multiple sequence alignment (msa)cschweikert/cisc4020/multseqalign… · multiple sequence...

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 TorontoSequence

relationships

If the MSA is incorrect, the

above inferences are incorrect!

Chapter 12

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

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

Multiple sequence alignment: features

• some aligned residues, such as cysteines that formdisulfide 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 ofinsertions or deletions (indels)

Multiple sequence alignment: uses

• MSA is more sensitive than pairwise alignmentto 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 consensussequences identifiable by MSA

I HvCO4

I OsC

I HvCO7

I OsF

I HvCO3

I OsB

I AtCO

I AtCOL3

I OsHd1

I HvCO1

I HvCO8

I OsG

II AtCOL6

II OsJ

IV HvCO9

IV OsH

IV OsI

ZCCT2 Ha

ZCCT2 Hb

ZCCT2 Tm

ZCCT2 Td

ZCCT1 TmG3116

ZCCT1 TmDV92

ZCCT1 Td

III AtCOL9

III OsN

83

82

91

99

60

66

73

94

56

53

99

I HvCO4

IV OsI

I OsC

III OsN

I HvCO7

I OsF

I HvCO3

I OsB

I AtCO

I AtCOL3

I OsHD1

I HvCO1

I HvCO8

I OsG

II AtCOL6

II OsJ

IV HvCO9

IV OsH

III AtCOL9

HvZCCT-Ha

HvZCCT-Hb

TmZCCT2

TdZCCT2

TmZCCT1-G3116

TmZCCT1-DV92

TdZCCT1

Photoperiod

response

MSA of ZCCT genes

Cluster analysis of the CCT domains

Putative Zinc fingers

Exon 1 Exon 2CCTZF

IV

II

III

CCT domains

EMS

co-7

Yan et al. 2004. Science 303:1640

Fig. from Boris Steipe Univ. of Toronto

MSA. Technical difficulties

MSA. Technical difficulties


1. Uses of MSA


1. Select sequences



1. Exact algorithms



1. Stochastic

2. Non-stochastic



1. MEGA

2. JALVIEW

MSA: Selection of sequences

• Use database searching to identify related proteins.

• Select homologous sequences (sequences sharing common ancestor).

• Global MSA programs are designed to align sequences that are similar over their entire

length. If sequences are different in length trim the parts that are not similar based on the

database search.

• PSI-Blast can help to define these region.

• Alternatively use local MSA programs (Gibbs sampler, Match-Box, or MACAW)

• Select a “representative” group of sequences. Alignments with large number of

sequences are slow to compute and hard to analyze.

• The inclusion of a large number of a particular group of closely related proteins

will dominate the alignment or profile (in spite of weighting schemes).

REMEMBER: MSA programs will produce alignments even

if you provide unrelated sequences!

Past problem: not enough related sequences.

Today’s problem: too many related sequences!


1. Uses of MSA


1. Select sequences



1. Exact algorithms



1. Stochastic

2. Non-stochastic



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 -)

43 2 Sum-of-pairs distance = 4 + 3 + 2 = 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) http://bibiserv.techfak.uni-bielefeld.de/dialign/.

• 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 # mismathes (including -) Seq.3 ATGAAG Gap ext.= 1


1. Uses of MSA


1. Select sequences



1. Exact algorithms



1. Stochastic

2. Non-stochastic



1. MEGA

2. JALVIEW

MSA: Exact algorithmMSA program

• Multidimensional dynamic programming

• Optimizes sum-of-pairs

• More accurate than progressive methods

• BUT… Time proportional to Ln

• Practical to ~10 seq. of L<200-bp

DCA (Divide & Conquer Algorithm)

• Sits on top of MSA program

• Produces simultaneous MSA

• Cuts seq. in subsets, that are fed into MSA

• Practical to ~20-30 seq. of L<200-bp

• Easy WEB submission

OMA (Optimal Multiple Aligment)

• Iterative implementation of DCA

• Speeds up DCA

• Decreases memory requirements

Progressive algorithms (ClustalW, MultAlign, AMPS)

GARFIELDTHEFASTCAT---

GARFIELDTHELASTFATCAT

GARFIELD THE LAST FAST CAT GARFIELD THE FAST CAT GARFIELD THE VERY FAST CAT THE FAT CAT

GARFIELDTHEVERYFASTCAT

GARFIELDTHEFASTCAT----

GARFIELDTHELASTFAT-CAT

--------THEFAT-----CAT


GARFIELDTHEFASTCAT---

GARFIELDTHELASTFATCAT

--------THEFA-----TCAT


GARFIELDTHEFAS----TCAT

GARFIELDTHELASTFA-TCAT

DCA alignment

ClustalW Blosum62 Gap 11-1

Cheaper to open distal 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 (a little like aphylogenetic 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 verypopular subject in the ‘80s. Fitch and Yasunobu (1974)

may have first proposed the idea, but Hogeweg andHesper (1984) and many others worked on the topic before

Feng and Doolittle (1987)—they made one

important contribution that got their names attached to thisalignment 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.

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

Use ClustalW to do a progressive MSA

http://www2.ebi.ac.uk/clustalw/

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

Progressive MSA stage 1 of 3:generate global pairwise alignments

five distantly related lipocalins

best score

Progressive MSA stage 1 of 3:generate global pairwise alignments

Start of Pairwise alignments

Aligning...

Sequences (1:2) Aligned. Score: 84










five closely related lipocalins

best score

Number of pairwise alignments needed

For n sequences, (n-1)(n) / 2

For 5 sequences, (4)(5) / 2 = 10

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

(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

Progressive MSA stage 2 of 3:generate a guide tree calculated from

the distance matrix

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.”• 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 trustthat those gaps are most believable

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

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

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 presenceof conserved or divergent sequences, e.g.:

PAM20 80-100% idPAM60 60-80% idPAM120 40-60% idPAM350 0-40% id

• Residue-specific gap penalties are applied

Iterative algorithmsRecurrent modifications of suboptimal solutions

Stochastic Iterative Algorithms

SAGA

• Uses a ‘Genetic Algorithm’

• Can use different objective functions (e.g. Coffee)

• Mutations randomly insertion or shift gaps

• Sequences can recombine

• Sequences evolve, higher OF scores survive

GAs and HMMs have probed rather

disappointing in ab initio alignments.

Better: Pre-compute MSA with other

program and then use this ones for

optimization

Evolution of a seq. alignment by recombination

Compatible ends

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 redundantThe 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

Benchmark tests (from Notredame 2002)

ClustalW: performed well on Ref. sets 1-3, but poorly on 4-5 when long

internal or terminal gaps are required.

When large gaps required T-Coffee and DiAlign perform better

Summary strategies

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 inormation

Alignment Editors

Alignment Visualization

Multiple Sequence Alignment

At the end of the day ...

• Use more than one alignment method,

• Make sure you have the right sequences.

• Don't align parts of sequences that can't be aligned

(because they are not homologuous).

• Realize problems from multi-domain proteins.

• Above all, use your common sense.



From Boris Steipe Univ. of Toronto

Hidden Markov Models

Bioinformatics, Sequence and Genome Analysis (pg205) , Mount.

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 0.05. 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 10-10. 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 with-in the model, the model is described as a hidden Markov model

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 alignmentsthan traditional techniques such as

progressive alignment

Simple Hidden Markov Model

Observation: YNNNYYNNNYN

(Y=goes out, N=doesn’t go out)

What is underlying reality (the hidden state chain)?

R

S

0.15

0.85

0.2

0.8

P(dog goes out in rain) = 0.1

P(dog goes out in sun) = 0.85

GTWYA (hs RBP)

GLWYA (mus RBP)

GRWYE (apoD)

GTWYE (E Coli)

GEWFS (MUP4)

An HMM is constructed from a MSA

Example: five lipocalins

GTWYA

GLWYA

GRWYE

GTWYE

GEWFS

PositionProb. 1 2 3 4 5p(G) 1.0p(T) 0.4p(L) 0.2p(R) 0.2p(E) 0.2 0.4p(W) 1.0p(Y) 0.8p(F) 0.2p(A) 0.4p(S) 0.2

GTWYA

GLWYA

GRWYE

GTWYE

GEWFS

Prob. 1 2 3 4 5p(G) 1.0p(T) 0.4p(L) 0.2p(R) 0.2p(E) 0.2 0.4p(W) 1.0p(Y) 0.8p(F) 0.2p(A) 0.4p(S) 0.2

P(GEWYE) = (1.0)(0.2)(1.0)(0.8)(0.4) = 0.064

log odds score = ln(1.0) + ln(0.2) + ln(1.0) + ln(0.8) + ln(0.4) = -2.75

GTWYA

GLWYA

GRWYE

GTWYE

GEWFS

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) = -2.53

G:1.0 W:1.0

T:0.4

L:0.2

R:0.2

E:0.2

Y:0.8

F:0.2

A:0.5

E:0.5

S:1.0

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: 411.45 bits

Minimum score: 353.73 bits

Maximum score: 460.63 bits

Std. deviation: 52.58 bits

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: 1034351005

histogram(s) saved to: [not saved]

POSIX threads: 2

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

HMM : x

mu : -123.894508

lambda : 0.179608

max : -79.334000

HMMER: search an HMM against GenBankScores for complete sequences (score includes all domains):

Sequence Description Score E-value N

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

gi|20888903|ref|XP_129259.1| [Mus] (XM_129259) ret 461.1 1.9e-133 1

gi|132407|sp|P04916|RETB_RAT Plasma retinol- 458.0 1.7e-132 1

gi|20548126|ref|XP_005907.5|[Homo] (XM_005907) sim 454.9 1.4e-131 1

gi|5803139|ref|NP_006735.1| (NM_006744) ret 454.6 1.7e-131 1

gi|20141667|sp|P02753|RETB_HUMAN Plasma retinol- 451.1 1.9e-130 1

.

.

gi|16767588|ref|NP_463203.1|[Salmonella] (NC_003197) out 318.2 1.9e-90 1

gi|5803139|ref|NP_006735.1|: domain 1 of 1, from 1 to 195: score 454.6, E = 1.7e-131

*->mkwVMkLLLLaALagvfgaAErdAfsvgkCrvpsPPRGfrVkeNFDv

mkwV++LLLLaA + +aAErd Crv+s frVkeNFD+

gi|5803139 1 MKWVWALLLLAA--W--AAAERD------CRVSS----FRVKENFDK 33

erylGtWYeIaKkDprFErGLllqdkItAeySleEhGsMsataeGrirVL

+r++GtWY++aKkDp E GL+lqd+I+Ae+S++E+G+Msata+Gr+r+L

gi|5803139 34 ARFSGTWYAMAKKDP--E-GLFLQDNIVAEFSVDETGQMSATAKGRVRLL 80

eNkelcADkvGTvtqiEGeasevfLtadPaklklKyaGvaSflqpGfddy

+N+++cAD+vGT+t++E dPak+k+Ky+GvaSflq+G+dd+

gi|5803139 81 NNWDVCADMVGTFTDTE----------DPAKFKMKYWGVASFLQKGNDDH 120

HMMER: search an HMM against GenBankmatch to a bacterial lipocalin


*->mkwVMkLLLLaALagvfgaAErdAfsvgkCrvpsPPRGfrVkeNFDv

M+LL+ +A a ++ Af+v++C++p+PP+G++V++NFD+

gi|1676758 1 ----MRLLPVVA------AVTA-AFLVVACSSPTPPKGVTVVNNFDA 36

erylGtWYeIaKkDprFErGLllqdkItAeySleEhGsMsataeGrirVL

+rylGtWYeIa+ D+rFErGL + +tA+ySl++ +G+i+V+

gi|1676758 37 KRYLGTWYEIARLDHRFERGL---EQVTATYSLRD--------DGGINVI 75

eNkelcADkvGTvtqiEGeasevfLtadPaklklKyaGvaSflqpGfddy

Nk++++D+ +++ +EG+a ++t+ P +++lK+ Sf++p++++y

gi|1676758 76 -NKGYNPDR-EMWQKTEGKA---YFTGSPNRAALKV----SFFGPFYGGY 116

HMMER: search an HMM against GenBank

Scores for complete sequences (score includes all domains):

Sequence Description Score E-value N

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

gi|3041715|sp|P27485|RETB_PIG Plasma retinol- 614.2 1.6e-179 1

gi|89271|pir||A39486 plasma retinol- 613.9 1.9e-179 1

gi|20888903|ref|XP_129259.1| (XM_129259) ret 608.8 6.8e-178 1

gi|132407|sp|P04916|RETB_RAT Plasma retinol- 608.0 1.1e-177 1

gi|20548126|ref|XP_005907.5| (XM_005907) sim 607.3 1.9e-177 1

gi|20141667|sp|P02753|RETB_HUMAN Plasma retinol- 605.3 7.2e-177 1

gi|5803139|ref|NP_006735.1| (NM_006744) ret 600.2 2.6e-175 1


*->meWvWaLvLLaalGgasaERDCRvssFRvKEnFDKARFsGtWYAiAK

m+WvWaL+LLaa+ a+aERDCRvssFRvKEnFDKARFsGtWYA+AK

gi|5803139 1 MKWVWALLLLAAW--AAAERDCRVSSFRVKENFDKARFSGTWYAMAK 45

KDPEGLFLqDnivAEFsvDEkGhmsAtAKGRvRLLnnWdvCADmvGtFtD

KDPEGLFLqDnivAEFsvDE+G+msAtAKGRvRLLnnWdvCADmvGtFtD

gi|5803139 46 KDPEGLFLQDNIVAEFSVDETGQMSATAKGRVRLLNNWDVCADMVGTFTD 95

tEDPAKFKmKYWGvAsFLqkGnDDHWiiDtDYdtfAvqYsCRLlnLDGtC

tEDPAKFKmKYWGvAsFLqkGnDDHWi+DtDYdt+AvqYsCRLlnLDGtC

gi|5803139 96 TEDPAKFKMKYWGVASFLQKGNDDHWIVDTDYDTYAVQYSCRLLNLDGTC 145

Two kinds of multiple sequence alignment resources

Text-based or query-based searches:CDD, Pfam (profile HMMs), PROSITE

[2] Multiple sequence alignment by manual input

Muscle, ClustalW, ClustalX

[1] Databases of multiple sequence alignments

BLOCKS

CDD

Pfam

SMART

DOMO (Gapped MSA)

INTERPRO

iProClass

MetaFAM

PRINTS

PRODOM (PSI-BLAST)

PROSITE

Databases of multiple sequence alignments

These

Use

HMMs

PFAM (protein family) database:

http://pfam.sanger.ac.uk/

PFAM (protein

family) text search result

PFAM HMM for lipocalins

20 amino acids

position

PFAM HMM for lipocalins: GXW motif

G W

20 amino acids

PFAM GCG MSF format

Pfam (protein family) database

PFAM JalView viewer

SMART: Simple Modular

Architecture Research Tool(emphasis on cell signaling)

Page 338

SMART: lipocalin result

BLOCKS

CDD

Pfam

SMART

DOMO (Gapped MSA)

INTERPRO

iProClass

MetaFAM

PRINTS

PRODOM (PSI-BLAST)

PROSITE

Databases of multiple sequence alignments

Conserved

Domain

Database

(CDD) at NCBI =

PFAM + SMART

[1] Go to NCBI � Structure[2] Click CDD

[3] Enter a text query, or a protein sequence

CDD: Conserved domain database

CDD: Conserved domain database

CDD=

PFAM+

SMART

Purpose: to find conserved domainsin the query sequence

Query = your favorite protein

Database = set of many position-specificscoring matrices (PSSMs), i.e. a set of MSAs

CDD is related to PSI-BLAST, but distinct

CDD searches against profiles generatedfrom pre-selected alignments

CDD uses RPS-BLAST: reverse position-specific

multiple sequence alignment (msa)cschweikert/cisc4020/multseqalign… · multiple sequence...

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