Multiple Sequence Alignment (MSA) Exercises and Demonstrations

MPI for Developmental Biology, Tuebingen

Tuesday 10th February 2009

Aidan Budd

Link to the Presentation

Demonstrating Structural Equivalence

Comparing (and aligning) pairs of similar structures demonstrates what it means for a pair of residues to be "structrually equivalent".

At the same time, it demonstrates that there may be regions of two structures for which there is not any such equivalence.

This example is a pair of bacterial toxins 1ji6 and 1i5p

We have aligned the N-terminal regions of these structures using FATCAT

this link is to the aligned structures
this link is the sequence alignment implied by this structural alignment

Here are links to the full-length structures aligned using

We made the alignments with FATCAT and CE as they both provide a pairwise sequence alignment and an easy-to-view structural alignment - not because of the quality of the alignments they produce (although these seem to be often good)

Comparing Sequence and Structural Alignments

Demonstration

How good are different structural alignment tools compared with purely sequence-based alignments?

We'll make a comparison using the example of two serine protease sequences (shown below) - the two sequences are both taken from a "gold-standard" alignment reference dataset (from http://bips.u-strasbg.fr/fr/Products/Databases/BAliBASE2/ref1/test2/5ptp_ref1.html)

By examining the UniProt record, I have highlighted in red the active-site residues for 5ptp.

Note that the sequence below describe the proteins found in the PDB (not the UniProt) records - in this case that means it is after cleavage of signal peptide and activation hexapeptide.

>5ptp Bovine Cationic Trypsin TRY1_BOVIN (beta-trypsin form following Lys-23 autocatalytic clevage)
IVGGYTCGANTVPYQVSLNSGYHFCGGSLINSQWVVSAAHCYKSGIQVRLGEDNINVVEG
NEQFISASKSIVHPSYNSNTLNNDIMLIKLKSAASLNSRVASISLPTSCASAGTQCLISG
WGNTKSSGTSYPDVLKCLKAPILSDSSCKSAYPGQITSNMFCAGYLEGGKDSCQGDSGGP
VVCSGKLQGIVSWGSGCAQKNKPGVYTKVCNYVSWIKQTIASN

>1ton Rat Tonin KLK2_RAT
IVGGYKCEKNSQPWQVAVINEYLCGGVLIDPSWVITAAHCYSNNYQVLLGRNNLFKDEPF
AQRRLVRQSFRHPDYIPLIVTNDTEQPVHDHSNDLMLLHLSEPADITGGVKVIDLPTKEP
KVGSTCLASGWGSTNPSEMVVSHDLQCVNIHLLSNEKCIETYKDNVTDVMLCAGEMEGGK
DTCAGDSGGPLICDGVLQGITSGGATPCAKPKTPAIYAKLIKFTSWIKKVMKENP

(both taken from http://bips.u-strasbg.fr/fr/Products/Databases/BAliBASE2/ref1/test2/5ptp_ref1.html)

Initial PDB files 5ptp 1ton (chain A in both cases)

We will align

structures using

sequences using

Exercise

Carry out a similar exercise yourself with the following amylase sequences.

The three active-site residues in both sequences as described by their UniProt records have been labeled in red in the FASTA format sequences provided below.

>1smd AMY1_HUMAN
GRTSIVHLFEWRWVDIALECERYLAPKGFGGVQVSPPNENVAIHNPFRPWWERYQPVSYK
LCTRSGNEDEFRNMVTRCNNVGVRIYVDAVINHMCGNAVSAGTSSTCGSYFNPGSRDFPA
VPYSGWDFNDGKCKTGSGDIENYNDATQVRDCRLSGLLDLALGKDYVRSKIAEYMNHLID
IGVAGFRIDASKHMWPGDIKAILDKLHNLNSNWFPEGSKPFIYQEVIDLGGEPIKSSDYF
GNGRVTEFKYGAKLGTVIRKWNGEKMSYLKNWGEGWGFMPSDRALVFVDNHDNQRGHGAG
GASILTFWDARLYKMAVGFMLAHPYGFTRVMSSYRWPRYFENGKDVNDWVGPPNDNGVTK
EVTINPDTTCGNDWVCEHRWRQIRNMVNFRNVVDGQPFTNWYDNGSNQVAFGRGNRGFIV
FNNDDWTFSLTLQTGLPAGTYCDVISGDKINGNCTGIKIYVSDDGKAHFSISNSAEDPFI
AIHAESKL

>1bf2 ISOA_PSEAY
DVIYEVHVRGFTEQDTSIPAQYRGTYYGAGLKASYLASLGVTAVEFLPVQETQNDANDVV
PNSDANQNYWGYMTENYFSPDRRYAYNKAAGGPTAEFQAMVQAFHNAGIKVYMDVVYNHT
AEGGTWTSSDPTTATIYSWRGLDNATYYELTSGNQYFYDNTGIGANFNTYNTVAQNLIVD
SLAYWANTMGVDGFRFDLASVLGNSCLNGAYTASAPNCPNGGYNFDAADSNVAINRILRE
FTVRPAAGGSGLDLFAEPWAIGGNSYQLGGFPQGWSEWNGLFRDSLRQAQNELGSMTIYV
TQDANDFSGSSNLFQSSGRSPWNSINFIDVHDGMTLKDVYSCNGANNSQAWPYGPSDGGT
STNYSWDQGMSAGTGAAVDQRRAARTGMAFEMLSAGTPLMQGGDEYLRTLQCNNNAYNLD
SSANWLTYSWTTDQSNFYTFAQRLIAFRKAHPALRPSSWYSGSQLTWYQPSGAVADSNYW
NNTSNYAIAYAINGPSLGDSNSIYVAYNGWSSSVTFTLPAPPSGTQWYRVTDTCDWNDGA
STFVAPGSETLIGGAGTTYGQCGQSLLLLISK

Here are the PDB structure file 1smd 1bf2

As for the serine proteases, align the above (note that pre-calculated results are provided in case the servers are down - try and get these results without consulting these files, however, unless necessary)

pairwise structures using

pairwise sequences using

multiple sequences using probcons (here are the unaligned sequences taken from a BaliBase record 1pamA_ref1 containing both sequences)

execute this from the command line e.g.

probcons 1pamA_ref1.reference.fasta > 1pamA_ref1.probcons.fasta

here is the result

When examining the structural alignments, it may help to label within pymol the active site residues for the different chains

here are some basic instructuctions describing how to use pymol to:

show the sequences
highlight residues
change colour of residues

Do the sequence- and structure-based alignments successfuly align the active-site residues from thetwo sequences?
Do gaps in the structural alignment tend to be associated with secondary structure elements (alpha helices or beta strands) or with loop regions?

MSA from A-Z

Decision-making is central to building a good/accurate/appropriate MSA. Here are some examples of the kind of issues that require these decisions:

where to get an initial set of sequences from?
which software to use to create an initial alignment of the sequences?
whether to exclude any of the initial sequences from the analysis?

if so, which ones?

etc.

Your reasons for building the alignment - i.e. the biological quesiton(s) you are aiming to address using it - play a very important role in helping you reach decisions in response to questions such as these. Thus, there is not just one way to build an alignment comparing mammalian COX genes - the alignment you build will be differednt, depending on what you want to use the alignment for.

Thus, going from the beginning to the end of the process of building an MSA only makes sense in the context of a particular biological questions.

The demonstration/exercise shown below aims to do two things

provide you with a concrete example of building an MSA from scratch
indicate ways in which you might vary this recipe when your sequences/aims are different from those described here (which they almost certainly will be when you carry out similar exercises in your own research)

Given that the other focus of this course is phylogeny, we will take evolutionary questions as the basis for this demonstration and exercise.

We'll go through the analysis step by step "online" using the projector.

After each step, we'll give you a chance to carry out the same actions as I've carried out on your own machines.

However, if you haven't time to carry out these analyses yourself, we've tried to provide files at each stage that you can use to move on to the next step, even if you haven't been able to complete the previous one.

COX1/COX2 Phylogeny

We want to make inferences about the phylogeny of the prostaglandin G/H synthases (COX1/COX2) gene family in mammals. In particular, we would like to estimate when the duplication event occured that yielded these two members of the family in humans. Perhaps we are interested in this question due to reading this paper (here's a static version of the Entrez PubMed Abstract for the paper incase the NCBI pages are down) where we read about there being two COX genes in vertebrates/mammals and reference to an "invertebrate precursor" - when did this duplication occur?

This is a typical evolutionary question - using what we see today - (e.g contemporary human COX1/COX2 protein sequences), we try to infer when (and if possible by what mechanism) the differences we see today have occured.

Identifying Our Initial Sequence of Interest

To begin, we need to find the sequence of at least one of our proteins of interest

A good place to start is searching the UniProt/SwissProt database, which provides high-quality, partially manually-curated sequence and functional annotation data for proteins (as content has relatively high accuracy, and is relatively small, making it faster to search)

We can do this using the EB-eye search tool

We might begin by querying with "human cox1"

This hits many (100+) records in the protein sequence databases

Ideally we would have found just a single record corresponding to our protein of interest - however for several reasons we might hit many records (as is the case here)

Records that mention interactions with cox1 (either as cross-references to other databases, or in the "free text" of the records)
More than one protein with the name cox1

Rather than investitgate all these records to find the one we are looking for, we modify the query e.g. "human cox1 prostaglandin"

This matches just one record - reading the annotation we find that, indeed, this is the gene we are looking for

Notice how we need to use our biological knowledge to aid our bioinformatic searching - generally, the more we know about the biology of our sequences, the better we can judge the results of such searches, and the more interesting questions we can pose using our bioinformatic tools

This task (identifying the sequence/record associated with a given gene/protein/biological entitiy) is one of the main applications of bioinformatic tools (the other is to predict function/structure using comparison to similar genes/proteins/entities with demonstrated function/structure)

This was a relatively easy example - however, if we wanted to find additional information about the cox1 sequence e.g. 3D structure, known protein-protein or protein-chemical interactions etc., we would have to carry out similar kinds of searches of different databases (or, if we're lucky, use cross-references from the UniProt record) All this can take quite a lot of time and effort.

A short-cut that works in many circumstances is to use the automatic entity-recognition and biological-database integration software REFLECT

you can use the tool by cutting-and-pasting a URL into the bar at the top of the REFLECT webpage
or you can install a Firefox plugin to give you a button on your browser that will REFLECT any page you happen to be viewing

This is what happens when we REFLECT our abstract of interest

Protein/gene names are identified and supplied with pop-up boxes containing information about them
Note the "disambiguation" feature when several proteins have the same name
We can go from these pop-ups to find the sequences for the genes/proteins
Can do this with any HTML page e.g.

Chemicals are highlighted in a different colour and have different pop-ups
Note that, using OnTheFly you can do the same thing with PDFs, Excel spreadsheets, and other document types - useful for those of you obtaining gene-lists from screens/microarray experiments etc.

Anyway - here are the UniProt records of the two human sequences COX1 and COX2

Note that these are NOT related to the mitochondrial protein COX1_HUMAN - these mitchondrial proteins were one of the reasons why our initial search of the UniProt database hit more than one record.

Identifying an Initial Set of Proteins Related to Our Sequence of Interest

We now need to identify a set of proteins that have the same structure/are related to our sequence of interest

There are many web resources/databases that provide such sets of proteins, in many cases already built into an MSA

We've already mentioned that different biological questions may well be best addressed by different MSAs. Thus the alignments/sets of sequences we obtain from such resources are unlikely to be the best possible alignments to address our own specific biological question. However, they often provide a good starting place for such an analysis - by carrying out modifications such as those described below, they can be adjusted to suit our specific question

including additional sequences in the alignment
removing sequence from the alignment
adjusting/editing regions to correct misalignments
excluding unneeded regions/columns of the alignment

However, in some cases such an alignment is not available - in which case a typical starting point is a BLAST search.

To build the MSA as far as possible "from scratch", we'll begin by looking at how we'd use a BLAST search for this.

We begin by choosing

to search against the UniProt/SwissProt (we start with a small database to make the BLAST search quicker)
switching the low-complexity filter on (makes it much less likely that we will obtain alignments with low E-values between unrelated sequences)
using other parameters in their default settings

If this search gives us a good starting point for our analysis, then no need to change the way we did the search.

If we hit many sequences, then downloading them all and building an MSA will take longer than necessary.

To change the search so that we hit fewer sequences we could:

query a smaller database
restrict the search to certain taxa

If we find very few/no sequences, then we may well not have enough sequences to build an MSA appropriate for addressing our question of interest.

To carry out a search to identify more sequences we could:

query a larger/more comprehensive database (perhaps located somewhere other than at the NCBI e.g. one of the JGI genome-specific databases)
adjust the query parameters

change the protein substitution matrix

short sequence? go to a higher BLOSSUM/lower PAM matrix
long sequence? go to a lower BLOSSUM/higher PAM matrix

try different gap penalties

use a more sensitive search method, typically based on protein profiles

protein domain database e.g. PFAM or SMART
create your own protein profile

PSI-BLAST
HMMER

We examine the results of the BLAST search

there are several sequences in the database that are very similar to our query
there are also many that are likely related (based on annotation and E-values) but realatively dissimilar to the query

We will have around 40 sequences if we collect both of these two groups of sequences - this is a reasonable number to manipulate, so we will take all of them.

We can easily get both GenPept and FASTA format records (and save them locally) for these sequences by re-formating the BLAST output to show only those with low-enough E-values

When I carried this out recently, a threshold of 1e-5 was appropriate
We can get an overview of the different taxa represnted in the list by opening the "[Taxonomy reports]" link
here is a link to the sequences in FASTA format

Building an Initial MSA

We build an initial MSA from these sequences using MUSCLE at the EBI

MUSCLE is one of the faster MSA tools available, and is reasonably accurate - thus it is a good choice for carrying out an initial alignment
Here is an example of the output from MUSCLE

We examine the alignment in CLUSTALX

There are clearly two sets of sequences, within which all members of a set are more similar to each other than they are to members of the other set.
Both of our sequences of interest are in the same set - all of which are from mammals (apart from one chicken sequence).

Collecting Additional Sequences of Interest

Perhaps the duplication occured within the mammals - but we'd be interested to supplement these sequences with some from other chordates, so we'll query a larger database, restricting our search to non-mammalian chordates.

To identify the terms used by the NCBI taxonomy to describe these taxonomic groups we query the taxonomy with the name(s) of organisms that we know belong to groups whose terms we want to use in restricting the BLAST query

here is the page for humans - this gives us the term used in the NCBI Taxonomy for mammals (Mammalia) and chordates (Chordata)

We repeat the BLAST search this time

using a larger sequence database ("Non-redundant protein sequences - nr")
restricting the sequence space to non-mammalian chordates using the "Entrez Query" box "Chordata [ORGN] NOT Mammalia [ORGN]"
here is the result page
we check the taxonomic groups represented in the set of hit sequences - and see a reasonable representation of different groups
to make the next steps quicker, we'll restrict the set of sequences we'll collect using an E-value threshold of 1e-20
if doing this analysis "for real" I would probably have

collected more sequences at this stage i.e. used a higher E-value threshold
extended the range of organisms searched against - perhaps querying against non-mammalian Metazoans as a first step, restricting this more and more if too many sequences were being identified

here are these sequences in FASTA format

Combing Sequence Sets in one Alignment

It's probably easiest just to bundle all the sequences we have collected so far into one file and to submit this to MUSCLE again

combined FASTA file
To be kind to the EBI webservers, rather than all submiting large-ish (84 sequences is not a "small" query) queries to them at the same time, we will use MUSCLE from the command-line

muscle -in combined_Sp_nrNonMammalChordates.fasta -out combined_Sp_nrNonMammalChordates.fasta.muscle

MUSCLE alignment

In this case, we did not spend any time/effort creating a high-quality initial alignment e.g. by correcting mis-alignments, excluding unneeded sequences etc.

Sometimes, however, we want to incorporate additional sequences into an alignment that we have previously edited and manipulated - in which case we will not want to simply combine all (old and new) sequences together and completely re-align them.

Instead, we want to align the new sequences to the initial alignment while preserving the relationships of the sequecnes in the initial alignment to each other.

We can do this using CLUSTALX (in "profile alignment" mode.)

We can also do this using t_coffee (but only from the command-line)

t_coffee sequences.fasta -profile=prf1.aln -outfile=combined_profiles.aln

sequences.fasta contains any number of sequences - these are assumed to be unaligned
prf1.aln is a sequence alignment
the sequences in sequences.fasta will each be aligned, individually, to prf1.aln, to create a new MSA incorporating all the sequences in sequences.fasta and prf1.aln
it's possible to integrate more than one profile at a time

t_coffee sequences.fasta -profile=prf1.aln,prf2.aln,prf3.aln -outfile=combined_profiles.aln

MUSCLE also allows you to align two alignments with each other (known as profile-profile alignment), although not a set of sequences individually to a pre-existing alignment - this functionality is also only available from the command line

muscle -profile -aln1.fasta -in2 aln2..fasta -out combinedAlignment.fasta
this aligns the alignments in the two files aln1.fasta and aln2.fasta to each other and writes them to combinedAlignment.fasta

Adjusting Sequence Identifiers

As indicated, to make decisions about which sequences to exclude, we need to always consider the biological questions we are trying to address with our alignment.

In this case, we are interested in the timing of the duplication event leading to the two mammalian COX genes - thus we need to change the sequence identifiers so that we can:

use them to recognise the organisms they come from.
as we have multiple sequences from the same organism in our set, we also want to be able to quickly determine which of several sequences from a given organism is associated with a given identifier

When choosing new identifiers for the sequences we need to take into account that:

each sequence in the file should have a unique identifier - many/most MSA software will fail if this is not the case.
some MSA formats only allow a maximum of 10-character identifiers (i.e. PHYLIP format) - therefore we need to make sure that the first 10 characters for every sequence identifier are unique. Thus, we typically place a unique identifier (e.g. a UniProt Accession Number) in the first characters of the identifier
quite a lot of the software that uses MSAs is unable to accept sequence identifiers that include anything other than the alphanumeric characters and underscore (i.e. a-z, A-Z, 0-9 and _) - some software even has probles with lowercase letters

Here is a copy of the MUSCLE alignment created above with the identifiers altered - although note that the policy described above has not been carefully followed closely.

Preparing a "Final" Alignment

Almost always you will want to exclude some of the sequences from such an alignment. This might be for several different reasons:

Many identical/almost identical sequences in the alignment

Keeping them in the alignment adds very little extra information to your analysis, and can slow calculations down considerably
You would usually remove all but one of each such group of sequences
Note, however, that for some applications you may expect and even want many very similar sequences - so this (like everything on this list) needs to be interpreted in the context of the aim of the analysis

Sequences look like they may contain errors

Positions in a sequence where the residue described in the sequence file is different from that which is specified in the organism's genome will introduce systematic errors into any analysis of that sequence that makes use of that residue. Clearly we want to avoid such errors in our analyses

Such errors could have many different sources - for example:

sequencing errors
contig-assembly errors
gene-prediction errors

Likely errors are recognised by finding regions of an alignment where

all/most sequences are conserved
where the likely "wrong" sequence isn't conserved
where the rest of the "wrong" sequence (especially the region flanking the likely error) is conserved in a similar way to other sequences in the alignment
THIS NEED NOT BE AN ERROR! Instead it could be the signature of some atypical evolution occuring in that region of the sequence. However, it's more likely to be an error

Unless you are particularly interested in that sequence, you should discard it
If you ARE partcicularly interested in that sequence, you will need to do further investigations to try and determine whether or not it is an error

Sequences that contain many gaps

Many analyses (particularly evolutionary analyses) can only make use of columns in which residues are present from all the sequences included in an alignment
For such analyses, as far as possible, we want to exclude sequences that include gaps in columns that we would otherwise expect to use in our analysis
Again, these gaps might be due to errors, or be biologically valid/relevant - as before, the decision on whether to keep a particular sequence or not depends on the aim of the alignment - if the sequence is parrticularly important for the analysis, we will of course keep it in the alignment, even if it is present in a short fragment

Here is a copy of the above MUSCLE alignment, with altered sequence identifiers, trimmed of many sequences due to reasons described above.

Estimating a Phylogeny from the Alignment

Before estimating the phylogeny, we need to take into account that phylogenetic applications of MSAs are known to be strongly influenced by the inclusion of columns in the analysis where residues in the column are not realated by simple substitution events.

Therefore, we use the GBLOCKS server to extract those regions of the alignment that are considered most likely to contain only columns where all residues are realated via substitution events.

Here is the HTML page showing which regions GBLOCKS kept and discarded from the alignment
Here is the FASTA format GBLOCKed alignment

Finally, we use a very quick and innacurate method to estimate phylogeny from this alignment - neighbour-joining as implemented by CLUSTALX - click here for a tree estimated in this way

(We will look at a more accurate approach in tomorrow's session which focuses on phylogenies)

Below is an image of the tree estimated in this way

Interestingly, there seem to be two copies of the gene also in the urochodate Ciona intestalis and the cephalochordate Branchiostoma floridae - however, the duplications appear to have occured independently of each other, and of the duplication that occurred to yield the two vertebrate copies of the gene.

Mammalian CDKs 1/2/3

Now you have a chance to carry out an similar analysis, this time on your own.

This time, suppose you are interested in a similar question, only this time you want to investigate the timing of the duplications that yielded the three mammalian CDK genes CDK1, CDK2, and CDK3 (for example, see this abstract [Santamaria et al.2007] which mentions all three of the proteins).

Identify the UniProt record(s) associated with at least one of these genes
Use BLAST to identify an appropriate set of proteins to build an alignment from, and download these sequences to a local FASTA-format file
Use MUSCLE to build a multiple sequence alignment of the sequences

if possible, use MUSCLE from the command line e.g.

muscle -in unalignedSequenceFile.fasta -out alignedSequenceFile.fasta

if not, use the MUSCLE server at the EBI

Edit the names of the sequences so that you can tell which organisms are from (while keeping the sequence identifiers unique)
Examine the alignment in CLUSTALX (or JalView)

remove sequences from the analysis to make it more appropriate for the phylogenetic analysis
if necessary, carry out further BLAST searches to obtain additional sequences for the alignment and repeat the MSA and sequence-removal stages

Remove poorly-aligned regions from the alignment using the GBLOCKS server
Quickly estimate a phylogeny from the GBLOCKed alignment, and visualise it using Njplot

Based on this tree, when do you think the three lineages diverged from each other? (i.e. when do you think their ancestral genes underwent duplications?)

If you have time, repeat a similar analysis as described above, this time begining with the appropriate TreeFam family as the initial source of sequences
(If you can't find the appropriate TreeFam record, here's the link to it).

Conserved Hydrophobic Core

Just to remind you - here we can see clearly that the non-polar residues can be almost completely excluded from the core of a globular protein.

Visualising and Editing Alignments

The aim of this exercise is to give you some practise using alignment viewers/editors to (i) identify and (ii) correct mis-aligned regions.

Download the alignments in the left column of the table below, all of which contain regions that are misaligned (in comparison with a set of reference alignments from BaliBase).

The second column links to an image of the alignment, with a box surrounding a region that contains a misalignment.

Using JalView and CLUSTALX, try to identify and correct the misalignment.

To see whether you were sucessful, compare your edited alignments with the reference alignments (provided in the third column in FASTA format, in the fourth as HTML, which highlights the regions BaliBase is confident are correctly aligned). The fifth column links to an image highlighting more closely the region that is misaligned, along with an indication of how the region might be corrected. The final column links to the BaliBase page for the alignment.

Alignment with Misalignment	Misaligned Region Highlighted	Reference BaliBase Alignment (FASTA)	Reference BaliBase Alignment (HTML - Core Regions Highlighted)	Edit Highlighted	BaliBase Page
BB12003.mis.fasta	BB12003.mis.jpg	BB12003.ref.fasta	BB12003.html	BB12003.edit.jpg	BB12003-BaliBase
BB12032.mis.fasta	BB12032.mis.jpg	BB12032.ref.fasta	BB12032.html	BB12032.edit.jpg	BB12032-BaliBase
BB50013.mis.fasta	BB50013.mis.jpg	BB50013.ref.fasta	BB50013.html	BB50013.edit.jpg	BB50013-BaliBase
BB50002.mis.fasta	BB50002.mis.jpg	BB50002.ref.fasta	BB50002.html	BB50002.edit.jpg	BB50002-BaliBase

Comparing MSA Tools

Demonstration

As you'll do in the exercises below, here we'll look at what happens when we re-align a reference alignment from BaliBase (1aboA_ref1) using several different pieces of automatic alignment software.

Exercises

You will be told to take one from the list of BaliBase2 list of reference alignments.

Download the RSF format of the alignments of interest.

Load alignment into CLUSTALX locally, remove all gaps from all sequences (Edit->Select all sequences, Edit->Remove all Gaps) and save in FASTA format.

Alignment->Output Format Options->Output Order change to "Input" (this will preserve the order of the sequences in any alignments done by CLUSTALX to be the same as the order in the input alignment - makes it easier to compare the results of a CLUSTALX alignment with thre reference alignment)

Re-align the sequences locally using CLUSTALX (Alignment->Do Complete Alignment) - REMEMBER TO CHOOSE A DIFFERENT NAME FOR THE ALIGNMENT OUTFILE!

Compare the automatic alignment created by CLUSTALX with the reference BaliBase alignment - it will help if you re-arrange the (vertical) order of the sequences in the CLUSTALX alignment.

How many columns are mis-aligned in the CLUSTALX alignment i.e. where residues are aligned in the same column in the CLUSTALX alignment which are not aligned in the same column in the BaliBase alignment? NB - Only consider those columns in the BaliBase alignment which are underlined/in upper case - these are the ones which are assessed as being reliably aligned according to structural comparisons

Do the same for other alignment tools such as:

MAFFT
PROBCONS
MUSCLE
T-COFFEE

Finding Pre-Calculated Alignments

Demonstration

If you can find a pre-calculated alignment that already contains a set of sequences that is useful for addressing your problem, you may well be able to save lots of time. Using the databases/resources below, we'll look at how you can get access to alignments that you can download yourself and examine locally.

Ensembl (protein families)
TreeFam
PFAM
HOMSTRAD
OPTIC (Try using the Amniotes - query with Ensembl ID)

>SRC_HUMAN|P12931|Proto-oncogene tyrosine-protein kinase Src (EC 2.7.10.2) ENSG00000197122
GSNKSKPKDASQRRRSLEPAENVHGAGGGAFPASQTPSKPASADGHRGPSAAFAPAAAEP
KLFGGFNSSDTVTSPQRAGPLAGGVTTFVALYDYESRTETDLSFKKGERLQIVNNTEGDW
WLAHSLSTGQTGYIPSNYVAPSDSIQAEEWYFGKITRRESERLLLNAENPRGTFLVRESE
TTKGAYCLSVSDFDNAKGLNVKHYKIRKLDSGGFYITSRTQFNSLQQLVAYYSKHADGLC
HRLTTVCPTSKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTRVAIKTLK
PGTMSPEAFLQEAQVMKKLRHEKLVQLYAVVSEEPIYIVTEYMSKGSLLDFLKGETGKYL
RLPQLVDMAAQIASGMAYVERMNYVHRDLRAANILVGENLVCKVADFGLARLIEDNEYTA
RQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELTTKGRVPYPGMVNREVLDQVERG
YRMPCPPECPESLHDLMCQCWRKEPEERPTFEYLQAFLEDYFTSTEPQYQPGENL

Exercise

Using these (and perhaps other alignment resources you might find) find a pre-calculated MSA that is will be a good starting place for
building an alignment to investigate:

the evolution of FGF genes (e.g. FGF4_HUMAN) in the early vertebrate lineage
variation in the 3D structure of tyrosine kinase domains within the animals

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