Multiple Sequence Alignment (MSA) 1.Uses of MSA 2.Technical difficulties 1.Select sequences 2.Select objective function 3.Optimize the objective function 1.Exact algorithms 2.Progressive algorithms 3.Iterative algorithms 1.Stochastic 2.Non-stochastic 4.Consistency-based algorithms 3. Tools to view alignments 1.MEGA 2.JALVIEW (PSI-BLAST) Function prediction Fig. from Boris Steipe U. of Toronto Sequence relationships If the MSA is incorrect, the above inferences are incorrect!

Multiple sequence alignment: outline [1] Introduction to MSA Exact methods Progressive (ClustalW) Iterative (MUSCLE) Consistency (ProbCons) Structure-based (Expresso) Conclusions: benchmarking studies [2] Hidden Markov models (HMMs), Pfam and CDD [3] MEGA to make a multiple sequence alignment [4] 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 evolutionary sense residues are homologous in an evolutionary sense structural sense residues are homologous in a structural sense

Example: someone is interested in caveolin Step 1: at NCBI change the pulldown menu to HomoloGene and enter caveolin in the search box

Step 2: inspect the results. We’ll take the first set of caveolins. Change the Display to Multiple alignment.

Step 3: inspect the multiple alignment. Note that these eight proteins align nicely, although gaps must be included.

Here’s another multiple alignment, Rac: This insertion could be due to alternative splicing

HomoloGene includes groups of eukaryotic proteins. The site includes links to the proteins, pairwise alignments, and more

Example: 5 alignments of 5 globins five globins proteins. Let’s look at a multiple sequence alignment (MSA) of five globins proteins. We’ll use five prominent MSA programs: ClustalW, Praline, MUSCLE (used at HomoloGene), ProbCons, and TCoffee. Each program offers unique strengths. histidine (H) residue We’ll focus on a histidine (H) residue that has a critical role in binding oxygen in globins, and should be aligned. But often it’s not aligned, and all five programs give different answers. Our conclusion will be that there is no single best approach to MSA. Dozens of new programs have been introduced in recent years.

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

Praline Page 194

MUSCLE Page 194

Probcons Page 195

TCoffee Page 195

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 180

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 181

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 181

Multiple sequence alignment: outline [1] Introduction to MSA Exact methods Progressive (ClustalW) Iterative (MUSCLE) Consistency (ProbCons) Structure-based (Expresso) Conclusions: benchmarking studies [2] Hidden Markov models (HMMs), Pfam and CDD [3] MEGA to make a multiple sequence alignment [4] Multiple alignment of genomic DNA [5] Introduction to molecular evolution and phylogeny

Fig. from Boris Steipe Univ. of Toronto MSA. Technical difficulties

Multiple Sequence Alignment (MSA) 1.Uses of MSA 2.Technical difficulties 1.Select sequences 2.Select objective function 3.Optimize the objective function 1.Exact algorithms 2.Progressive algorithms 3.Iterative algorithms 1.Stochastic 2.Non-stochastic 4.Consistency-based algorithms 3. Tools to view alignments 1.MEGA 2.JALVIEW

Multiple Sequence Alignment (MSA) 1.Uses of MSA 2.Technical difficulties 1.Select sequences 2.Select objective function 3.Optimize the objective function 1.Exact algorithms 2.Progressive algorithms 3.Iterative algorithms 1.Stochastic 2.Non-stochastic 4.Consistency-based algorithms 3. Tools to view alignments 1.MEGA 2.JALVIEW

Objective functions (OF) Define the mathematical objective of the search A biologically ideal OF should Maximize similarity Minimize the number of gaps (over their length) Retain conserved motifs and patterns Retain functionally important alignments Recapitulate phylogeny Concentrate on alignable regions, not in gapped regions Consider the limitations imposed by the 3D structure Most widely used MSA packages use a simple sum-of-pairs OF Define a mathematical optimum Use sum-of-pairs and affine gaps Use a context-independent Mutation Data Matrix (e.g. Blosum 62) Some add weighting proportional to the information in the seq. It is a non-trivial task to test the biological correctness of an objective function.

Seq.1 AT-AATG Seq.2 CTGAG-G Seq.3 ATGAA-G Sum-of-pairs (SP) Objective Function Induced pairwise alignment: After the best MSA is obtained, other sequences are removed, spaces facing spaces are removed and a score is calculated using any chosen scoring scheme (distance or similarity). Seq.1 AT-AATG Induced Seq. 3-4 alignment Seq.2 CTGAG-G Seq.3 CTG-GG Distance scheme Seq.3 ATGAA-G Seq.4 ATGAAG # mismathes (including -) Sum-of-pairs distance = = 9 Sum-of-pairs score: The SP of a MSA is the sum of the scores of all the scores of the induced pairwise global alignments 3 Weighted Sum-of-pairs score: each score can be multiplied by a weight. Weights are often intended to reflect evolutionary distances to induce the MSA to more accurately reflect known evolutionary history, or the information carried by the sequences being aligned.

Sum-of-pairs (SP) Objective Function Multiple MSA: Depending on the Mutation Data matrix selected (e.g. PAM or BLOSUM) and on the selected gap penalties (opening and extension) different MSA will be obtained. Which one is the correct one? New Objective functions: less sensitive to gap penalty estimations thanks to the incorporation of local information Segment-to-segment comparisons of the sequences (instead of character-to- character) without gap penalties is the strategy used by DiAlign. This approach is efficient where sequences are not globally related but share only local similarities, (genomic DNA, many protein families) Consistency objective function: (e.g. T-Coffee) The optimal MSA is defined as the one that agrees the most with all the optimal pair-wise alignments. Given a set of independent observations the most consistent are often closer to “the truth”. Seq.1 AT-AATG Seq.1 ATAATG Clustal Seq.2 CTGAG-G Distance scheme Seq.2 CTGAGG Gap open= 11 Seq.3 ATGAA-G # mismatches (including -) Seq.3 ATGAAG Gap ext.= 1

Progressive algorithms (ClustalW, MultAlign, AMPS) GARFIELDTHEFASTCAT--- GARFIELDTHELASTFATCAT GARFIELD THE LAST FAST CATGARFIELD THE FAST CAT GARFIELD THE VERY FAST CAT THE FAT CAT GARFIELDTHEVERYFASTCAT GARFIELDTHEFASTCAT---- GARFIELDTHELASTFAT-CAT THEFAT-----CAT GARFIELDTHEVERYFASTCAT GARFIELDTHEFASTCAT--- GARFIELDTHELASTFATCAT THEFA-----TCAT GARFIELDTHEVERYFASTCAT GARFIELDTHEFAS----TCAT GARFIELDTHELASTFA-TCAT DCA alignment ClustalW Blosum62 Gap 11-1 Cheaper to open terminal gap than to align C and F Example of Progressive algorithm Calculate distances/similarities between sequences Construct a tree Add sequentially, following tree

Multiple sequence alignment: methods Progressive methods: use a guide tree (related to a phylogenetic tree) to determine how to combine pairwise alignments one by one to create a multiple alignment. Examples: CLUSTALW, MUSCLE Page 185

Multiple sequence alignment: methods Example of MSA using ClustalW: two data sets Five distantly related globins (human to plant) Five closely related beta globins Obtain your sequences in the FASTA format. Page 185

Use ClustalW to do a progressive MSA ac.uk/clustalw/ Page 186

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 185

Page 186 Progressive MSA stage 1 of 3: generate global pairwise alignments best score

Number of pairwise alignments needed For n sequences, (n-1)(n) / 2 For 5 sequences, (4)(5) / 2 = 10 For 200 sequences, (199)(200) / 2 = 19,900 Page 185

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 Page 187

Page 186 Progressive MSA stage 2 of 3: generate a guide tree calculated from the distance matrix (5 distantly related globins)

Page closely related globins

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 188

Clustal W alignment of 5 distantly related globins Fig. 6.3 Page 187

Fig. 6.5 Page 189 Clustal W alignment of 5 closely related globins * asterisks indicate identity in a column

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 189

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.: PAM % id PAM % id PAM % id PAM % id Residue-specific gap penalties are applied Page 190

See Thompson et al. (1994) for an explanation of the three stages of progressive alignment implemented in ClustalW

Pairwise alignment: Calculate distance matrix Unrooted neighbor- joining tree

Unrooted neighbor- joining tree Rooted neighbor-joining tree (guide tree) and sequence weights

Progressive alignment: Align following the guide tree

Multiple sequence alignment: outline [1] Introduction to MSA Exact methods Progressive (ClustalW) Iterative (MUSCLE) Consistency (ProbCons) Structure-based (Expresso) Conclusions: benchmarking studies [2] Hidden Markov models (HMMs), Pfam and CDD [3] MEGA to make a multiple sequence alignment [4] Multiple alignment of genomic DNA

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: MUSCLE, IterAlign, Praline, MAFFT Page 190

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 ( (Unweighted Pair Group Method with Arithmetic Mean – will be covered later) Construct draft progressive alignment following tree Page 191

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 Page 191

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 Page 191

Access to MUSLCE at EBI

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 Page 190

Iterative approaches: MAFFT Has about 1000 advanced settings!

Multiple sequence alignment: outline [1] Introduction to MSA Exact methods Progressive (ClustalW) Iterative (MUSCLE) Consistency (ProbCons) Structure-based (Expresso) Conclusions: benchmarking studies [2] Hidden Markov models (HMMs), Pfam and CDD [3] MEGA to make a multiple sequence alignment [4] Multiple alignment of genomic DNA

Multiple sequence alignment: consistency 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 Page 192

Consistency-based Algorithms T-Coffee (Consistency Objective Function For alignmEnt Evaluation) Version 2.00 and higher can mix sequences and structures Local and global pair-wise alignments can come from different programs and can be redundant The EL is a position-specific substitution matrix where the score associated with each pair of residues depends on its compatibility with the rest of the library. This library replaces the Mutation data Matrix used in ClustalW. Pair-wise distances are computed A Neighbor joining tree is estimated Sequences are aligned progressively following the topology of the tree

ProbCons—consistency-based approach Combines iterative and progressive approaches with a unique probabilistic model. Uses Hidden Markov Models 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) Page 192

Fig Page 158 ProbCons uses an HMM to make alignments

ProbCons—consistency-based approach Sequence xx i Sequence yy j Sequence zz 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. Page 193

ProbCons output for the same alignment: consistency iteration helps

Multiple sequence alignment: outline [1] Introduction to MSA Exact methods Progressive (ClustalW) Iterative (MUSCLE) Consistency (ProbCons) Structure-based (Expresso) Conclusions: benchmarking studies [2] Hidden Markov models (HMMs), Pfam and CDD [3] MEGA to make a multiple sequence alignment [4] Multiple alignment of genomic DNA

Access to TCoffee: Make a MSA MSA w. structural data Compare MSA methods Make an RNA MSA Combine MSA methods Consistency-based Structure-based Back translate protein MSA Page 194

APDB ClustalW output: TCoffee can incorporate structural information into a MSA Protein Data Bank accession numbers

Summary strategies

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 [2] Hidden Markov models (HMMs), Pfam and CDD [3] MEGA to make a multiple sequence alignment [4] Multiple alignment of genomic DNA

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 user- friendly than others. And most programs are excellent so it depends on your preference. If your proteins have 3D structures, use these to help you judge your alignments. For example, try Expresso at Page 196

[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. [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. Strategy for assessment of alternative multiple sequence alignment algorithms Page 196

BaliBase: comparison of multiple sequence alignment algorithms Page 196

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. But several programs perform better. There is no one single best program to use, and your answers will certainly differ (especially if you align divergent protein or DNA sequences) Page 196

Multiple sequence alignment: outline [1] Introduction to MSA Exact methods Progressive (ClustalW) Iterative (MUSCLE) Consistency (ProbCons) Structure-based (Expresso) Conclusions: benchmarking studies [2] Hidden Markov models (HMMs), Pfam and CDD [3] MEGA to make a multiple sequence alignment [4] Multiple alignment of genomic DNA

Multiple sequence alignment to profile HMMs ► 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 ► HMMs may give more sensitive alignments than traditional techniques such as progressive alignment Page 197

Structure of a hidden Markov model (HMM) main state insert state delete state Fig Page 158

Hidden Markov Models The model accommodates the identities, mismatches, insertions, and deletions expected in a group of related proteins. (A) MSA: Each column may include matches and mismatches (red positions), insertions (green positions), and deletions (purple positions). (B) Each column in the model represents the possibility of a match, insert, or delete in each column of the alignment in A. The HMM is a probabilistic representation of the MSA. Sequences can be generated from the HMM by starting at the beginning state labeled BEG and then by following anyone of many pathways from one type of sequence variation to another (states) along the state transition arrows and terminating in the ending state labeled END. Any sequence can be generated by the model and each pathway has a probability associated with it. Each square match state stores an amino acid distribution such that the probability of finding an amino acid depends on the frequency of that amino acid within that match state. Each diamond-shaped insert state produces random amino acid letters for insertions between aligned columns and each circular delete state produces a deletion in the alignment with probability 1. One of many ways of generating the sequence N K Y L T in the above profile is by the sequence BEG ->Ml ->11 ->M2 ->M3 :>M4 ->END. Each transition has an associated probability, and the sum of the probabilities of transitions leaving each state is 1. The average value of a transition would thus be 0.33, since there are three transitions from most states (there are only two from M4 and D4, hence the average from them is 0.5 ). For example, if a match state contains a uniform distribution across the 20 amino acids, the probability of any amino acid is Using these average values of 0.33 or 0.5 for the transition values and 0.05 for the probability of each amino acid in each state, the probability of the above sequence N K Y L T is the product of all of the transition probabilities in the path and the probability that each state will produce the corresponding amino acid in the sequences, or 0.33 X 0.05 X 0.33 X 0.05 X 0.33 X 0.05 X 0.33 X 0.05 X 0.33 X 0.05 X 0.5 = 6.1 X Since these probabilities are very small numbers, probabilities are converted to log odds scores, and the logarithms are added to give the overall probability score. The secret of the HMM is to adjust the transition values and the distributions in each state by training the model with the sequences. The training involves finding every possible pathway through the model that can produce the sequences, counting the number of times each transition is used and which amino acids were required by each match and insert state to produce the sequences. This training procedure leaves a memory of the sequences in the model. As a consequence, the model will be able to give a better prediction of the sequences. Once the model has been adequately trained, of all the possible paths through the model that can generate the sequence N KY L T, the most probable should be the match-insert-3 match combination (as opposed to any other combination of matches, inserts, and deletions). Likewise, the other sequences in the alignment would also be predicted with highest probability as they appear in the alignment; i.e., the last sequence would be predicted with highest probability by the path match-match-delete-match. In this fashion, the trained HMM provides a multiple sequence alignment, such as shown in A. For each sequence, the objective is to infer the sequence of states in the model that generate the sequences. The generated sequence is a Markov chain because the next state is dependent on the current one. Because the actual sequence information is hidden with­in the model, the model is described as a hidden Markov model

PFAM (protein family) database is a leading resource for the analysis of protein families Page 198

PFAM HMM for lipocalins: resembles a position-specific scoring matrix 20 amino acids position

PFAM HMM for lipocalins: GXW motif GW 20 amino acids

PFAM GCG MSF format

Pfam (protein family) database

PFAM JalView viewer

Alignment Editors Jalview Written in Java Input MSF, aligned FASTA ClustalW alignment Interactive alignment editor Multiple color schemes Can divide in sub-families Produces UPGMA, Neighbor- joining trees and Principal Component Analysis Incorporates information from feature Table Incorporates structural information

SMART: Simple Modular Architecture Research Tool (emphasis on cell signaling) Page 199

[1] Go to NCBI  Domains & Structure (left sidebar) [2] Click CDD [3] Enter a text query, or a protein sequence CDD: Conserved domain database (at NCBI): CDD = Pfam + SMART

CDD = PFAM + SMART Page 199 CDD entry for “globin”

Page 199 CDD entry for “globin”

Page 199 CDD entry for “globin”

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 CDD uses RPS-BLAST: reverse position-specific Page 200

Multiple sequence alignment: outline [1] Introduction to MSA Exact methods Progressive (ClustalW) Iterative (MUSCLE) Consistency (ProbCons) Structure-based (Expresso) Conclusions: benchmarking studies [2] Hidden Markov models (HMMs), Pfam and CDD [3] MEGA to make a multiple sequence alignment [4] Multiple alignment of genomic DNA

MEGA version 4: Molecular Evolutionary Genetics Analysis Download from

MEGA version 4: Molecular Evolutionary Genetics Analysis

MEGA version 4: Molecular Evolutionary Genetics Analysis 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 2

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

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

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 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. Page 203

Page 205 Multiple alignment of genomic DNA at UCSC 50,000 base pairs (at

Page 205 Note conserved regions: exons and regulatory sites (scale: 50,000 base pairs) regulatory

Page 205 Multiple alignment of beta globin gene scale: 1,800 base pairs

Page 205 Multiple alignment of beta globin gene scale: 55 base pairs