Copyright notice. Multiple sequence alignment. Multiple sequence alignment: outline. Multiple sequence alignment: today s goals

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1 Copyright notice Multiple sequence alignment Monday, December 8, 2008 Many of the images in this powerpoint presentation are from Bioinformatics and Functional Genomics by J Pevsner (ISBN ). Copyright 2003 by Wiley. These images and materials may not be used without permission from the publisher. Introduction to Bioinformatics ME: J. Pevsner pevsner@kennedykrieger.org Visit Additional credit: some of these slides were made by Sarah Wheelan (Johns Hopkins Bloomberg School of Public Health) from the Intro to Bioinformatics course. Multiple sequence alignment: today s goals to define what a multiple sequence alignment is and how it is generated; to describe profile HMMs to introduce databases of multiple sequence alignments to introduce ways you can make your own multiple sequence alignments to show how a multiple sequence alignment provides the basis for phylogenetic trees Page 319 Multiple sequence alignment: outline Multiple sequence alignment: definition ClustalW a collection of three or more protein (or nucleic acid) sequences that are partially or completely aligned homologous residues are aligned in columns across the length of the sequences residues are homologous in an evolutionary sense residues are homologous in a structural sense Page 320 Note how the region of a conserved histidine ( ) varies depending on which algorithm is used

2 Praline MUSCLE Probcons TCoffee Multiple sequence alignment: properties not necessarily one correct alignment of a protein family protein sequences evolve......the corresponding three-dimensional structures of proteins also evolve may be impossible to identify amino acid residues that align properly (structurally) throughout a multiple sequence alignment for two proteins sharing 30% amino acid identity, about 50% of the individual amino acids are superposable in the two structures Page 320 Multiple sequence alignment: features some aligned residues, such as cysteines that form disulfide bridges, may be highly conserved there may be conserved motifs such as a transmembrane domain there may be conserved secondary structure features there may be regions with consistent patterns of insertions or deletions (indels) Page 320

3 Multiple sequence alignment: uses MSA is more sensitive than pairwise alignment to detect homologs BLAST output can take the form of a MSA, and can reveal conserved residues or motifs Population data can be analyzed in a MSA (PopSet) A single query can be searched against a database of MSAs (e.g. PFAM) Regulatory regions of genes may have consensus sequences identifiable by MSA Page 321 Multiple sequence alignment: outline Multiple sequence alignment: methods : dynamic programming Instead of the 2-D dynamic programming matrix in the Needleman-Wunsch technique, think about a 3-D, 4-D or higher order matrix. Multiple sequence alignment: outline give optimal alignments but are not feasible in time or space for more than ~10 sequences. Still an extremely active field. Multiple sequence alignment: methods Progressive methods: use a guide tree (a little like a phylogenetic tree but NOT a phylogenetic tree) to determine how to combine pairwise alignments one by one to create a multiple alignment. Making multiple alignments using trees was a very popular subject in the 80s. Fitch and Yasunobu (1974) may have first proposed the idea, but Hogeweg and Hesper (1984) and many others worked on the topic before Feng and Doolittle (1987) they made one important contribution that got their names attached to this alignment method. Examples: CLUSTALW, MUSCLE Multiple sequence alignment: methods Example of MSA using ClustalW: two data sets Five distantly related lipocalins (human to E. coli) Five closely related RBPs When you do this, obtain the sequences of interest in the FASTA format! (You can save them in a Word document) Page 321

4 The input for ClustalW: a group of sequences (DNA or protein) in the FASTA format Get sequences from Entrez Protein (or HomoloGene) You can display sequences from Entrez Protein in the fasta format When you get a DNA sequence from Entrez Nucleotide, you can click CDS to select only the coding sequence. This is very useful for phylogeny studies. HomoloGene: an NCBI resource to obtain multiple related sequences Use ClustalW to do a progressive MSA [1] Enter a query at NCBI such as globin [2] Click on HomoloGene (left side) [3] Choose a HomoloGene family, and view in the fasta format ac.uk/clustalw/ Fig Page 321

5 Feng-Doolittle MSA occurs in 3 stages Progressive MSA stage 1 of 3: generate global pairwise alignments [1] Do a set of global pairwise alignments (Needleman and Wunsch s dynamic programming algorithm) [2] Create a guide tree five distantly related lipocalins [3] Progressively align the sequences best score Page 321 Fig Page 323 Progressive MSA stage 1 of 3: generate global pairwise alignments Number of pairwise alignments needed Start of Pairwise alignments Aligning... Sequences (1:2) Aligned. Score: 84 Sequences (1:3) Aligned. Score: 84 Sequences (1:4) Aligned. Score: 91 Sequences (1:5) Aligned. Score: 92 Sequences (2:3) Aligned. Score: 99 Sequences (2:4) Aligned. Score: 86 Sequences (2:5) Aligned. Score: 85 Sequences (3:4) Aligned. Score: 85 Sequences (3:5) Aligned. Score: 84 Sequences (4:5) Aligned. Score: 96 five closely related lipocalins best score Fig Page 325 For n sequences, (n-1)(n) / 2 For 5 sequences, (4)(5) / 2 = 10 Page 322 Feng-Doolittle stage 2: guide tree Convert similarity scores to distance scores Progressive MSA stage 2 of 3: generate a guide tree calculated from the distance matrix A tree shows the distance between objects Use UPGMA (defined in the phylogeny lecture) ClustalW provides a syntax to describe the tree A guide tree is not a phylogenetic tree Page 323 Fig Page 323

6 Progressive MSA stage 2 of 3: generate guide tree Feng-Doolittle stage 3: progressive alignment ( ( gi ref NP_ : , ( gi sp Q00724 RETB_MOUS: , gi sp P04916 RETB_RAT: ) : ) : , gi pir A39486: , gi sp P18902 RETB_BOVIN: ); Make a MSA based on the order in the guide tree Start with the two most closely related sequences Then add the next closest sequence Continue until all sequences are added to the MSA five closely related lipocalins Rule: once a gap, always a gap. Fig Page 325 Page 324 Progressive MSA stage 3 of 3: progressively align the sequences following the branch order of the tree Progressive MSA stage 3 of 3: CLUSTALX output Fig Page 324 Note that you can download CLUSTALX locally, rather than using a web-based program! Clustal W alignment of 5 closely related lipocalins CLUSTAL W (1.82) multiple sequence alignment gi pir A39486 MEWVWALVLLAALGSAQAERDCRVSSFRVKENFDKARFSGTWYAMAKKDP 50 gi sp P18902 RETB_BOVIN ERDCRVSSFRVKENFDKARFAGTWYAMAKKDP 32 gi ref NP_ MKWVWALLLLAAW--AAAERDCRVSSFRVKENFDKARFSGTWYAMAKKDP 48 gi sp Q00724 RETB_MOUS MEWVWALVLLAALGGGSAERDCRVSSFRVKENFDKARFSGLWYAIAKKDP 50 gi sp P04916 RETB_RAT MEWVWALVLLAALGGGSAERDCRVSSFRVKENFDKARFSGLWYAIAKKDP 50 ********************:* ***:***** gi pir A39486 EGLFLQDNIVAEFSVDENGHMSATAKGRVRLLNNWDVCADMVGTFTDTED 100 gi sp P18902 RETB_BOVIN EGLFLQDNIVAEFSVDENGHMSATAKGRVRLLNNWDVCADMVGTFTDTED 82 gi ref NP_ EGLFLQDNIVAEFSVDETGQMSATAKGRVRLLNNWDVCADMVGTFTDTED 98 gi sp Q00724 RETB_MOUS EGLFLQDNIIAEFSVDEKGHMSATAKGRVRLLSNWEVCADMVGTFTDTED 100 gi sp P04916 RETB_RAT EGLFLQDNIIAEFSVDEKGHMSATAKGRVRLLSNWEVCADMVGTFTDTED 100 *********:*******.*:************.**:************** gi pir A39486 PAKFKMKYWGVASFLQKGNDDHWIIDTDYDTYAAQYSCRLQNLDGTCADS 150 gi sp P18902 RETB_BOVIN PAKFKMKYWGVASFLQKGNDDHWIIDTDYETFAVQYSCRLLNLDGTCADS 132 gi ref NP_ PAKFKMKYWGVASFLQKGNDDHWIVDTDYDTYAVQYSCRLLNLDGTCADS 148 gi sp Q00724 RETB_MOUS PAKFKMKYWGVASFLQRGNDDHWIIDTDYDTFALQYSCRLQNLDGTCADS 150 gi sp P04916 RETB_RAT PAKFKMKYWGVASFLQRGNDDHWIIDTDYDTFALQYSCRLQNLDGTCADS 150 ****************:*******:****:*:* ****** ********* * asterisks indicate identity in a column Fig Page 326 Progressive MSA stage 3 of 3: progressively align the sequences following the branch order of the tree: Order matters THE LAST FAT CAT THE FAST CAT THE VERY FAST CAT THE FAT CAT THE LAST FAT THE FAST CAT -- THE LAST FA-T THE FAST CA-T -- THE VERY FAST Adapted from C. Notredame, Pharmacogenomics 2002 THE LAST FA-T THE FAST CA-T -- THE VERY FAST THE ---- FA-T

7 Progressive MSA stage 3 of 3: progressively align the sequences following the branch order of the tree: Order matters THE FAT CAT THE FAST CAT THE VERY FAST CAT THE LAST FAT CAT Why once a gap, always a gap? There are many possible ways to make a MSA Where gaps are added is a critical question Gaps are often added to the first two (closest) sequences THE FA-T THE FAST THE ---- FA-T THE ---- FAST THE VERY FAST Adapted from C. Notredame, Pharmacogenomics 2002 THE ---- FA-T THE ---- FAST THE VERY FAST THE LAST FA-T To change the initial gap choices later on would be to give more weight to distantly related sequences To maintain the initial gap choices is to trust that those gaps are most believable Page 324 Additional features of ClustalW improve its ability to generate accurate MSAs Individual weights are assigned to sequences; very closely related sequences are given less weight, while distantly related sequences are given more weight Scoring matrices are varied dependent on the presence of conserved or divergent sequences, e.g.: PAM20 PAM60 PAM120 PAM % id 60-80% id 40-60% id 0-40% id Residue-specific gap penalties are applied

8 Multiple sequence alignment: outline Multiple sequence alignment: methods Iterative methods: compute a sub-optimal solution and keep modifying that intelligently using dynamic programming or other methods until the solution converges. Examples: IterAlign, Praline, MAFFT MUSCLE: next-generation progressive MSA [1] Build a draft progressive alignment Determine pairwise similarity through k-mer counting (not by alignment) Compute distance (triangular distance) matrix Construct tree using UPGMA Construct draft progressive alignment following tree MUSCLE: next-generation progressive MSA [2] Improve the progressive alignment Compute pairwise identity through current MSA Construct new tree with Kimura distance measures Compare new and old trees: if improved, repeat this step, if not improved, then we re done MUSCLE: next-generation progressive MSA [3] Refinement of the MSA Split tree in half by deleting one edge Make profiles of each half of the tree Re-align the profiles Accept/reject the new alignment

9 MUSCLE output (formatted with SeaView) Praline output for the same alignment: pure iterative approach SeaView is a graphical multiple sequence alignment editor available at Boxes highlight a region that is difficult to align Iterative approaches: MAFFT Iterative approaches: MAFFT Uses Fast Fourier Transform to speed up profile alignment Uses fast two-stage method for building alignments using k-mer frequencies Offers many different scoring and aligning techniques One of the more accurate programs available Available as standalone or web interface Many output formats, including interactive phylogenetic trees Has about 1000 advanced settings!

10 Multiple sequence alignment: outline Multiple sequence alignment: methods Consistency-based algorithms: generally use a database of both local high-scoring alignments and long-range global alignments to create a final alignment These are very powerful, very fast, and very accurate methods Examples: T-COFFEE, Prrp, DiAlign, ProbCons ProbCons consistency-based approach Combines iterative and progressive approaches with a unique probabilistic model. Uses Hidden Markov Models (more in a minute) to calculate probability matrices for matching residues, uses this to construct a guide tree Progressive alignment hierarchically along guide tree Post-processing and iterative refinement (a little like MUSCLE) 2e Fig ProbCons consistency-based approach ProbCons consistency-based approach Sequence x Sequence y Sequence z x i y j z k If x i aligns with z k and z k aligns with y j then x i should align with y j ProbCons incorporates evidence from multiple sequences to guide the creation of a pairwise alignment.

11 ProbCons output for the same alignment: consistency iteration helps Multiple sequence alignment: outline APDB ClustalW output Make an MSA MSA w. structural data Compare MSA methods Make an RNA MSA Combine MSA methods Consistency-based Structure-based Back translate protein MSA Multiple sequence alignment: outline Multiple sequence alignment: methods How do we know which program to use? There are benchmarking multiple alignment datasets that have been aligned painstakingly by hand, by structural similarity, or by extremely time- and memory-intensive automated exact algorithms. Some programs have interfaces that are more userfriendly than others. And most programs are excellent so it depends on your preference. If your proteins have 3D structures, use these to help you judge your alignments. For example, try Expresso at

12 Strategy for assessment of alternative multiple sequence alignment algorithms [1] Create or obtain a database of protein sequences for which the 3D structure is known. Thus we can define true homologs using structural criteria. BaliBase: comparison of multiple sequence alignment algorithms [2] Try making multiple sequence alignments with many different sets of proteins (very related, very distant, few gaps, many gaps, insertions, outliers). [3] Compare the answers. Page 346 Fig Page 349 Multiple sequence alignment: methods Benchmarking tests suggest that ProbCons, a consistency-based/progressive algorithm, performs the best on the BAliBASE set, although MUSCLE, a progressive alignment package, is an extremely fast and accurate program. ClustalW is the most popular program. It has a nice interface (especially with ClustalX) and is easy to use. Multiple sequence alignment: outline Multiple sequence alignment to profile HMMs Simple Hidden Markov Model Hidden Markov models (HMMs) are states that describe the probability of having a particular amino acid residue at arranged in a column of a multiple sequence alignment HMMs are probabilistic models 0.15 S P(dog goes out in rain) = 0.1 P(dog goes out in sun) = 0.85 HMMs may give more sensitive alignments than traditional techniques such as progressive alignment 0.2 R Observation: YNNNYYNNNYN (Y=goes out, N=doesn t go out) What is underlying reality (the hidden state chain)? Page 325

13 An HMM is constructed from a MSA Example: five lipocalins GTWYA (hs RBP) GLWYA (mus RBP) GRWYE (apod) GTWYE (E Coli) GEWFS (MUP4) GTWYA GLWYA GRWYE GTWYE GEWFS Position Prob p(g) 1.0 p(t) 0.4 p(l) 0.2 p(r) 0.2 p(e) p(w) 1.0 p(y) 0.8 p(f) 0.2 p(a) 0.4 p(s) 0.2 Fig Page 327 Fig Page 327 GTWYA GLWYA GRWYE GTWYE GEWFS Prob p(g) 1.0 p(t) 0.4 p(l) 0.2 p(r) 0.2 p(e) p(w) 1.0 p(y) 0.8 p(f) 0.2 p(a) 0.4 p(s) 0.2 P(GEWYE) = (1.0)(0.2)(1.0)(0.8)(0.4) = log odds score = ln(1.0) + ln(0.2) + ln(1.0) + ln(0.8) + ln(0.4) = Fig Page 327 GTWYA GLWYA GRWYE GTWYE GEWFS G:1.0 P(GEWYE) = (1.0)(0.2)(1.0)(0.8)(0.5) = 0.08 log odds score = ln(1.0) + ln(0.2) + ln(1.0) + ln(0.8) + ln(0.5) = T:0.4 A:0.5 L:0.2 Y:0.8 E:0.5 R:0.2 W:1.0 F:0.2 S:1.0 E:0.2 Structure of a hidden Markov model (HMM) Structure of a hidden Markov model (HMM) p 4 p 2 I x p 1 M p 3 p 5 p 6 I y p 7 Fig Page 328

14 Structure of a hidden Markov model (HMM) delete state insert state main state HMMER: build a hidden Markov model Determining effective sequence number... done. [4] Weighting sequences heuristically... done. Constructing model architecture... done. Converting counts to probabilities... done. Setting model name, etc.... done. [x] Constructed a profile HMM (length 230) Average score: bits Minimum score: bits Maximum score: bits Std. deviation: bits Fig Page 328 Fig Page 329 HMMER: calibrate a hidden Markov model HMM file: lipocalins.hmm Length distribution mean: 325 Length distribution s.d.: 200 Number of samples: 5000 random seed: histogram(s) saved to: [not saved] POSIX threads: HMMER: search an HMM against GenBank Scores for complete sequences (score includes all domains): Sequence Description Score E-value N gi ref XP_ (XM_129259) ret e [Mus] gi sp P04916 RETB_RAT Plasma retinol e gi ref XP_ [Homo] (XM_005907) sim e gi ref NP_ (NM_006744) ret e gi sp P02753 RETB_HUMAN Plasma retinol e gi ref NP_ [Salmonella] (NC_003197) out e-90 1 gi ref NP_ : domain 1 of 1, from 1 to 195: score 454.6, E = 1.7e-131 *->mkwvmkllllaalagvfgaaerdafsvgkcrvpspprgfrvkenfdv mkwv++llllaa + +aaerd Crv+s frvkenfd+ MKWVWALLLLAA--W--AAAERD------CRVSS----FRVKENFDK gi HMM : x mu : lambda : max : Fig Page 329 erylgtwyeiakkdprferglllqdkitaeysleehgsmsataegrirvl +r++gtwy++akkdp E GL+lqd+I+Ae+S++E+G+Msata+Gr+r+L gi ARFSGTWYAMAKKDP--E-GLFLQDNIVAEFSVDETGQMSATAKGRVRLL 80 enkelcadkvgtvtqiegeasevfltadpaklklkyagvasflqpgfddy +N+++cAD+vGT+t++E dpak+k+ky+gvasflq+g+dd+ gi NNWDVCADMVGTFTDTE DPAKFKMKYWGVASFLQKGNDDH 120 Fig Page 330 HMMER: search an HMM against GenBank match to a bacterial lipocalin gi ref NP_ : domain 1 of 1, from 1 to 177: score 318.2, E = 1.9e-90 *->mkwvmkllllaalagvfgaaerdafsvgkcrvpspprgfrvkenfdv M+LL+ +A a ++ Af+v++C++p+PP+G++V++NFD+ gi MRLLPVVA------AVTA-AFLVVACSSPTPPKGVTVVNNFDA 36 erylgtwyeiakkdprferglllqdkitaeysleehgsmsataegrirvl +rylgtwyeia+ D+rFErGL + +ta+ysl++ +G+i+V+ KRYLGTWYEIARLDHRFERGL---EQVTATYSLRD DGGINVI gi enkelcadkvgtvtqiegeasevfltadpaklklkyagvasflqpgfddy Nk++++D EG+a ++t+ P +++lk+ Sf++p++++y -NKGYNPDR-EMWQKTEGKA---YFTGSPNRAALKV----SFFGPFYGGY gi HMMER: search an HMM against GenBank Scores for complete sequences (score includes all domains): Sequence Description Score E-value N gi sp P27485 RETB_PIG Plasma retinol e gi pir A39486 plasma retinol e gi ref XP_ (XM_129259) ret e gi sp P04916 RETB_RAT Plasma retinol e gi ref XP_ (XM_005907) sim e gi sp P02753 RETB_HUMAN Plasma retinol e gi ref NP_ (NM_006744) ret e gi ref NP_ : domain 1 of 1, from 1 to 199: score 600.2, E = 2.6e-175 *->mewvwalvllaalggasaerdcrvssfrvkenfdkarfsgtwyaiak m+wvwal+llaa+ a+aerdcrvssfrvkenfdkarfsgtwya+ak gi MKWVWALLLLAAW--AAAERDCRVSSFRVKENFDKARFSGTWYAMAK 45 KDPEGLFLqDnivAEFsvDEkGhmsAtAKGRvRLLnnWdvCADmvGtFtD KDPEGLFLqDnivAEFsvDE+G+msAtAKGRvRLLnnWdvCADmvGtFtD gi KDPEGLFLQDNIVAEFSVDETGQMSATAKGRVRLLNNWDVCADMVGTFTD 95 tedpakfkmkywgvasflqkgnddhwiidtdydtfavqyscrllnldgtc tedpakfkmkywgvasflqkgnddhwi+dtdydt+avqyscrllnldgtc gi TEDPAKFKMKYWGVASFLQKGNDDHWIVDTDYDTYAVQYSCRLLNLDGTC 145 Fig Page 330 Fig Page 330

15 Multiple sequence alignment: outline Two kinds of multiple sequence alignment resources [1] Databases of multiple sequence alignments Text-based or query-based searches: CDD, Pfam (profile HMMs), PROSITE [2] Multiple sequence alignment by manual input Muscle, ClustalW, ClustalX Page 329 Databases of multiple sequence alignments PFAM (protein family) database: BLOCKS CDD Pfam SMART DOMO (Gapped MSA) INTERPRO iproclass MetaFAM PRINTS PRODOM (PSI-BLAST) PROSITE These Use HMMs Fig Page 331 PFAM (protein family) text search result PFAM HMM for lipocalins 20 amino acids position Fig Page 334

16 PFAM HMM for lipocalins: GXW motif PFAM GCG MSF format 20 amino acids G W Fig Page 335 Pfam (protein family) database PFAM JalView viewer Fig Page 336 PFAM JalView viewer PFAM JalView viewer: principal components analysis (PCA) Fig Page 336 Fig Page 337

17 Fig Page 337 SMART: Simple Modular Architecture Research Tool (emphasis on cell signaling) Page 338 SMART: lipocalin result Databases of multiple sequence alignments Fig Page 338 BLOCKS CDD Pfam SMART DOMO (Gapped MSA) INTERPRO iproclass MetaFAM PRINTS PRODOM (PSI-BLAST) PROSITE Conserved Domain Database (CDD) at NCBI = PFAM + SMART CDD: Conserved domain database CDD: Conserved domain database [1] Go to NCBI Structure [2] Click CDD [3] Enter a text query, or a protein sequence

18 CDD uses RPS-BLAST: reverse position-specific CDD = PFAM + SMART Purpose: to find conserved domains in the query sequence Query = your favorite protein Database = set of many position-specific scoring matrices (PSSMs), i.e. a set of MSAs CDD is related to PSI-BLAST, but distinct CDD searches against profiles generated from pre-selected alignments Fig Page 339 Page 333 Multiple sequence alignment: outline MEGA version 4: Molecular Evolutionary Genetics Analysis Download from MEGA version 4: Molecular Evolutionary Genetics Analysis MEGA version 4: Molecular Evolutionary Genetics Analysis 1 2 Two ways to create a multiple sequence alignment 1. Open the Alignment Explorer, paste in a FASTA MSA 2. Select a DNA query, do a BLAST search Once your sequences are in MEGA, you can run ClustalW then make trees and do phylogenetic analyses

19 [1] Open the Alignment Explorer [4] Find, select, and copy a multiple sequence alignment (e.g. from Pfam; choose FASTA with dashes for gaps) [2] Select Create a new alignment [3] Click yes (for DNA) or no (for protein) [5] Paste it into MEGA [6] If needed, run ClustalW to align the sequences [7] Save (Ctrl+S) as.mas then exit and save as.meg Multiple sequence alignment: outline Multiple sequence alignment of genomic DNA There are typically few sequences (up to several dozen, each having up to millions of base pairs. Adding more species improves accuracy. Alignment of divergent sequences often reveals islands of conservation (providing anchors for alignment). Chromosomes are subject to inversions, duplications, deletions, and translocations (often involving millions of base pairs). E.g. human chromosome 2 is derived from the fusion of two acrocentric chromosomes. There are no benchmark datasets available.

20 Multiple sequence alignment: outline Goal of the phylogeny lecture Introduction Introduction to evolution and phylogeny Nomenclature of trees Four stages of molecular phylogeny: [1] selecting sequences [2] multiple sequence alignment [3] tree-building [4] tree evaluation Practical approaches to making trees Charles Darwin s 1859 book (On the Origin of Species By Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life) introduced the theory of evolution. To Darwin, the struggle for existence induces a natural selection. Offspring are dissimilar from their parents (that is, variability exists), and individuals that are more fit for a given environment are selected for. In this way, over long periods of time, species evolve. Groups of organisms change over time so that descendants differ structurally and functionally from their ancestors. Page 357

21 Introduction Introduction Darwin did not understand the mechanisms by which hereditary changes occur. In the 1920s and 1930s, a synthesis occurred between Darwinism and Mendel s principles of inheritance. The basic processes of evolution are [1] mutation, and also [2] genetic recombination as two sources of variability; [3] chromosomal organization (and its variation); [4] natural selection [5] reproductive isolation, which constrains the effects of selection on populations At the molecular level, evolution is a process of mutation with selection. Molecular evolution is the study of changes in genes and proteins throughout different branches of the tree of life. Phylogeny is the inference of evolutionary relationships. Traditionally, phylogeny relied on the comparison of morphological features between organisms. Today, molecular sequence data are also used for phylogenetic analyses. (See Stebbins, 1966) Page 357 Page 358 Historical background Studies of molecular evolution began with the first sequencing of proteins, beginning in the 1950s. In 1953 Frederick Sanger and colleagues determined the primary amino acid sequence of insulin. Mature insulin consists of an A chain and B chain heterodimer connected by disulphide bridges (The accession number of human insulin is NP_000198) Page 358 The signal peptide and C peptide are cleaved, and their sequences display fewer functional constraints. Fig Page 359 Fig Page 359 Note the sequence divergence in the disulfide loop region of the A chain Fig Page 359

22 Historical background: insulin By the 1950s, it became clear that amino acid substitutions occur nonrandomly. For example, Sanger and colleagues noted that most amino acid changes in the insulin A chain are restricted to a disulfide loop region. Such differences are called neutral changes (Kimura, 1968; Jukes and Cantor, 1969). Subsequent studies at the DNA level showed that rate of nucleotide (and of amino acid) substitution is about sixto ten-fold higher in the C peptide, relative to the A and B chains. 1 x x x 10-9 Page 358 Number of nucleotide substitutions/site/year Fig Page 359 Historical background: insulin Guinea pig and coypu insulin have undergone an extremely rapid rate of evolutionary change Surprisingly, insulin from the guinea pig (and from the related coypu) evolve seven times faster than insulin from other species. Why? The answer is that guinea pig and coypu insulin do not bind two zinc ions, while insulin molecules from most other species do. There was a relaxation on the structural constraints of these molecules, and so the genes diverged rapidly. Page 360 Arrows indicate positions at which guinea pig insulin (A chain and B chain) differs from both human and mouse Fig Page 359 Historical background Molecular clock hypothesis Oxytocin Vasopressin CYIQNCPLG CYFQNCPRG In the 1960s, sequence data were accumulated for small, abundant proteins such as globins, cytochromes c, and fibrinopeptides. Some proteins appeared to evolve slowly, while others evolved rapidly. In the 1950s, other labs sequenced oxytocin and vasopressin. These peptides differ at only two amino acid residues, but they have distinctly different functions. It became clear that there are significant structural and functional consequences to changes in primary amino acid sequence. Fig Page 360 Linus Pauling, Emanuel Margoliash and others proposed the hypothesis of a molecular clock: For every given protein, the rate of molecular evolution is approximately constant in all evolutionary lineages Page 360

23 Molecular clock hypothesis As an example, Richard Dickerson (1971) plotted data from three protein families: cytochrome c, hemoglobin, and fibrinopeptides. The x-axis shows the divergence times of the species, estimated from paleontological data. The y-axis shows m, the corrected number of amino acid changes per 100 residues. n is the observed number of amino acid changes per 100 residues, and it is corrected to m to account for changes that occur but are not observed. N 100 corrected amino acid changes per 100 residues (m) Dickerson (1971) = 1 e-(m/100) Fig Page 360 Millions of years since divergence Page 361 Molecular clock hypothesis: conclusions Dickerson drew the following conclusions: For each protein, the data lie on a straight line. Thus, the rate of amino acid substitution has remained constant for each protein. The average rate of change differs for each protein. The time for a 1% change to occur between two lines of evolution is 20 MY (cytochrome c), 5.8 MY (hemoglobin), and 1.1 MY (fibrinopeptides). The observed variations in rate of change reflect functional constraints imposed by natural selection. Page 361