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Molecular Evolution of Proteins and Phylogenetic Analysis Fred R. Opperdoes Christian de Duve Institute of Cellular Pathology (ICP) and Laboratory of Biochemistry, Université catholique de Louvain, Brussels, Belgium ICP-TROP The ‘tree of life’ based on rRNA sequences Eukaryota Algae Fungi Cilates Mitochondriates Animals Eubacteria Plants Euglena Kinetoplastida Parabasalia Microsporidia Diplomonads Archaebacteria ICP-TROP Amitochondriates The fusion hypothesis: the eukaryotic cell is a chimaera of eubacterial and archaebacterial traits Eukaryota Algae Fungi Energy metabolism Cilates Animals Eubacteria Plants Euglena Kinetoplastida Parabasalia Microsporidia Diplomonads Root? Archaebacteria Common ancestor? ICP-TROP Genetic machinery Triosephosphate isomerase TPIS HUMAN TPIS MACMU TPIS RABIT TPIS MOUSE TPIS RAT TPIS LATCH TPIS CHICK TPIS SCHJA TPIS SCHMA TPIS AEDTO TPIS CULPI TPIS CULTA TPIS ANOME TPIS DROME TPIS HELVI TPIS CAEEL TPIS GRAVE TPIS ARATH TPIS PETHY TPIS COPJA TPIS LACSA TPIS HORVU TPIS SECCE TPIS MAIZE TPIS ORYSA TPIC SPIOL TPIC SECCE TPIS STELP TPIS TRYBB TPIS TRYCR TPIS LEIME TPI1 GIALA TPI2 GIALA TPIS EMENI TPIS SCHPO TPIS YEAST TPIS COPCI TPIS BACSU TPIS STAAU TPIS BACME TPIS BACST TPIS LACDE TPIS LACLA TPIS CLOAB TPIS BORBU TPIS SYNY3 TPIS PLAFA TPIS MYCHR TPIS MYCFL TPIS MYCHY TPIS MYCGE TPIS MYCPN TPIS TREPA TPIS MYCLE TPIS MYCTU TPIS CORGL TPIS STRCO TPIS XANFL TPIS CHLAU TPIS RHIET PGKT THEMA TPIS AQUAE TPIS VIBSA TPIS PSESY TPIS CHLPN TPIS CHLTR TPIS ECOLI TPIS ENTCL TPIS HAEIN TPIS VIBMA TPIS BUCAP TPIS HELPJ TPIS HELPY TPIS FRATU TPIS MORSP TPIS PYRHO TPIS PYRWO TPIS METTH TPIS ARCFU TPIS METJA TPIS METBR 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? ICP-TROP 0.1 Animalia Planta Protists Fungi Eubacteria Archaebacteria Arguments in favour of protein rather than the DNA sequences CODON BIAS : 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, called stop (or termination) codons, specify "end of peptide sequence" The different codons are used with unequal frequency and this distribution of frequency is referred to as "codon usage" Codon usage varies between species. Amino-acid codons have been degenerated with wobble in the third position. ICP-TROP The universal genetic code First Position | ICP-TROP Second Position Third ------------------------------------ Position U(T) C A G | U(T) Phe Phe Leu Leu Ser Ser Ser Ser Tyr Tyr STOP STOP Cys Cys STOP Trp U(T) C A G C Leu Leu Leu Leu Pro Pro Pro Pro His His Gln Gln Arg Arg Arg Arg U(T) C A G A Ile Ile Ile Met Thr Thr Thr Thr Asn Asn Lys Lys Ser Ser Arg Arg U(T) C A G G Val Val Val Val Ala Ala Ala Ala Asp Asp Glu Glu Gly Gly Gly Gly U(T) C A G Arguments in favour of ... (codon bias 2) Yeasts, protozoa, and animals have different codon preferences, This would result in differences in DNA sequence related to codon bias and not to evolution. ICP-TROP Different species use different codons Homo sapiens [gbmam]: 1 CDS's (389 codons) ---------------------------------------------------------------------------fields: [triplet] [frequency: per thousand] ([number]) ---------------------------------------------------------------------------UUU UUC UUA UUG 20.6( 12.9( 10.3( 10.3( 8) 5) 4) 4) UCU 5.1( UCC 20.6( UCA 18.0( UCG 0.0( 2) 8) 7) 0) UAU 7.7( UAC 30.8( UAA 0.0( UAG 2.6( 3) 12) 0) 1) UGU 7.7( UGC 0.0( UGA 0.0( UGG 15.4( 3) 0) 0) 6) Saccharomyces cerevisiae [gbpln]: 9295 CDS's (4586264 codons) ---------------------------------------------------------------------------fields: [triplet] [frequency: per thousand] ([number]) ---------------------------------------------------------------------------UUU UUC UUA UUG 25.9(118900) 18.3( 83880) 26.3(120698) 27.2(124967) ICP-TROP UCU 23.6(108308) UCC 14.3( 65421) UCA 18.7( 85618) UCG 8.5( 39137) UAU 18.7( 85651) UAC 14.7( 67599) UAA 1.0( 4476) UAG 0.4( 2058) UGU 8.0( 36624) UGC 4.6( 21255) UGA 0.6( 2742) UGG 10.4( 47694) Differences between the “Universal” and Mitochondrial Genetic Codes Codon UGA AGA AGG AUA Universal code Stop Arg Arg Ile mitochondrial code Trp Stop Stop (or Lys*) Met Modified from: Li and Graur, 1991, Fundamentals of Molecular Evolution , Sinauer Publ. * Only in arthropod mitochonria (Abascal et al., PLoS Biol 4, e127 (2006)) ICP-TROP Arguments in favour... (codon bias) Also, the protozoa use the codons UAA and UGA to encode glutamine, rather than STOP The inclusion of unique codons in a subset of the sequences will tend to make that subset appear more divergent than they really are ICP-TROP Arguments in favour... (codon bias 2) High GC content of DNA seems to be associated with aerobiosis in prokaryotes (Naya et al., 2002) In all major groups both organisms with AT rich and GC rich DNA can be found. The inclusion of unique codons in a subset of the sequences will tend to make that subset appear more divergent than they really are ICP-TROP GC content of DNA in aerobic and anaerobic prokaryotes Anaerobic Aerobic ICP-TROP From Naya et al., J. Mol. Evol. 55 (2002) 260-264 The use of protein sequences in phylogeny requires knowledge of the properties of the amino acids and their single letter codes ICP-TROP The use of protein sequences in phylogeny requires knowledge of the properties of the amino acids and their single letter codes Alanine Arginine Asparagine Aspartic acid Cysteine Glutamic acid Glutamine Glycine Histidine Isoleucine ICP-TROP A R N D C E Q G H I Leucine Lysine Methionine Phenylalanine Proline Serine Threonine Tryptophane Tyrosine Valine L K M F P S T W Y V Arguments in favour of a phylogenetic analysis of the corresponding protein rather than the DNA LONG TIME HORIZON : When comparing sequences that have diverged for possibly a billion years or more, it is very likely that the wobble bases in the codons will have become randomized. By excluding the wobble bases (a general technique), one is actually looking at amino acid sequences. So why not taking a protein sequence directly? ICP-TROP Advantages of the translation of DNA into protein (1) ICP-TROP DNA 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 randomly chosen aligned sequences would be identical If gaps are allowed, as much as 50 % of residues in two randomly chosen aligned sequences can be identical. Such a situation may obscure any genuine relationship that may exist. Especially when comparing distantly related or rapidly evolving gene sequences Moreover, it is easier to translate a gene sequence into its corresponding protein than to remove the third wobble base from each of the codons in the gene All open reading frames have alreday been translated in to their corresponding peptide sequences (GenPept and Uniprot databases) Alignment of two random DNA sequences Without indels 19% identity Indels allowed 56% identity ICP-TROP Advantages of the translation of DNA into protein (2) Translation of DNA into 21 different types of codon (20 amino acids and a terminator) allows the information to sharpen up considerably. Wrong frame information is set aside Third-base degeneracies are consolidated After insertion of gaps to align two random protein sequences it can be expected that they are between 10-20% identical As a result of the translation procedure the protein sequences with their 20 amino acids are much more easy to align than the corresponding DNA sequences with only 4 nucleotides ICP-TROP Alignment of two random protein sequences Without indels 7% identity Indels allowed 22% identity ICP-TROP Advantages of the translation of DNA into protein (3) If, after this, you still want to align distantly related gene sequences, you better prepare first a protein alignment and then base yourself on this alignment for the alignment of the gene sequences and the precise placement of indels in the aligned sequences (use EMBOSS’ tranalign). Conclusion: The signal to noise ratio is greatly improved when using protein sequences over DNA sequences! ICP-TROP TBLASTX The blast algorithm TBLASTX allows the use of translated nucleic acid sequence information to search for distant relationships between genes A translated protein sequence is compared with all the translated sequences from a nucleotide database ICP-TROP NCBI BLASTN output ICP-TROP NCBI TBLASTX output ICP-TROP Nature of Sequence Divergence in Proteins The observed sequence difference of two diverging sequences takes the course of a negative exponential. This is the result of the fact that each position is subject to reverse changes ("back mutations") and multiple hits Thus the observed percentage of difference between the protein sequences is not proportional to the actual evolutionary difference between two homologous sequences The evolutionary distance between two proteins is expressed in PAM units. PAM (Dayhoff and Eck, 1968) stands for "accepted point mutation" ICP-TROP Relation between % distance and PAM distance PAM value Distance (%) 80 100 200 250 300 50 60 75 85 92 Twilight zone (From Doolittle, 1987, Of URFs and ORFs, University Science Books) As the evolutionary distance increases, the probability of super-imposed mutations becomes greater resulting in a lower observed percent difference. ICP-TROP Relation between % distance and PAM distance Distance % 85 80 75 70 65 60 55 50 45 40 35 30 25 20 15 10 5 Twilight zone 0 ICP-TROP 100 200 300 Pam value 400 The Kimura correction for multiple 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). ICP-TROP Proteins evolve at highly different rates Rate of Change PAMs / 108 yrs Pseudogenes Fibrinopeptides Lactalbumins Lysozymes Ribonucleases Haemoglobins Acid proteases Cytochrome c Glyceraldehyde-P dehydrogenase Glutamate dehydrogenase 400 90 27 24 21 12 8 4 2 1 Theoretical Lookback Time 45 x 106 yrs 200 " 670 " 850 " 850 " 1500 " 2300 " 5000 " 9000 " 18000 " PAM = number of Accepted Point Mutations per 100 amino acids. Useful lookback time = 360 PAMs ICP-TROP Some Important Dates in History Event Origin of the Universe Formation of the Solar System First Self-replicating System Prokaryotic-Eukaryotic Divergence Plant-Animal Divergence Invertebrate-Vertebrate Divergence Mammalian Radiation Beginning Number of years ago 15 ± 4 4.6 3.5 ± 0.5 2.0 ± 0.5 ~1.0 0.5 ~ 0.1 From Doolittle, Of URFs and ORFs, 1987 ICP-TROP 109 yrs " " " " " " Construction of a phylogenetic tree from phosphoglycerate kinase sequences Rat Mouse Human Horse Drosophila Schistosoma Kluyveromyces Yeast Neurospora Yarli Plasmodium Leishmania Crithidia Trypanosoma 1 Human Mouse Horse Drosophila Schistosoma Wheat Yarli Yeast Neurospora Kluyveromyces Plasmodium Trypanosoma Crithidia Leishmania Bacillus Escherichia Mycobacter Zymomonas Methanobacter Trypanosoma 2 Wheat Zymomonas Escherichia Methanobacter Bacillus 0.1 ICP-TROP GL GL GL GL GL GL GL GL GL GL GL AL AL AL AL I L S L I L I Y DCGP E S S KKY DCGTESSKKY DCGTESSKKY DV GP K T RE L F D I GP K T I E E F D I GP DS V K T F DCGP KS I E E F DNGP E S RK L F DCGE E S V K L F DNGP E S RKA F DAGP KS I E NY D I GP K T I E KY D I GP K T I K I Y D I GP R T I HMY D I GP K T RE L Y D I GDAS AQE L DV GS K T I A L F DV GP KAV AA L D I GTNT I TEY AEAV AEAV AEAV AAP I SKV I NDA L QKV I AA TV T QA I AA TV KDV I VQT I EDV I EEV I RDV I AE I L ESY L TEV L AK F I T R A K Q I V WN G P G R A K Q I V WN G P A R A K Q I V WN G P A R A K L I V WN G P S R A K T I V WN G P D T T Q T I I WN G P G E S K T I L WN G P A K A K T I V WN G P N E S Q T I L WN G P A E A K T I V WN G P L T S K T V I WN G P G K C K S A I WN G P A K C K S T I WN G P G R C K S A I WN G P R E S K L V V WN G P K N A K T I L WN G P K T A K T I F WN G P K A S K T L V WN G P RDAK T I F ANGP Arguments in favour of a protein rather than a DNA sequence (3) INTRONS : A study of the evolution of a protein using its DNA sequence should only include coding sequences This requires that in every DNA sequence all the introns are being edited out. This may be cumbersome and time consuming An easier approach would be the direct translation of the cDNA sequence into its corresponding protein sequence ICP-TROP Typical structure of a eukaryotic gene Flanking region Exon 2 Exon 1 Exon 3 Flanking region 3' 5' Intron I TATA box Initiation codon Transcription initiation ICP-TROP Intron II Stop codon Poly (A) addition site AATAA Arguments in favour of a protein rather than a DNA sequence (4) MULTIGENE FAMILIES : Organisms may contain many highly similar genes, while only one peptide sequence can be identified (e.g. histones, tubulins and GAPDH in humans). Using these DNA sequences, it would be difficult to decide which are expressed and which not and thus which genes to include in the analysis. Moreover, if all the genes that are expressed encode the same protein, then DNA differences are not significant ICP-TROP Arguments in favour of a protein rather than a DNA sequence (5) PROTEIN IS THE UNIT OF SELECTION : ICP-TROP For protein-encoding genes, the object on which natural selection acts is the protein itself. The underlying DNA sequence reflects this process in combination with species-specific pressures on DNA sequence (like the need for aerophiles to have DNA that is GC richer). If function demands that a protein maintains a specific sequence, there still is room for the DNA sequence to change. Arguments in favour of a protein rather than a DNA sequence (6) RNA EDITING : The DNA sequence doesn't always translate into amino acid sequence. In post-translational editing non-coded amino acids are added or coded amino acids are removed in the editing process. This could lead to major differences in DNA sequence (sometimes more than 50%) that nevertheless leads to roughly the same protein sequence after final editing ICP-TROP Pan-editing of mitochondrial RNA in Kinetoplastida UCCuAuuA*AuUUUUUGuUA**UAu AGuuuuuuAA*UGUUGuuuGGuGuA *uuuuuuuAuUG*UGuuuAGuuuuG uuuuGuuGuuGuuuGuuuG****GU GuGuuAuuG**UUUUGAGAuuGuuG note 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. ICP-TROP Some good advice (1) It is recommended to prepare the phylogenetic trees both ways (DNA and Protein) and see how they look For a group of species that are relatively close in time and closely related (like viral proteins or vertebrate enzymes), DNA-based analysis is probably a good way to go, since you avoid problems of codon bias and randomization of wobble bases. But check the protein anyway ICP-TROP Some good advice (2) ICP-TROP Be aware of the problems of multigene families (for instance coding for isoenzymes) Be careful when you decide to exclude or include such sequences (you may compare paralogous rather than orthologous sequences) What is required A DNA or protein sequence A set of homologous sequences A good multiple sequence alignment Several programs to create a phylogenetic tree ICP-TROP ICP-TROP ICP-TROP What is required A DNA or protein sequence A set of homologous sequences A good multiple sequence alignment Several programs to create a phylogenetic tree ICP-TROP ICP-TROP ICP-TROP Alignment parametres in ClustalX ICP-TROP C S T P A G N D E Q H R K M I L V F Y W ICP-TROP PAM 250 matrix as used in Clustal 12, 0, 2, -2, 1, 3, -3, 1, 0, 6, -2, 1, 1, 1, 2, -3, 1, 0,-1, 1, 5, -4, 1, 0,-1, 0, 0, 2, -5, 0, 0,-1, 0, 1, 2, 4, -5, 0, 0,-1, 0, 0, 1, 3, 4, -5,-1,-1, 0, 0,-1, 1, 2, 2, 4, -3,-1,-1, 0,-1,-2, 2, 1, 1, 3, 6, -4, 0,-1, 0,-2,-3, 0,-1,-1, 1, 2, 6, -5, 0, 0,-1,-1,-2, 1, 0, 0, 1, 0, 3, 5, -5,-2,-1,-2,-1,-3,-2,-3,-2,-1,-2, 0, 0, 6, -2,-1, 0,-2,-1,-3,-2,-2,-2,-2,-2,-2,-2, 2, 5, -6,-3,-2,-3,-2,-4,-3,-4,-3,-2,-2,-3,-3, 4, 2, 6, -2,-1, 0,-1, 0,-1,-2,-2,-2,-2,-2,-2,-2, 2, 4, 2, 4, -4,-3,-3,-5,-4,-5,-4,-6,-5,-5,-2,-4,-5, 0, 1, 2,-1, 0,-3,-3,-5,-3,-5,-2,-4,-4,-4, 0,-4,-4,-2,-1,-1,-2, -8,-2,-5,-6,-6,-7,-4,-7,-7,-5,-3, 2,-3,-4,-5,-2,-6, C S T P A G N D E Q H R K M I L V 9, 7,10, 0, 0,17, F Y W ClustalX distance matrix Non-corrected AROC_LEIMJ AROC_PSEAE AROC_VIBCH AROC_VIBAN AROC_NEIMB 0.000 0.036 0.268 0.268 0.232 0.036 0.000 0.268 0.268 0.232 0.268 0.268 0.000 0.089 0.232 0.268 0.268 0.089 0.000 0.232 0.232 0.232 0.232 0.232 0.000 Corrected for multiple substitution AROC_LEIMJ AROC_PSEAE AROC_VIBCH AROC_VIBAN AROC_NEIMB ICP-TROP 0.000 0.037 0.332 0.332 0.278 0.037 0.000 0.332 0.332 0.278 0.332 0.332 0.000 0.095 0.278 0.332 0.332 0.095 0.000 0.278 0.278 0.278 0.278 0.278 0.000 Matrices often used for the alignment of proteins PAM 350 (Dayhoff et al., 1978) BLOSUM30 (Henikoff-Henikoff, 1992) JTT (Jones et al., 1992) mtREV24 (Adachi-Hasegawa, 1996) GONNET 250 matrix (Gonnet et al., 1992) ICP-TROP Alignment of two protein sequences (1) For the creation of a phylogenetic tree a good alignment of protein sequences is of vital importance Only homologous residues should be aligned with each other Doubtful regions should not be included in the alignment Aligned sequences should have similar lengths ICP-TROP Alignment of two protein sequences Alignment requires the user to make assumptions regarding relative costs of substitution versus insertions and deletions (indels). If substitution cost >> gap penalty: there will be many short gaps and no phylogenetic information. In general: search for maximum similarity and minimize the number of insertions and deletions. Exclude regions that cannot be aligned unambiguously! ICP-TROP Multiple alignment of protein sequences For the construction of reliable phylogenetic trees the quality of a multiple alignment is of the utmost importance There are many programs available for the multiple alignment of proteins. – A good program in the public domain is: ClustalW or ClustalX – Available on the web for free and for any platform (PC, Mac, Unix/Linux) They quickly align sequence pairs and roughly determine the degrees of identity between each pair Then the sequences are aligned more precisely in a progressive way starting with the two closest sequences Most programs work best when the sequences have similar length. ICP-TROP Some rules of thumb for the manual alignment of proteins (1) An automatically produced multiple alignment often needs manual adjustment to improve the quality of the alignment. Such improvement can be obtained by using all the knowledge that is available about a protein. If a structure is available you should use the detailed information about secondary structure for the alignment. ICP-TROP What is required A DNA or protein sequence A set of homologous sequences A good multiple sequence alignment Several programs to create a phylogenetic tree ICP-TROP Tree construction methods (2) Character-based methods: – maximum parsimony – maximum likelihood Non-character-based methods: – distance matrix methods ICP-TROP Tree construction methods (1) ICP-TROP Distance matrix methods – Cluster analysis (UPGMA, WPGMA, etc) – Fitch & Margoliash (1967) – Transformed distance methods (eg. Li, 1981) – Neighbor-joining (Saitou & Nei, 1987) – ...many more Parsimony methods – Maximum parsimony (Protpars, DNApars, PAUP) Other methods – Maximum likelihood (DNAML, ProtML, TreePuzzle) – Splitstree, Mr. Bayes – ... many more Text available from: [email protected] Text and slides: http://www.icp.be/~opperd/chapter8/ Website: http://www.icp.be/~opperd/private/proteins.html http://www.icp.be/~opperd/private/phylogeny_2006_Athens.ppt ICP-TROP Distance Matrix Methods UPGMA (Unweighted Pair Group with Arithmatic Mean) uses real (uncorrected) distance values and a sequential clustering algorithm. (Should only be used with closely related OTUs, or when there is constancy of evolutionary rate) Neighbors relation methods – FITCH (Fitch, 1981) – Neighbor-Joining method, (Saitou and Nei, 1987) Should all be used with corrected (see above) distance matrices ICP-TROP Alignment of two protein sequences (1) For the creation of a phylogenetic tree a good alignment of protein sequences is of vital importance Only homologous residues should be aligned with each other Doubtful regions should not be included in the alignment Aligned sequences should have similar lengths ICP-TROP Pair-wise alignment of two protein sequences according to the ‘Dot-Matrix’ method A B C D E G L D P G S E R K C D E G L D P G S E R K • • • • • • • • • • • • • • • • • C • C D E P L D P G S Q R K C D E G L D P G S E R K ICP-TROP • • • • • • • • • • • • • D C D E L D P G S Q R K C D E G L D P G S E R K • • • • • • • • • • • • • • • • C D E D G L S Q L K C D E G L D P L S E R K • • • • • • • • • • • • • • • Dot-Matrix plots Two homologous sequences with 81% identity ICP-TROP Two homologous sequences with 50% identity Pair-wise alignment of two protein sequences according to the ‘Dot-Matrix’ method A B C D E G L D P G S E R K C D E G L D P G S E R K • • • • • • • • • • • • • • • • • C • C D E P L D P G S Q R K C D E G L D P G S E R K ICP-TROP • • • • • • • • • • • • • D C D E L D P G S Q R K C D E G L D P G S E R K • • • • • • • • • • • • • • • • C D E D G L S Q L K C D E G L D P L S E R K • • • • • • • • • • • • • • • Alignment of two protein sequences (2) Alignment requires the user to make assumptions regarding relative costs of substitution versus insertions and deletions (indels). If substitution cost >> gap penalty: there will be many short gaps and no phylogenetic information. In general: search for maximum identity and minimize the number of insertions and deletions. Exclude regions that cannot be aligned unambiguously! Visual alignment is possible using the "dot-matrix method" ICP-TROP Identity matrix as used in Clustal C10, S 0, T 0, P 0, A 0, G 0, N 0, D 0, E 0, Q 0, H 0, R 0, K 0, M 0, I 0, L 0, V 0, F 0, Y 0, W 0, C ICP-TROP 10, 0, 10, 0, 0, 10, 0, 0, 0, 10, 0, 0, 0, 0, 10, 0, 0, 0, 0, 0, 10, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, S T P A G N D E Q H R K M I L V F Y W Distance matrix with mutation costs for amino acids Ala Ser Gly Leu Lys Val Thr Pro Glu Asp Asn Ile Gln Arg Phe Tyr Cys His Met Trp Glx Asx ??? = = = = = = = = = = = = = = = = = = = = = = = A S G L K V T P E D N I Q R F Y C H M W Z B X A 0 1 1 2 2 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 S 1 0 1 1 2 2 1 1 2 2 1 1 2 1 1 1 1 2 2 1 2 2 2 G 1 1 0 2 2 1 2 2 1 1 2 2 2 1 2 2 1 2 2 1 2 2 2 L 2 1 2 0 2 1 2 1 2 2 2 1 1 1 1 2 2 1 1 1 2 2 2 K 2 2 2 2 0 2 1 2 1 2 1 1 1 1 2 2 2 2 1 2 1 2 2 V 1 2 1 1 2 0 2 2 1 1 2 1 2 2 1 2 2 2 1 2 2 2 2 T 1 1 2 2 1 2 0 1 2 2 1 1 2 1 2 2 2 2 1 2 2 2 2 P 1 1 2 1 2 2 1 0 2 2 2 2 1 1 2 2 2 1 2 2 2 2 2 E 1 2 1 2 1 1 2 2 0 1 2 2 1 2 2 2 2 2 2 2 1 2 2 D 1 2 1 2 2 1 2 2 1 0 1 2 2 2 2 1 2 1 2 2 2 1 2 N 2 1 2 2 1 2 1 2 2 1 0 1 2 2 2 1 2 1 2 2 2 1 2 I 2 1 2 1 1 1 1 2 2 2 1 0 2 1 1 2 2 2 1 2 2 2 2 Q 2 2 2 1 1 2 2 1 1 2 2 2 0 1 2 2 2 1 2 2 1 2 2 R 2 1 1 1 1 2 1 1 2 2 2 1 1 0 2 2 1 1 1 1 2 2 2 F 2 1 2 1 2 1 2 2 2 2 2 1 2 2 0 1 1 2 2 2 2 2 2 Y 2 1 2 2 2 2 2 2 2 1 1 2 2 2 1 0 1 1 3 2 2 1 2 C 2 1 1 2 2 2 2 2 2 2 2 2 2 1 1 1 0 2 2 1 2 2 2 H 2 2 2 1 2 2 2 1 2 1 1 2 1 1 2 1 2 0 2 2 2 1 2 M 2 2 2 1 1 1 1 2 2 2 2 1 2 1 2 3 2 2 0 2 2 2 2 W 2 1 1 1 2 2 2 2 2 2 2 2 2 1 2 2 1 2 2 0 2 2 2 Z 2 2 2 2 1 2 2 2 1 2 2 2 1 2 2 2 2 2 2 2 1 2 2 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 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 change that requires all 3 codon positions to change. ICP-TROP Hydrophobicity matrix Arg Lys Asp Glu Asx Glx Ser Asn Gln Gly ??? Thr His Ala Cys Met Pro Val Leu Ile Tyr Phe Trp = = = = = = = = = = = = = = = = = = = = = = = R K D E B Z S N Q G X T H A C M P V L I Y F W R K D E B Z S N Q G X T H A C M P V L I Y F W 10 10 9 9 8 8 6 6 6 5 5 5 5 5 4 3 3 3 3 3 2 1 0 10 10 9 9 8 8 6 6 6 5 5 5 5 5 4 3 3 3 3 3 2 1 0 9 9 10 10 8 8 7 6 6 6 5 5 5 5 5 4 4 4 3 3 3 2 1 9 9 10 10 8 8 7 6 6 6 5 5 5 5 5 4 4 4 3 3 3 2 1 8 8 8 8 10 10 8 8 8 8 7 7 7 7 6 6 6 5 5 5 4 4 3 8 8 8 8 10 10 8 8 8 8 7 7 7 7 6 6 6 5 5 5 4 4 3 6 6 7 7 8 8 10 10 10 10 9 9 9 9 8 8 7 7 7 7 6 6 4 6 6 6 6 8 8 10 10 10 10 9 9 9 9 8 8 8 7 7 7 6 6 4 6 6 6 6 8 8 10 10 10 10 9 9 9 9 8 8 8 7 7 7 6 6 4 5 5 6 6 8 8 10 10 10 10 9 9 9 9 8 8 8 8 7 7 6 6 5 5 5 5 5 7 7 9 9 9 9 10 10 10 10 9 9 8 8 8 8 7 7 5 5 5 5 5 7 7 9 9 9 9 10 10 10 10 9 9 8 8 8 8 7 7 5 5 5 5 5 7 7 9 9 9 9 10 10 10 10 9 9 9 8 8 8 7 7 5 5 5 5 5 7 7 9 9 9 9 10 10 10 10 9 9 9 8 8 8 7 7 5 4 4 5 5 6 6 8 8 8 8 9 9 9 9 10 10 9 9 9 9 8 8 5 3 3 4 4 6 6 8 8 8 8 9 9 9 9 10 10 10 10 9 9 8 8 7 3 3 4 4 6 6 7 8 8 8 8 8 9 9 9 10 10 10 9 9 9 8 7 3 3 4 4 5 5 7 7 7 8 8 8 8 8 9 10 10 10 10 10 9 8 7 3 3 3 3 5 5 7 7 7 7 8 8 8 8 9 9 9 10 10 10 9 9 8 3 3 3 3 5 5 7 7 7 7 8 8 8 8 9 9 9 10 10 10 9 9 8 2 2 3 3 4 4 6 6 6 6 7 7 7 7 8 8 9 9 9 9 10 10 8 1 1 2 2 4 4 6 6 6 6 7 7 7 7 8 8 8 8 9 9 10 10 9 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. Biol. 104, 59 [1976]), derived by George et al. 1990, Mutation ICP-TROP Data Matrix and Its Uses, Methods in Enzymology 183, 333. PAM 1 mutation matrix 1 PAM evolutionary distance Ala 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 V Ala 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 V 9867 2 9 10 3 8 17 21 2 6 4 2 6 2 22 35 32 0 2 18 1 9913 1 0 1 10 0 0 10 3 1 19 4 1 4 6 1 8 0 1 4 1 9822 36 0 4 6 6 21 3 1 13 0 1 2 20 9 1 4 1 6 0 42 9859 0 6 53 6 4 1 0 3 0 0 1 5 3 0 0 1 1 1 0 0 9973 0 0 0 1 1 0 0 0 0 1 5 1 0 3 2 3 9 4 5 0 9876 27 1 23 1 3 6 4 0 6 2 2 0 0 1 10 0 7 56 0 35 9865 4 2 3 1 4 1 0 3 4 2 0 1 2 21 1 12 11 1 3 7 9935 1 0 1 2 1 1 3 21 3 0 0 5 1 8 18 3 1 20 1 0 9912 0 1 1 0 2 3 1 1 1 4 1 2 2 3 1 2 1 2 0 0 9872 9 2 12 7 0 1 7 0 1 33 3 1 3 0 0 6 1 1 4 22 9947 2 45 13 3 1 3 4 2 15 2 37 25 6 0 12 7 2 2 4 1 9926 20 0 3 8 11 0 1 1 1 1 0 0 0 2 0 0 0 5 8 4 9874 1 0 1 2 0 0 4 1 1 1 0 0 0 0 1 2 8 6 0 4 9946 0 2 1 3 28 0 13 5 2 1 1 8 3 2 5 1 2 2 1 1 9926 12 4 0 0 2 28 11 34 7 11 4 6 16 2 2 1 7 4 3 17 9840 38 5 2 2 22 2 13 4 1 3 2 2 1 11 2 8 6 1 5 32 9871 0 2 9 0 2 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 9976 1 0 1 0 3 0 3 0 1 0 4 1 1 0 0 21 0 1 1 2 9945 1 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] 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 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 by Glu. 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.) ICP-TROP C S T P A G N D E Q H R K M I L V F Y W PAM 100 matrix as used in Clustal 14, -1, -5, -6, -5, -8, -8, -11, -11, -11, -6, -6, -11, -11, -5, -12, -4, -10, -2, -13, C ICP-TROP 6, 2, 7, 1, -1, 10, 2, 2, 1, 6, 1, -3, -3, 1, 8, 2, 0, -3, -1, -1, -1, -2, -4, -1, -1, -2, -3, -3, 0, -2, -3, -3, -1, -2, -5, -4, -5, -2, -5, -7, -1, -4, -2, -5, -8, -2, -1, -4, -4, -5, -4, -2, -6, -3, -8, -4, -1, -6, -3, -7, -7, -5, -5, -5, -8, -4, -1, -4, 0, -4, -5, -6, -9, -7, -8, -6, -6,-11, -6,-11, -4,-10,-11,-11,-13, S T P A G 7, 4, 8, 1, 5, 8, -1, 1, 4, 9, 2, -1, -2, 4, -3, -6, -5, 1, 1, -2, -2, -1, -5, -8, -6, -2, -4, -6, -5, -5, -6, -9, -7, -3, -5, -6, -5, -5, -6,-11,-11,-10, -3, -9, -7, -9, -8,-13,-14,-11, N D E Q 11, 1, 10, -3, 3, 8, -7, -2, 1, 13, -7, -4, -4, 2, 9, -5, -7, -6, 4, 2, 9, -6, -6, -6, 1, 5, 1, 8, -4, -7,-11, -2, 0, 0, -5, 12, -1,-10,-10, -8, -4, -5, -6, 6, 13, -7, 1, -9,-11,-12, -7,-14, -2, -2, 19, H R K M I L V F Y W C S T P A G N D E Q H R K M I L V F Y W ICP-TROP PAM 250 matrix as used in Clustal 12, 0, 2, -2, 1, 3, -3, 1, 0, 6, -2, 1, 1, 1, 2, -3, 1, 0,-1, 1, 5, -4, 1, 0,-1, 0, 0, 2, -5, 0, 0,-1, 0, 1, 2, 4, -5, 0, 0,-1, 0, 0, 1, 3, 4, -5,-1,-1, 0, 0,-1, 1, 2, 2, 4, -3,-1,-1, 0,-1,-2, 2, 1, 1, 3, 6, -4, 0,-1, 0,-2,-3, 0,-1,-1, 1, 2, 6, -5, 0, 0,-1,-1,-2, 1, 0, 0, 1, 0, 3, 5, -5,-2,-1,-2,-1,-3,-2,-3,-2,-1,-2, 0, 0, 6, -2,-1, 0,-2,-1,-3,-2,-2,-2,-2,-2,-2,-2, 2, 5, -6,-3,-2,-3,-2,-4,-3,-4,-3,-2,-2,-3,-3, 4, 2, 6, -2,-1, 0,-1, 0,-1,-2,-2,-2,-2,-2,-2,-2, 2, 4, 2, 4, -4,-3,-3,-5,-4,-5,-4,-6,-5,-5,-2,-4,-5, 0, 1, 2,-1, 0,-3,-3,-5,-3,-5,-2,-4,-4,-4, 0,-4,-4,-2,-1,-1,-2, -8,-2,-5,-6,-6,-7,-4,-7,-7,-5,-3, 2,-3,-4,-5,-2,-6, C S T P A G N D E Q H R K M I L V 9, 7,10, 0, 0,17, F Y W Matrices often used for the alignment of proteins PAM 250 (Dayhoff et al., 1978) BLOSUM62 (Henikoff-Henikoff, 1992) JTT (Jones et al., 1992) mtREV24 (Adachi-Hasegawa, 1996) GONNET matrix (Gonnet et al., 1992) ICP-TROP Multiple alignment of protein sequences For the construction of reliable phylogenetic trees the quality of a multiple alignment is of the utmost importance There are many programs available for the multiple alignment of proteins. – A good program in the public domain is: ClustalW – A similar program is Pileup of the GCG package They quickly align sequence pairs and roughly determine the degrees of identity between each pair Then the sequences are aligned more precisely in a progressive way starting with the two closest sequences Most programs work best when the sequences have similar length. ICP-TROP Some rules of thumb for the manual alignment of proteins (1) An automatically produced multiple alignment often needs manual adjustment to improve the quality of the alignment. Such improvement can be obtained by using all the knowledge that is available about a protein. If a structure is available you should use the detailed information about secondary structure for the alignment. ICP-TROP Some rules of thumb for the manual alignment of proteins (2) ICP-TROP The rules for mutation of amino acids are dependent on their physicochemical properties. Surface residues (DRENK) are preferably mutated to residues of similar properties. Since they are not, or less, involved in protein folding they mutate rather easily. Hydrophobic residues (FAMILYVW) are preferentially replaced by other hydrophobic ones. These residues are mainly internal and determine the folding of the protein. They thus mutate rather slowly. Some rules of thumb for the manual alignment of proteins (3) The residues CHQST are indifferent and may be replaced with any other type of residue The residues (DRENKCHQST), when conserved throughout the alignment are very likely residues that are involved in the active site. So the multiple alignment should be adjusted accordingly Periodicity of charged residues may provide information as to the presence of elements of secondary structure such as -helices and -strands ICP-TROP -helix ICP-TROP -strand ICP-TROP Some rules of thumb for the manual alignment of proteins (4) Indels (insertions/deletions) are never found in elements of secondary structure but only in loops. Pro and Gly interfere with secondary structure elements and thus have a preference for loops Hydrophobicity (or hydropathy) profiles according to Kyte and Doolittle of two homologous proteins are in general strikingly similar ICP-TROP Proline interferes with -helix and -sheet formation ICP-TROP From Deber and Therien,2002 Possible functions for proline in trans-membrane domains From Deber and Therien,2002 ICP-TROP Alignment of malate dehydrogenase sequences ICP-TROP Slcl|CHR34_tmp.0150 lcl|CHR34_tmp.0140 lcl|CHR34_tmp.0130 lcl|CHR28_tmp.0050 ----MKPST--LSRFKVTVLGASGAIGQPLALALVQNKRVSEL-----ALYDIVQPR------MRRSQ--GCFFRVAVLGAAGGIGQPLSLLLKNNKYVKEL-----KLYDVKGGP--MGLLFRRSLTALKKGKVVLFGCSNAVGQPLSLLLKMNPHVEELVCCNTAADDDVPGS-------------MSAVKVAVTGAAGQIGYALVPLIARGALLGPTTPVELRLLDIEPALKAL . . :*.: *.:. :* .* : . : : * lcl|CHR34_tmp.0150 lcl|CHR34_tmp.0140 lcl|CHR34_tmp.0130 lcl|CHR28_tmp.0050 -GVAVDLSHFPRKVKVTGYPTKWIHK--ALDGADLVLMSAGMPRRPGMT-HDDLFNTNAL -GVAADLSHICAPAKVTGYTKDELSR--AVENADVVVIPAGIPRKPGMT-RDDLFNTNAS -GIAADLSHIDTLPKVH-YATDEGQWPALLRDAQLILVCFGSSFDLLREDRDIALKAAAP AGVEAELEDCAFPLLDKVVVTADPRV--AFDGVAIAIMCGAFPRKAGME-RKDLLEMNAR *: .:*.. . . .. : :: . . ::. :: * lcl|CHR34_tmp.0150 lcl|CHR34_tmp.0140 lcl|CHR34_tmp.0130 lcl|CHR28_tmp.0050 TVNELSAAVARYAPKSV-LAIISNPLNSMVPVAAETLQRAGVYDPRKLFGIISLNMMRAR IVRDLAIAVGTHAPKAI-VGIITNPVNSTVPVAAEALKKVGVYDPARLFGVTTLDVVRAR TMRRVMAAVASSDTTGN-VAVVSSPVNALTPFCAELLKASGKFDPRKLFGVTTLDVIRTR IFKEQGEAIAAVAASDCRVVVVGNPANTNALILLKSAQ--GKLNPRHVTAMTRLDHNRAL .. *:. .. : :: .* *: . . : : * :* :: .: *: *: lcl|CHR34_tmp.0150 lcl|CHR34_tmp.0140 lcl|CHR34_tmp.0130 lcl|CHR28_tmp.0050 KMLGDFTGQDPEMLDVPVIGGHSGQTIVPLFSHS--GVELRQEQVEYLTHRVR------TFVAEALGASPYDVDVPVIGGHSGETIVPLLSG---FPSLSEEQVRQLTHRIQ------KLVAGTLHMNPYDVNVPVVGGCGGVTACPLIAQT--GLRIPLDDIVRISGEVQSYGVLFE SLLARKAGVPVSQVRNVIIWGNHSSTQVPDTDSAVIGTTPAREAIKDDALDDD-----FV .::. : :: * . * * : : : . lcl|CHR34_tmp.0150 lcl|CHR34_tmp.0140 lcl|CHR34_tmp.0130 lcl|CHR28_tmp.0050 --VGGD-EVVKAKEGRGSSSLSMAFAAAEWADGVLRAMDGEKTLLQCSFVESPLFADKCR --FGGD-EVVKAKDGAGSATLSMAFAGNEWTTAVLRALSGEKGVVVCTYVQS-TVEPSCA AAVGADSHDALSTEVAPPVALGLAYAACDFSTSLLKALRGDVGIVECALVES-TMRSETP QVVRGRGAEIIQLRGLSSAMSAAKAAVDHVHDWIHGTPEGVYVSMGVYSDENPYGVPSGL . . . . * . : : * : :. . lcl|CHR34_tmp.0150 lcl|CHR34_tmp.0140 lcl|CHR34_tmp.0130 lcl|CHR28_tmp.0050 FFGSTVEVCKEGIERVLPLPPLNEYEEEQLDRCLPDLEKN-IRKGLAFVAENAATSTPST FFSSPVLLGNSGVEKIYPVPMLNAYEEKLMAKCLEGLQSN-ITKGIAFSNK--------FFSSRVELGREGVQRVFPMGALTSYEHELIETAVPELMRD-VQAGIEAATQF-------IFSFP-CTCHAGEWTVVSGKLNGDLGKQRLASTIAELQEERAQAGL-------------:*. . * : . .: : : * : *: : Hydrophobicity profiles Profiles according to Kyte and Doolittle of homologous proteins are in general strikingly similar and may provide a tool in the alignment of two or more proteins. The two phosphoglycerate kinase sequences below share 50% identical residues. Trypanosoma congolense PGK ICP-TROP Euglena gracilis PGK Tree construction methods (1) ICP-TROP Distance matrix methods – Cluster analysis (UPGMA, WPGMA, etc) – Fitch & Margoliash (1967) – Transformed distance methods (eg. Li, 1981) – Neighbor-joining (Saitou & Nei, 1987) – ...many more Parsimony methods – Maximum parsimony Other methods – Maximum likelihood (Felsenstein, 1981) – ... many more Tree construction methods (2) Character-based methods: – maximum parsimony – maximum likelihood Non-character-based methods: – distance matrix methods ICP-TROP Phylogeny (2) Distance Matrix methods (in the public domain) – Least squares method (Fitch and Margoliash) —Fitch, Kitsch of the Phylip package (Jo Felsentein, Univ. Washington) – Neighbor-joining method —Neighbor of the Phylip package (Jo Felsentein, Univ. Washington) —Clustal, or Distnj in Protml package (Adachi and Hasegawa, Univ. Tokyo) —Darwin (Gaston Gonner, ETH, Zurich, via mailserver or WWW) Protein Maximum likelihood (in the public domain) – Protml (Adachi and Hasegawa, Univ. Tokyo) (very cpu intensive) – TreePuzzle (Strimmer and von Haeseler, 1997) Protein maximal parsimony (in the public domain) — Protpars (Jo Felsentein, Univ. Washington) — Paup (David Swofford, latest version will be commercial) ICP-TROP Some useful information about phylogenetic trees External nodes Internal nodes OTUs A F B H C I Root A-E are external nodes (extant) F-I are internal (ancestral) nodes G D E ICP-TROP OTUs are operational taxonomic units They can be: species They are the extant (existing) or extinct (ancestral) OTUs Topology: order of the nodes on the tree Distance Matrix Methods UPGMA (Unweighted Pair Group with Arithmatic Mean) uses real (uncorrected) distance values and a sequential clustering algorithm. (Should only be used with closely related OTUs, or when there is constancy of evolutionary rate) Transformed distance methods. Corrections may be introduced to obtain trees with true evolutionary distances (PAM values, Kimura), or corrections are carried out with reference to an outgroup (Farris, 1971; Klotz et al, 1979). Should be used when evolutionary distant organisms are included in the dataset Neighbors relation methods – FITCH (Fitch, 1981) – Neighbor-Joining method, (Saitou and Nei, 1987) Should all be used with corrected (see above) distance matrices ICP-TROP Distance matrix Uncorrected for Multiple Substitutions 1 0.00 2 0.63 0.00 3 0.63 0.63 0.00 4 22.88 22.57 22.88 0.00 5 18.50 18.50 17.87 5.64 0.00 AC007866_13 AC007866_17 AC007866_15 AC007866_9 AC007866_11 1 2 3 4 5 5 21.29 21.29 20.47 5.88 0.00 AC007866_13 AC007866_17 AC007866_15 AC007866_9 AC007866_11 1 2 3 4 5 Using the Kimura correction method Gap weighting is 0.000000 1 0.00 2 0.63 0.00 3 0.63 0.63 0.00 4 27.35 26.90 27.35 0.00 Distance matrix as produced by the EMBOSS program distmat ICP-TROP UPGMA ICP-TROP UPGMA (Unweighted Pair Group with Arithmetic Mean) uses real (uncorrected) distance values and a sequential clustering algorithm. (Should only be used with closely related OTUs, or when there is constancy of evolutionary rate) Tree construction (UPGMA) First cycle B C D E F A B C D E 2 4 6 6 8 4 6 6 8 6 6 8 4 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. ICP-TROP Tree construction (UPGMA) Following the first clustering A and B are considered as a single composite OTU(A,B) and we now calculate the new distance matrix as follows: dist(A,B),C = (distAC + distBC) / 2 = 4 dist(A,B),D = (distAD + distBD) / 2 = 6 dist(A,B),E = (distAE + distBE) / 2 = 6 dist(A,B),F = (distAF + distBF) / 2 = 8 In other words the distance between a simple OTU and a composite OTU is the average of the distances between the simple OTU and the constituent simple OTUs of the composite OTU. Then a new distance matrix is recalculated using the newly calculated distances and the whole cycle is being repeated: ICP-TROP Tree construction (UPGMA) Second cycle C D E F ICP-TROP A,B 4 6 6 8 C D E 6 6 8 4 8 8 Tree construction (UPGMA) Third cycle A,B C 4 D,E 6 F 8 ICP-TROP C D,E 6 8 8 Tree construction (UPGMA 4) Fourth cycle AB,C D,E D,E 6 F 8 8 ICP-TROP Tree construction (UPGMA) Fifth cycle ABC,DE F 8 The final step consists of clustering the last OTU, F,with the composite OTU. ICP-TROP Pitfalls of UPGMA The UPGMA clustering method is very sensitive to unequal evolutionary rates. Clustering works only if the data are ultrametric Ultrametric distances are defined by the satisfaction of the 'three-point condition'. ICP-TROP The treepoint condition For any three taxa: dist AC <= max (distAB, distBC) or, in words: the two greatest distances are equal, or UPGMA assumes that the evolutionary rate is the same for all branches If the assumption of rate constancy among lineages does not hold UPGMA may give an erroneous topology. Non-ultrametric tree ICP-TROP Unequal rates of mutation lead to wrong trees ICP-TROP UPGMA tree construction based on the data of the left tree would result in the erroneous tree at the right UPGMA (conclusion) UPGMA uses real (uncorrected) distance values and a sequential clustering algorithm. This method of tree construction is very sensitive to differences in branch length or unequal rates of evolution. It should only be used with closely related OTUs, or when there is constancy of evolutionary rate. The method is often used in combination with isoenzyme or restriction site data or with morphological criteria ICP-TROP Maximum Parsimony Methods Use sequence information rather than distance information Calculate for all possible trees the tree that represents the minimum number of substitutions at each informative site ICP-TROP 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. ICP-TROP Maximum Parsimony analysis (3) 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. ICP-TROP Maximum Parsimony analysis (4) 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 2 1 3 1 4 3 5 15 6 105 7 954 8 10,395 9 135,135 10 34,459,425 15 2.13E15 ICP-TROP rooted trees 1 3 15 105 945 10,395 135,135 34,459,425 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. maximum parsimony method for 4 nucleic-acid sequences Sequence 1 2 3 4 ICP-TROP Site _________________________ 1 2 3 4 5 6 7 8 9 A A A A A G G G G C A A A C T G G G A A T T T T G G C C C C C C A G A 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 : (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) ICP-TROP 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 noninformative 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. Informative sites A 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. Sequence 1 2 3 4 Site _________________________ 1 2 3 4 5 6 7 8 9 A A A A A G G G G C A A A C T G G G A A * T T T T G G C C * C C C C A G A G * Informative sites are indicated by an asterisk (*) ICP-TROP 1 2 3 4 GGA GGG ACA ACG *** (1) (2) (1) (3) (1) (4) Informative sites only GGA ACA (3) \1 1/ \ 2 / GGG --- ACG / \ /0 0\ GGG ACG (4) Number of mutations Tree I: 4 GGA GGG (2) \1 1/ \ 1 / GCA --- GCG / \ /1 1\ ACA ACG (4) Tree II: 5 GGA GGG (2) \2 1/ \ 0 / GCG --- GCG / \ /1 2\ ACG ACA (3) Tree III: 6 ICP-TROP 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. 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. ICP-TROP Consensus tree ICP-TROP 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. Parsimony and branch lengths (1) G (2) A (3) \1 0/ \ 1 / C -----A / \ /0 1\ C T (4) (1) G (2) A (3) \0 1/ \ 1 / G -----T / \ /1 0\ C T (4) (1) G (2) ICP-TROP A (3) \1 1/ \ 1 / C -----A / \ /0 0\ 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. Some final notes on maximum parsimony ICP-TROP 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 How to root an unrooted tree? The majority of methods yield unrooted trees 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 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 may result in serious topolocical errors 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 outgroup The use of more than one outgroup generally improves the estimate of tree topology 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 the root is put at the midpoint of the longest pathway between two OTUs ICP-TROP Maximum likelihood ICP-TROP 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. 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 ICP-TROP Explication of the method Maximum 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) (2) (3) (4) 1 A A A A G G G U G G C U C U C U U U C C j C C A G C G G G A A A A ....N A ....A A ....A A.... 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 ? ICP-TROP Likelihood for one site 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 \ \ A G | / | / | / | / | / A 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 : _ _ | C C A G | | \ / | / | | \/ | / | L(j) = Sum(Prob | (5) | / |) | \ | / | | \ | / | |_ (6) _| ICP-TROP 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 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) = P 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) = S ln L(j) j=1 ICP-TROP The 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. ICP-TROP The maximum likelihood tree The above procedure is then repeated for all possible topologies (or for all possible trees). The tree with the highest probablility is the tree with the highest maximum likelihood. ICP-TROP 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: – – – ICP-TROP 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! 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 A B C D A B C B 1 C 6 5 D 8 7 4 ICP-TROP 0 1 2 0 3 0 1 2 0 1 ___________________ A G G C U C C A A A A G G U U C G A A A A G C C C C G A A A A U U U C C G A A C (<- number of times each site is sampled) A B C D G G G U G G C U G G C U U U C C U U C C U U C C C G G G A A A A A A A A A A A C Sample 2 Sample 2 A B C D A B C B 2 C 4 2 D 7 5 3 ICP-TROP 1 0 0 0 2 2 2 0 0 3 ___________________ A G G C U C C A A A A G G U U C G A A A A G C C C C G A A A A U U U C C G A A C A B C D A A A A U U C C U U C C C C C C C C C C C G G G C G G G A A A C A A A C A A A C Sample 3 Sample 3 A B C D A B C B 1 C 3 2 D 6 3 4 ICP-TROP 1 0 0 0 2 2 2 0 0 3 ___________________ A A A A G G G U G G C U C U C U U U C C C C C C C G G G A A A A A A A A A A A C A B C D A A A A U U C C U U C C C C C C C C C C C G G G C G G G A A A C A A A C A A A C Consensus tree A 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 B C D A B C B 2 C 3 3 D 6 4 4 ICP-TROP A A A A G G G U G G C U C U C U U U C C C C C C C G G G A A A A A A A A A A A C Again some good advice (1) Tree topologies may strongly depend on the following: – DNA or Protein used in the analysis – Distance or Parsimony methods applied – The number of OTUs included in the alignment – The order of the OTUs in the alignment – The selection of a good outgroup None of the methods may guarantee the one tree with the correct topology ICP-TROP Again some good advice (2) 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: – Apply more than one of different methods (distance, parsimony) to the dataset. – Vary the parameters used by the different programs, such as seed value and jumble factor for the order of OTU addition. – Add or remove one or more OTUs and see how this influences tree topology. – 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 consensus tree of 100 - 1000 replicas (depending on the size of the dataset and on computer power). Only when widely different methods provide you with similar or identical tree topologies and such topologies are suported by good bootstrap values (> 95%) the trees can be considered reliable ICP-TROP Limitations of the various methods Distance approaches (UPGMA, corrected distances and neighborjoining) do not use the original (sequence) data, but derived distance information. Some information is said to be lost Character-state approaches (Maximum Parsimony) are said to be more powerful than distance methods because they use the raw data. However, this is usually a small fraction of the data. Maximum parsimony uses only the informative sites. So when the number of informative sites is not large, this method is often less efficient than distance methods (Saitou and Nei, 1986). Maximum parsimony is notorious for its sensitivity to codon bias None of the methods is reliable when OTUs with highly unequal evolutionary separation are included in the dataset ICP-TROP Some terms used in molecular evolution Indel: position in a sequence alignment where one of the sequences has acquired an insertion or extension or has undergone a deletion Identity: percentage of identical residues in pairwise aligned sequences. 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 creation of such an indel Homology: two sequences are homologous or have homology when they have evolved from a common ancestral sequence. The same holds for the aligned residues in a sequence alignment. Homologous residues are derived from a common ancestral residuerity and homology as percentage should not be used. Two sequences can be similar, and have a certain percentage of identity, but cannot have a certain percentage of similarity. The same holds for homology. ICP-TROP Some PAM rates PAMS per 100 Million Years IG kappa chain C region Lactalbumin Epidermal growth factor Haptoglobin alpha chain Serum albumin Phospholipase A Hemoglobin alpha chain Animal lysozyme Myoglobin Amyloid AA Acid proteases Myelin basic protein Cytochrome b Lactate dehydrogenase Adenylate kinase Triosephosphate isomerase Cytochrome c Plant ferredoxin Glutamate dehydrogenase Histone H4 ICP-TROP 37 27 26 20 19 19 12 9.8 8.9 8.7 8.4 7.4 4.5 3.4 3.2 2.8 2.2 1.9 0.9 0.1 (Adapted from Table 1. Atlas of Protein Sequence and Structure, Suppl 3, 1978, M.O. Dayhoff, ed. National Biomedical Research Foundation, 1979.) The three letter amino acid code A B C D E F G H ICP-TROP Ala Asx Cys Asp Glu Phe Gly His I K L M N P Q R Ile Lys Leu Met Asn Pro Gln Arg S T V W X Y Z Ser Thr Val Try Xxx Tyr Glx Alignment of two protein sequences (1) Consider four hypothetical sequences: PHYLOGENY, PHOLOGENY, PHLOGENY, PHOLONY Alignment can be done in various ways: PHYLOGENY PHOLOGENY PH-LOGENY PHOLO--NY ICP-TROP or PHY-LOGENY PH-OLOGENY PH--LOGENY PH-OLO--NY Tree construction using distance-matrix methods phylogenetic tree constructed from 6 aligned sequences 1 1 1 1 A MOLECULAR--EVOLUTION B C D E MOLEKULARE-EVOLUTIEN MOLECULAIREEVOLUTIEN MO-ECALIAREEFOLUTIEMO-ESALIARE-GOLUTIU- F NO-ASELIAKE-HODATAUICP-TROP 2 2 1 1 2 4 A B C D E F Triosephosphate isomerase ICP-TROP TPIS HUMAN TPIS MACMU TPIS RABIT TPIS MOUSE TPIS RAT TPIS LATCH TPIS CHICK TPIS SCHJA TPIS SCHMA TPIS AEDTO TPIS CULPI TPIS CULTA TPIS ANOME TPIS DROME TPIS HELVI TPIS CAEEL TPIS GRAVE TPIS ARATH TPIS PETHY TPIS COPJA TPIS LACSA TPIS HORVU TPIS SECCE TPIS MAIZE TPIS ORYSA TPIC SPIOL TPIC SECCE TPIS STELP TPIS TRYBB TPIS TRYCR TPIS LEIME TPI1 GIALA TPI2 GIALA TPIS EMENI TPIS SCHPO TPIS YEAST TPIS COPCI TPIS BACSU TPIS STAAU TPIS BACME TPIS BACST TPIS LACDE TPIS LACLA TPIS CLOAB TPIS BORBU TPIS SYNY3 TPIS PLAFA TPIS MYCHR TPIS MYCFL TPIS MYCHY TPIS MYCGE TPIS MYCPN TPIS TREPA TPIS MYCLE TPIS MYCTU TPIS CORGL TPIS STRCO TPIS XANFL TPIS CHLAU TPIS RHIET PGKT THEMA TPIS AQUAE TPIS VIBSA TPIS PSESY TPIS CHLPN TPIS CHLTR TPIS ECOLI TPIS ENTCL TPIS HAEIN TPIS VIBMA TPIS BUCAP TPIS HELPJ TPIS HELPY TPIS FRATU TPIS MORSP TPIS PYRHO TPIS PYRWO TPIS METTH TPIS ARCFU TPIS METJA TPIS METBR 0.1 Animalia Planta Protists Fungi Eubacteria Archaebacteria