup is To root a tree one should add an outgroup to the dataset. An outgroup is

an OTU for which external information (eg. paleontological information) is an OTU for which external information (eg. paleontological information) is available that indicates that the outgroup branched off before all other taxa available that indicates that the outgroup branched off before all other taxa

Do not choose an outgroup that is very distantly related to your taxa. This Do not choose an outgroup that is very distantly related to your taxa. This may result in serious topolocical errorsmay result in serious topolocical errors

Do not choose either an outgroup that is too closely related to the taxa in Do not choose either an outgroup that is too closely related to the taxa in question. In this case it may not be a true outgroupquestion. In this case it may not be a true outgroup

The use of more than one outgroup generally improves the estimate of tree The use of more than one outgroup generally improves the estimate of tree topologytopology

In the absence of a good outgroup the root may be positioned by assuming In the absence of a good outgroup the root may be positioned by assuming approximately equal evolutionary rates over all the branches. In this way approximately equal evolutionary rates over all the branches. In this way the root is put at the midpoint of the longest pathway between two OTUsthe root is put at the midpoint of the longest pathway between two OTUs

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Maximum likelihood It evaluates a hypothesis about evolutionary history in terms of the

probability that the proposed model and the hypothesized history would give rise to the observed data set. A history with a higher probability of reaching the observed state is preferred to a history with a lower probability. The method searches for the tree with the highest probability or likelihood.

The following programs are available from the web:– DNAML (DNA data only. By Joe Felsenstein in the Phylip package) – FastDNAML (DNA data only. A faster algorithm applied by Gary

Olsen to Joe Felsenstein's DNAML program ) – ProtML (DNA and protein. By Adachi and Hasegawa, 1992) – TreePuzzle (DNA and protein. By Strimmer and von Haeseler, 1995).

This program applies a heuristic method and is much faster than PROTML, but does not guarantee to find the best tree.

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Advantages and disadvantages of the maximum likelihood method

There are some supposed adavantages of maximum likelihood methods over other methods.

– It is the estimation method least affected by sampling error – It is robust to many violations of the assumptions in the evolutionary model – with very short sequences it tends to outperform alternative methods such

as parsimony or distance methods. – the method is statistically well founded – evalutates different tree topologies – uses all the sequence information

There are also some supposed disadvantages – maximum likelihood is very CPU intensive and thus extremely slow – result is dependent on the model of evolution used

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Explication of the methodExplication of the methodMaximum likelihood evaluates the probability that the choosen evolutionary model will have generated the observed sequences. Phylogenies are then inferred by finding those trees that yield the highest likelihood. Assume that we have the aligned nucleotide sequences for four taxa:

1 j ....N (1) A G G C U C C A A ....A (2) A G G U U C G A A ....A (3) A G C C C A G A A.... A (4) A U U U C G G A A.... C

and we want to evauate the likelihood of the unrooted tree represented by the nucleotides of site j in the sequence and shown below:

(1) (2) \ / \ / ------ / \ / \ (3) (4) What is the probabliity that this tree would have generated the data presented in the sequence under the the chosen model ?

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The models are time-reversible, therefore the likelihood of the tree is independent of the position of the root. Thus it is convenient to root the tree at an arbitrary internal node.

C C A G \ / | / \/ | / A | / \ | / \ | / A

_ _ | C C A G | | \ / | / | | \/ | / |L(j) = Sum(Prob | (5) | / |) | \ | / | | \ | / | |_ (6) _|

Assume that nucleotide sites evolve independently (the Markovian model of evolution). Then we can calculate the likelihood for each site separately and combine these to the total likelihood.

For the likelihood for site j, we have to consider all the possible scenarios by which the nucleotides present at the tips of the tree could have evolved. So the likelihood for a particular site is the summation of the probablilities of every possible reconstruction of ancestral states, given some model of base substitution. So in this specific case all possible nucleotides A, G, C, and T occupying nodes (5) and (6), or 4 x 4 = 16 possibilities :

In the case of protein sequences each site may ooccupy 20 states (that of the 20 amino acids) an thus 400 possibilities have to be considered. Since any one of these scenarios could have led to the amino-acid configuration at the tip of the tree, we must calculate the probability of each and sum and sum them to obtain the total probability for each site j.

Likelihood for one siteLikelihood for one site

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likelihood for the full treelikelihood for the full tree

The likelihood for the full tree then is the product of the likelihood at each site.

N L= L(1) x L(2) ..... x L(N) = L(j) j=1

Since the individual likelihoods are extremely small numbers it is convenient to sum the log likelihoods at each site and report the likelihood of the entire tree as the log likelihood.

N ln L= ln L(1) + ln L(2) ..... + ln L(N) = ln L(j) j=1

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The model of evolutionThe model of evolution

The PROTML program in the MOLPHY package (Adachi and Hasegawa, 1992), as well as the TreePUZZLE program by Strimmer and von Haeseler (1995), have implemented an instantaneous rate matrix derived from the Dayhoff emperical substitution matrix. This has been called the Dayhoff model.

Recently a model called the JTT model of evolution and based upon the updated emperical substitution matrix of Jones et al. (1992) has been developed and and implemented in these programs.

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The maximum likelihood treeThe maximum likelihood tree

The above procedure is then repeated for all The above procedure is then repeated for all possible topologies (or for all possible trees).possible topologies (or for all possible trees).

The tree with the highest probablility is the The tree with the highest probablility is the tree with the highest maximum likelihood.tree with the highest maximum likelihood.

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Bootstrapping Bootstrapping is a way of testing the reliability of the dataset. It is the creation of

pseudoreplicate datasets by resampling. Bootstrapping allows you to assess whether the distribution of characters has been influenced by stochastic effects. In phylogenetic analyses nonparametric bootstrapping is the most commonly used method. The pseudoreplicate datasets are generated by randomly sampling the original character matrix to create new matrices of the same size as the original. The frequency with which a given branch is found is recorded as the bootstrap proportion. These proportions can be used as a measure of the reliability (within limitations) of individual branches in the optimal tree.

Thus bootstrap analysis:– is a statistical method for obtaining an estimate of error – is used to evaluate the reliability of a tree – is used to examine how often a particular cluster in a tree appears when nucleotides

or aminoacids are resampled

NB: If the entire dataset is compatible and has not been biased by stochastic effects, all bootstrap trees should in principle have the same topology!

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The practice of bootstrapping and the construction of a consensus tree

Take a dataset consisting of in total n sequences with m sites each (see below). A number of resampled datasets of the same size (n x m) as the original dataset is produced. However, each site is sampled at random and no more sites are sampled than there were original sites. In order to be statistically significant the number of the datasets should should be high and equal or higher than the number of individual sites present in the dataset.

Our example dataset consists of in total 4 sequences with 10 sites each (see below). When three new datasets are prepared by random sampling of sites, the following three sample sets of data can be obtained:

Sample 1 0 1 2 0 3 0 1 2 0 1 (<- number of times each site is sampled) ___________________ A A G G C U C C A A A A G G G U U U C A A A B A G G U U C G A A A B G G G U U U G A A A C A G C C C C G A A A C G C C C C C G A A A D A U U U C C G A A C D U U U C C C G A A C A B C B 1 C 6 5 D 8 7 4

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Sample 2Sample 2

Sample 2 1 0 0 0 2 2 2 0 0 3 ___________________ A A G G C U C C A A A A A U U C C C C A A A B A G G U U C G A A A B A U U C C G G A A A C A G C C C C G A A A C A C C C C G G A A A D A U U U C C G A A C D A C C C C G G C C C

A B C B 2 C 4 2 D 7 5 3

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Sample 3Sample 3

Sample 3 1 0 0 0 2 2 2 0 0 3 ___________________ A A G G C U C C A A A A A U U C C C C A A A B A G G U U C G A A A B A U U C C G G A A A C A G C C C C G A A A C A C C C C G G A A A D A U U U C C G A A C D A C C C C G G C C C

A B C B 1 C 3 2 D 6 3 4

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Consensus treeConsensus treeA large number of datasets (between hundred and thousand, depending on computer power) and the same number of different trees are so generated. In this specific case taxa A and B form a cluster in all three trees, while C clusters with D in only one tree. There exist specialised programs, such as the program Consense in the Phylip package of Joe Felsenstein, that are able to analyse all the resulting trees and prepare the most likely tree or consensus tree from those data.

The resulting consensus tree for our small dataset is shown below. The number of times each branch point or node occured (the so-called bootstrap proportion) is indicated at each node.

Result A A G G C U C C A A A B A G G U U C G A A A C A G C C C C G A A A D A U U U C C G A A C

A B C B 2 C 3 3 D 6 4 4

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Again some good advice (1)Again some good advice (1)

Tree topologies may strongly depend on the following:Tree topologies may strongly depend on the following:– DNA or Protein used in the analysisDNA or Protein used in the analysis– Distance or Parsimony methods appliedDistance or Parsimony methods applied– The number of OTUs included in the alignmentThe number of OTUs included in the alignment– The order of the OTUs in the alignmentThe order of the OTUs in the alignment– The selection of a good outgroupThe selection of a good outgroup

None of the methods may guarantee the one tree with None of the methods may guarantee the one tree with the correct topologythe correct topology

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So as to have an idea of the reliability of the topology of the resulting tree, one So as to have an idea of the reliability of the topology of the resulting tree, one should do one or all of the following:should do one or all of the following:– Apply more than one of different methods (distance, parsimony) to the Apply more than one of different methods (distance, parsimony) to the

dataset.dataset.– Vary the parameters used by the different programs, such as seed value Vary the parameters used by the different programs, such as seed value

and jumble factor for the order of OTU addition. and jumble factor for the order of OTU addition. – Add or remove one or more OTUs and see how this influences tree Add or remove one or more OTUs and see how this influences tree

topology.topology.– Try to include an outgroup that may serve as a root for your tree.Try to include an outgroup that may serve as a root for your tree.– Apply Bootstrap or Jacknife analyses to your dataset and prepare a Apply Bootstrap or Jacknife analyses to your dataset and prepare a

consensus tree of 100 - 1000 replicas (depending on the size of the dataset consensus tree of 100 - 1000 replicas (depending on the size of the dataset and on computer power).and on computer power).

Only when widely different methods provide you with similar or identical tree Only when widely different methods provide you with similar or identical tree topologies and such topologies are suported by good bootstrap values (> 95%) topologies and such topologies are suported by good bootstrap values (> 95%) the trees can be considered reliablethe trees can be considered reliable

Again some good advice (2)Again some good advice (2)

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Limitations of the various Limitations of the various methodsmethods

Distance approachesDistance approaches (UPGMA, corrected distances and neighbor- (UPGMA, corrected distances and neighbor-joining) do not use the original (sequence) data, but derived distance joining) do not use the original (sequence) data, but derived distance information. information. Some information is said to be lostSome information is said to be lost

Character-state approachesCharacter-state approaches (Maximum Parsimony) are said to be (Maximum Parsimony) are said to be more powerful than distance methods because they use the raw more powerful than distance methods because they use the raw data. However, this is usually a small fraction of the data. Maximum data. However, this is usually a small fraction of the data. Maximum parsimony uses parsimony uses onlyonly the the informative sitesinformative sites. So when the number of . So when the number of informative sites is not large, this method is often less efficient than informative sites is not large, this method is often less efficient than distance methods (Saitou and Nei, 1986). Maximum parsimony is distance methods (Saitou and Nei, 1986). Maximum parsimony is notorious for its sensitivity to codon biasnotorious for its sensitivity to codon bias

None of the methods is reliable when OTUs with highly unequal None of the methods is reliable when OTUs with highly unequal evolutionary separation are included in the datasetevolutionary separation are included in the dataset

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Some terms used in molecular Some terms used in molecular evolutionevolution

Indel:Indel: position in a sequence alignment where one of the sequences has position in a sequence alignment where one of the sequences has acquired an insertion or extension or has undergone a deletionacquired an insertion or extension or has undergone a deletion

Identity:Identity: percentage of identical residues in pairwise aligned sequences. percentage of identical residues in pairwise aligned sequences. Normally deletions or insertions are not taken into consideration, since it is Normally deletions or insertions are not taken into consideration, since it is not possible to tell how many events have been at the basis of the not possible to tell how many events have been at the basis of the creation of such an indelcreation of such an indel

Homology:Homology: two sequences are homologous or have homology when they two sequences are homologous or have homology when they have evolved from a common ancestral sequence. The same holds for the have evolved from a common ancestral sequence. The same holds for the aligned residues in a sequence alignment. Homologous residues are aligned residues in a sequence alignment. Homologous residues are derived from a common ancestral residuerity and homology as percentage derived from a common ancestral residuerity and homology as percentage should not be used. Two sequences can be similar, and have a certain should not be used. Two sequences can be similar, and have a certain percentage of identity, but cannot have a certain percentage of similarity. percentage of identity, but cannot have a certain percentage of similarity. The same holds for homology.The same holds for homology.

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Some PAM ratesSome PAM rates PAMS per 100PAMS per 100

Million YearsMillion Years

IG kappa chain C region IG kappa chain C region 3737

Lactalbumin Lactalbumin 2727

Epidermal growth factor Epidermal growth factor 2626

Haptoglobin alpha chain Haptoglobin alpha chain 2020

Serum albumin Serum albumin 1919

Phospholipase A Phospholipase A 1919

Hemoglobin alpha chain Hemoglobin alpha chain 1212

Animal lysozyme Animal lysozyme 9.8 9.8

Myoglobin Myoglobin 8.9 8.9

Amyloid AA Amyloid AA 8.7 8.7

Acid proteases Acid proteases 8.4 8.4

Myelin basic protein Myelin basic protein 7.4 7.4

Cytochrome b Cytochrome b 4.5 4.5

Lactate dehydrogenase Lactate dehydrogenase 3.4 3.4

Adenylate kinase Adenylate kinase 3.2 3.2

Triosephosphate isomerase Triosephosphate isomerase 2.8 2.8

Cytochrome c Cytochrome c 2.2 2.2

Plant ferredoxin Plant ferredoxin 1.9 1.9

Glutamate dehydrogenase Glutamate dehydrogenase 0.9 0.9

Histone H4 Histone H4 0.1 0.1

(Adapted from Table 1. Atlas of Protein Sequence and Structure, Suppl 3, 1978, M.O. Dayhoff, ed. (Adapted from Table 1. Atlas of Protein Sequence and Structure, Suppl 3, 1978, M.O. Dayhoff, ed. National Biomedical Research Foundation, 1979.)National Biomedical Research Foundation, 1979.)

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The three letter amino acid The three letter amino acid codecode

AA AlaAla II IleIle SS SerSer

BB AsxAsx KK LysLys TT ThrThr

CC CysCys LL LeuLeu VV ValVal

DD AspAsp MM MetMet WW TryTry

EE GluGlu NN AsnAsn XX XxxXxx

FF PhePhe PP ProPro YY TyrTyr

GG GlyGly QQ GlnGln ZZ GlxGlx

HH HisHis RR ArgArg

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Consider four hypothetical sequences:Consider four hypothetical sequences:

PHYLOGENY, PHOLOGENY, PHLOGENY, PHOLONYPHYLOGENY, PHOLOGENY, PHLOGENY, PHOLONY

Alignment can be done in various ways:Alignment can be done in various ways:

PHYLOGENYPHYLOGENY PHY-LOGENYPHY-LOGENY

PHOLOGENYPHOLOGENY oror PH-OLOGENYPH-OLOGENY

PH-LOGENYPH-LOGENY PH--LOGENYPH--LOGENY

PHOLO--NYPHOLO--NY PH-OLO--NYPH-OLO--NY


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Tree construction using Tree construction using distance-matrix methodsdistance-matrix methods

phylogenetic tree constructed from 6 aligned phylogenetic tree constructed from 6 aligned sequencessequences

A MOLECULAR--EVOLUTIONA MOLECULAR--EVOLUTION

B MOLEKULARE-EVOLUTIENB MOLEKULARE-EVOLUTIEN

C MOLECULAIREEVOLUTIENC MOLECULAIREEVOLUTIEN

D MO-ECALIAREEFOLUTIE-D MO-ECALIAREEFOLUTIE-

E MO-ESALIARE-GOLUTIU-E MO-ESALIARE-GOLUTIU-

F NO-ASELIAKE-HODATAU-F NO-ASELIAKE-HODATAU-

A

B

C

D

E

F

1

11

2

2

2

4

1

1

1

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TPIS HUMANTPIS MACMUTPIS RABITTPIS MOUSETPIS RAT

TPIS LATCHTPIS CHICKTPIS SCHJA

TPIS SCHMATPIS AEDTOTPIS CULPITPIS CULTA

TPIS ANOMETPIS DROMETPIS HELVITPIS CAEEL

TPIS GRAVETPIS ARATH

TPIS PETHYTPIS COPJATPIS LACSA

TPIS HORVUTPIS SECCE

TPIS MAIZETPIS ORYSA

TPIC SPIOLTPIC SECCETPIS STELP

TPIS TRYBBTPIS TRYCRTPIS LEIME

TPI1 GIALATPI2 GIALA

TPIS EMENITPIS SCHPO

TPIS YEASTTPIS COPCI

TPIS BACSUTPIS STAAU

TPIS BACMETPIS BACSTTPIS LACDE

TPIS LACLATPIS CLOAB

TPIS BORBUTPIS SYNY3

TPIS PLAFATPIS MYCHR

TPIS MYCFLTPIS MYCHY

TPIS MYCGETPIS MYCPN

TPIS TREPATPIS MYCLE

TPIS MYCTUTPIS CORGL

TPIS STRCOTPIS XANFL

TPIS CHLAUTPIS RHIET

PGKT THEMATPIS AQUAE

TPIS VIBSATPIS PSESY

TPIS CHLPNTPIS CHLTR

TPIS ECOLITPIS ENTCL

TPIS HAEINTPIS VIBMA

TPIS BUCAPTPIS HELPJTPIS HELPY

TPIS FRATUTPIS MORSP TPIS PYRHO

TPIS PYRWOTPIS METTH

TPIS ARCFUTPIS METJA

TPIS METBR

Animalia

Planta

Protists

Fungi

Eubacteria

Archaebacteria

Triosephosphate Triosephosphate isomeraseisomerase

ICP-TROP Molecular Evolution of Proteins and Phylogenetic Analysis Fred R. Opperdoes Christian de...

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Transcript of ICP-TROP Molecular Evolution of Proteins and Phylogenetic Analysis Fred R. Opperdoes Christian de...