Multiple sequence alignment

Multiple sequence alignment: outline [1] Introduction to MSA Exact methods Progressive (ClustalW) Iterative (MUSCLE) Consistency (ProbCons) Structure-based (Expresso) Conclusions: benchmarking studies [3] Hidden Markov models (HMMs), Pfam and CDD [4] MEGA to make a multiple sequence alignment [5] Multiple alignment of genomic DNA

Multiple sequence alignment: definition • a collection of three or more protein (or nucleic acid) sequences that are partially or completely aligned • homologous residues are aligned in columns across the length of the sequences • residues are homologous in an evolutionary sense • residues are homologous in a structural sense Page 320

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

Praline

MUSCLE

Probcons

TCoffee

Multiple sequence alignment: properties • not necessarily one “correct” alignment of a protein family • protein sequences evolve... • ...the corresponding three-dimensional structures of proteins also evolve • may be impossible to identify amino acid residues that align properly (structurally) throughout a multiple sequence alignment • for two proteins sharing 30% amino acid identity, about 50% of the individual amino acids are superposable in the two structures Page 320

Multiple sequence alignment: features • some aligned residues, such as cysteines that form disulfide bridges, may be highly conserved there may be conserved motifs such as a transmembrane domain there may be conserved secondary structure features there may be regions with consistent patterns of insertions or deletions (indels) Page 320

Multiple sequence alignment: uses • MSA is more sensitive than pairwise alignment to detect homologs BLAST output can take the form of a MSA, and can reveal conserved residues or motifs Population data can be analyzed in a MSA (PopSet) A single query can be searched against a database of MSAs (e.g. PFAM) Regulatory regions of genes may have consensus sequences identifiable by MSA Page 321

Multiple sequence alignment: outline [1] Introduction to MSA Exact methods Progressive (ClustalW) Iterative (MUSCLE) Consistency (ProbCons) Structure-based (Expresso) Conclusions: benchmarking studies [3] Hidden Markov models (HMMs), Pfam and CDD [4] MEGA to make a multiple sequence alignment [5] Multiple alignment of genomic DNA [6] Introduction to molecular evolution and phylogeny

Multiple sequence alignment: methods Exact 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. Exact methods give optimal alignments but are not feasible in time or space for more than ~10 sequences. Still an extremely active field.

Multiple sequence alignment: outline [1] Introduction to MSA Exact methods Progressive (ClustalW) Iterative (MUSCLE) Consistency (ProbCons) Structure-based (Expresso) Conclusions: benchmarking studies [3] Hidden Markov models (HMMs), Pfam and CDD [4] MEGA to make a multiple sequence alignment [5] Multiple alignment of genomic DNA [6] Introduction to molecular evolution and phylogeny

Multiple sequence alignment: 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

Multidimensional Dynamic Programming Example: in 3D (three sequences): 7 neighbors/cells F(i,j,k) = max{ F(i-1,j-1,k-1)+S(xi, xj, xk), F(i-1,j-1,k )+S(xi, xj, - ), F(i-1,j ,k-1)+S(xi, -, xk), F(i-1,j ,k )+S(xi, -, - ), F(i ,j-1,k-1)+S( -, xj, xk), F(i ,j-1,k )+S( -, xj, xk), F(i ,j ,k-1)+S( -, -, xk) }

HOW CAN I ALIGN MANY SEQUENCES 2 Globins =>1 Min

HOW CAN I ALIGN MANY SEQUENCES 3 Globins =>2 hours

HOW CAN I ALIGN MANY SEQUENCES 4 Globins => 10 days

HOW CAN I ALIGN MANY SEQUENCES 5 Globins => 3 years

HOW CAN I ALIGN MANY SEQUENCES 6 Globins =>300 years

HOW CAN I ALIGN MANY SEQUENCES 7 Globins =>30. 000 years Solidified Fossil, Old stuff

HOW CAN I ALIGN MANY SEQUENCES 8 Globins =>3 Million years

The Choice of an objective function Biological problem that lies in the definition of correctness Sum of pair, Entropy score, Consistency based, … The Optimization of that function Exact Algorithms (Dynamic Programming) Progressive alignment (ClustalW) Iterative approaches (SA, GA, …)

Alignment Costs Traditional A C A C C A Input seq Reconstructed seq Missmatches Traditional (SP) Tree-Alignment Star-Alignment A, A, A, C, C A, A, A, C, C A, A, A, C, C -- 6 A, A, C 1 A 2

The Progressive Multiple Alignment Algorithm (Clustal W)

Making An Alignment Any Exact Method would be TOO SLOW We will use a Heuristic Algorithm. Progressive Alignment Algorithm is the most Popular -ClustalW -Greedy Heuristic (No Guarranty). -Fast

Progressive Alignment Feng and Dolittle, 1988; Taylor 1989 Clustering

Progressive Alignment Dynamic Programming Using A Substitution Matrix

Progressive Alignment -Depends on the CHOICE of the sequences. -Depends on the ORDER of the sequences (Tree). -Depends on the PARAMETERS: Substitution Matrix. Penalties (Gop, Gep). Sequence Weight. Tree making Algorithm.

Example : Progressive alignment Pairwise Alignment Guide Tree MSA by adding sequences 1 + 2 3 + 4 1 + 3 1 + 4 2 + 4 2 + 3 1 2 3 4 2 3 4 1 1 2 3

Progressive alignment (cont.) Sequence Guide Tree 1 2 3 4 5 1 1 2 3 4 5 Distance Matrix: displays distances of all sequence pairs. 2 4 5 3 D = 1 - S UPGMA (unweighted pair group method of arithmetic averages) or Neighbour-Joining method

UPGMA Clustering (Guide Tree) 1 2 3 4 5 1 0 2 6 9 7 2 0 5 7 7 3 0 5 4 4 0 3 5 0 ij u 3 4 5 u 0 5 8 7 3 0 5 4 4 0 3 5 0 ij u 3 v u 0 5 7 3 0 4 v 0 ij 6 u w u 0 6 w 0 ij 2 . 5 . 5 . 5 . . 8 5 4 . . 5 5 3 1 2 3 5 4 1 2 3 5 4 1 2 3 5 4 1 2 3 5 4

Progressive alignment (cont.) Guide Tree Alignment of alignments 1 2 4 5 2 3 1 Columns - once aligned - are never changed. . . and new gaps are inserted. Depend strongly on pairwise alignments and the intitial starting sequences No guarantee that the global optimal solution will be found. In case of sequences identity less than 25-30%, this approach become much less reliable.

Progressive Alignment When Doesn’t It Work SeqA GARFIELD THE LAST FA-T CAT SeqB GARFIELD THE FAST CA-T --- SeqC GARFIELD THE VERY FAST CAT SeqD -------- THE ---- FA-T CAT CLUSTALW (Score=20, Gop=-1, Gep=0, M=1) SeqA GARFIELD THE LAST FA-T CAT SeqB GARFIELD THE FAST ---- CAT SeqC GARFIELD THE VERY FAST CAT SeqD -------- THE ---- FA-T CAT CORRECT (Score=24)

GARFIELD THE LAST FAT CAT GARFIELD THE VERY FAST CAT GARFIELD THE FAST CAT GARFIELD THE VERY FAST CAT THE FAT CAT GARFIELD THE LAST FAT CAT GARFIELD THE FAST CAT --- GARFIELD THE LAST FA-T CAT GARFIELD THE FAST CA-T --- GARFIELD THE VERY FAST CAT -------- THE ---- FA-T CAT GARFIELD THE VERY FAST CAT -------- THE ---- FA-T CAT

Iterative alignment Iterate until the MSA doesn’t change (convergence) B C DE Pairwise distance table Iterate until the MSA doesn’t change (convergence) E D C B A 11 1 3 10 2 Guide tree MSA A B C D E

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

Get sequences from Entrez Protein (or HomoloGene)

You can display sequences from Entrez Protein in the fasta format

When you get a DNA sequence from Entrez Nucleotide, you can click CDS to select only the coding sequence. This is very useful for phylogeny studies.

HomoloGene: an NCBI resource to obtain multiple related sequences [1] Enter a query at NCBI such as globin [2] Click on HomoloGene (left side) [3] Choose a HomoloGene family, and view in the fasta format

http://www2.ebi. ac.uk/clustalw/ Use ClustalW to do a progressive MSA Fig. 10.1 Page 321

Feng-Doolittle MSA occurs in 3 stages [1] Do a set of global pairwise alignments (Needleman and Wunsch’s dynamic programming algorithm) [2] Create a guide tree [3] Progressively align the sequences Page 321

Progressive MSA stage 1 of 3: generate global pairwise alignments five distantly related lipocalins best score Fig. 10.2 Page 323

Progressive MSA stage 1 of 3: generate global pairwise alignments five closely related lipocalins 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 best score Fig. 10.4 Page 325

Number of pairwise alignments needed For n sequences, (n-1)(n) / 2 For 5 sequences, (4)(5) / 2 = 10 Page 322

Feng-Doolittle stage 2: guide tree Convert similarity scores to distance scores A tree shows the distance between objects Use UPGMA (defined in the phylogeny lecture) ClustalW provides a syntax to describe the tree A guide tree is not a phylogenetic tree Page 323

Progressive MSA stage 2 of 3: generate a guide tree calculated from the distance matrix Fig. 10.2 Page 323

Progressive MSA stage 2 of 3: generate guide tree ( gi|5803139|ref|NP_006735.1|:0.04284, gi|6174963|sp|Q00724|RETB_MOUS:0.00075, gi|132407|sp|P04916|RETB_RAT:0.00423) :0.10542) :0.01900, gi|89271|pir||A39486:0.01924, gi|132403|sp|P18902|RETB_BOVIN:0.01902); five closely related lipocalins Fig. 10.4 Page 325

Feng-Doolittle stage 3: progressive alignment Make a MSA based on the order in the guide tree Start with the two most closely related sequences Then add the next closest sequence Continue until all sequences are added to the MSA Rule: “once a gap, always a gap.” Page 324

Progressive MSA stage 3 of 3: progressively align the sequences following the branch order of the tree Fig. 10.3 Page 324

Progressive MSA stage 3 of 3: CLUSTALX output Note that you can download CLUSTALX locally, rather than using a web-based program!

Clustal W alignment of 5 closely related lipocalins CLUSTAL W (1.82) multiple sequence alignment gi|89271|pir||A39486 MEWVWALVLLAALGSAQAERDCRVSSFRVKENFDKARFSGTWYAMAKKDP 50 gi|132403|sp|P18902|RETB_BOVIN ------------------ERDCRVSSFRVKENFDKARFAGTWYAMAKKDP 32 gi|5803139|ref|NP_006735.1| MKWVWALLLLAAW--AAAERDCRVSSFRVKENFDKARFSGTWYAMAKKDP 48 gi|6174963|sp|Q00724|RETB_MOUS MEWVWALVLLAALGGGSAERDCRVSSFRVKENFDKARFSGLWYAIAKKDP 50 gi|132407|sp|P04916|RETB_RAT MEWVWALVLLAALGGGSAERDCRVSSFRVKENFDKARFSGLWYAIAKKDP 50 ********************:* ***:***** gi|89271|pir||A39486 EGLFLQDNIVAEFSVDENGHMSATAKGRVRLLNNWDVCADMVGTFTDTED 100 gi|132403|sp|P18902|RETB_BOVIN EGLFLQDNIVAEFSVDENGHMSATAKGRVRLLNNWDVCADMVGTFTDTED 82 gi|5803139|ref|NP_006735.1| EGLFLQDNIVAEFSVDETGQMSATAKGRVRLLNNWDVCADMVGTFTDTED 98 gi|6174963|sp|Q00724|RETB_MOUS EGLFLQDNIIAEFSVDEKGHMSATAKGRVRLLSNWEVCADMVGTFTDTED 100 gi|132407|sp|P04916|RETB_RAT EGLFLQDNIIAEFSVDEKGHMSATAKGRVRLLSNWEVCADMVGTFTDTED 100 *********:*******.*:************.**:************** gi|89271|pir||A39486 PAKFKMKYWGVASFLQKGNDDHWIIDTDYDTYAAQYSCRLQNLDGTCADS 150 gi|132403|sp|P18902|RETB_BOVIN PAKFKMKYWGVASFLQKGNDDHWIIDTDYETFAVQYSCRLLNLDGTCADS 132 gi|5803139|ref|NP_006735.1| PAKFKMKYWGVASFLQKGNDDHWIVDTDYDTYAVQYSCRLLNLDGTCADS 148 gi|6174963|sp|Q00724|RETB_MOUS PAKFKMKYWGVASFLQRGNDDHWIIDTDYDTFALQYSCRLQNLDGTCADS 150 gi|132407|sp|P04916|RETB_RAT PAKFKMKYWGVASFLQRGNDDHWIIDTDYDTFALQYSCRLQNLDGTCADS 150 ****************:*******:****:*:* ****** ********* * asterisks indicate identity in a column Fig. 10.5 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 CAT THE FAST CAT --- THE LAST FA-T CAT THE FAST CA-T --- THE VERY FAST CAT THE LAST FA-T CAT THE FAST CA-T --- THE VERY FAST CAT THE ---- FA-T CAT Adapted from C. Notredame, Pharmacogenomics 2002

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 THE FA-T CAT THE FAST CAT THE ---- FA-T CAT THE ---- FAST CAT THE VERY FAST CAT THE ---- FA-T CAT THE ---- FAST CAT THE VERY FAST CAT THE LAST FA-T CAT Adapted from C. Notredame, Pharmacogenomics 2002

Why “once a gap, always a gap”? There are many possible ways to make a MSA Where gaps are added is a critical question Gaps are often added to the first two (closest) sequences To change the initial gap choices later on would 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 80-100% id PAM60 60-80% id PAM120 40-60% id PAM350 0-40% id Residue-specific gap penalties are applied

Molecular Evolutionary Genetics Analysis MEGA version 4: Molecular Evolutionary Genetics Analysis Download from www.megasoftware.net

Molecular Evolutionary Genetics Analysis MEGA version 4: Molecular Evolutionary Genetics Analysis

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

[1] Open the Alignment Explorer [2] Select “Create a new alignment” [3] Click yes (for DNA) or no (for protein)

[4] Find, select, and copy a multiple sequence alignment (e. g [4] Find, select, and copy a multiple sequence alignment (e.g. from Pfam; choose FASTA with dashes for gaps) [5] Paste it into MEGA [6] If needed, run ClustalW to align the sequences [7] Save (Ctrl+S) as .mas then exit and save as .meg