Multiple sequence alignment

Sequence alignments can be used to study the relationship(s) between sequences in sets of more than two sequences. This application is particularly useful when studying the relationships between a similar type of gene product that is expressed by different organisms, like analyzing CFTR sequences from several different species, or when studying similar, yet divergent, sequences within the same organism, as the variance in troponin I isoforms in Homo sapiens.

Often, a primary focus of a multiple sequence alignment is to identify, within several related sequences, regions that are highly conserved in identity orsimilarity, and therefore probably have functional and/or structural significance. Many factors affect the analysis of conserved regions withinrelated sequences, such as the number of sequences included in the analysis, and the ratio of the number of very similar (almost identical) sequences to thenumber of more distantly related sequences. Divergent sequences can cause problems in a multiple sequence alignment. It is more difficult to identifythe correct alignment when two sequences that are related throughout part of the sequence also contain large sections that diverge. Therefore, the errorrates in the alignment increase as divergence increases. These errors in the alignment can cause the related part of the sequences to show lower similaritythan they actually have, and this sort of error is often amplified in subsequent steps. ClustalW (1), a commonly used multiple sequence alignment program, addresses the problemsassociated with alignment of divergent sequences in several ways. Individual weights are assigned to each sequence in a partial alignment such thatnear-duplicate sequences are down-weighted and divergent sequences are up-weighted. Also, the amino acid substitution matrices at different alignmentstages are chosen according to the divergence of the sequences to be aligned. Residue-specific gap penalties and locally reduced gap penalties in hydrophilicregions increase the penalty for opening new gaps in regions of regular secondary structure. Therefore, it increases the likelihood that gaps willoccur in loop regions than in highly structured regions such as alpha helices and beta sheets. Highly structured regions are commonly very important to thefold and function of a protein, and so divergence is often biologically less likely in these areas. For similar reasons, existing gaps receive locallyreduced gap penalties to encourage the opening up of new gaps near the existing ones. These features have been designed into ClustalW to produce multiplesequence alignments that are biologically meaningful.

First, use the example of analyzing the variance in troponin I isoforms in Homo sapiens to get acquainted with multiple sequence alignments with ClustalW. ClustalW is available at many bioinformatics web sites, but the EBI site is chosen here, for its niceinterface and graphical display. Accept the default values on the submission form, andscroll down the page until the box for pasting in the sequences to be aligned appears. Now, the troponin I sequences must be obtained. For the sake of time, instead of searching with the string "troponin I human",the pertinent accession codes are supplied for this exercise. Open a new browser window to the NCBI home page . Locate the "Search" box at the top of the page and select"Protein" to search the protein database. Type in the accession codeTNNI3_HUMAN in the box to the right, and click on "Go". The results of this search will become part of the search history. Select the history link from the menuunder the query box to verify this. Now, search "Protein" again, using the accession code TNNI2_HUMAN, then perform one last search using the accession codeTNNI1_HUMAN. As an illustration of the search history function in Entrez, combine the results of these three searches. Do this using the boolean operator "OR" inbetween the number assigned to each search in the history listing. For instance, if the Search History gave the following list, #3 Search TNNI1_HUMAN 17:44:55 #2 Search TNNI2_HUMAN 17:44:49#1 Search TNNI3_HUMAN 17:41:47 then "#1 OR #2 OR #3" would be the search string. The boolean operatorOR is specifying that all results included in any of the above searches be combined. Using the boolean AND operator would specify only results that are common toall three searches, which in this case would yield zero results. To view the results, click on "GO" once you have entered the search string into the query box.Now, the combined search should return all three of the desired troponin I isoforms. Check the box to the left of each accession code for all three results.Just above the list of results, find the region on the left that states "Display Summary" and change "Summary" to "FASTA". This should yield the amino acid sequences in FASTA format for the3 proteins that were selected. Copy and paste each sequence into the ClustalW window, pressing return after pasting each entry. There does not have to be aline space between entries, as long as the FASTA identifying line starts on a new line. However, it does not hurt to have a line space between entries,either. Note that the numbered search description line from Entrez is not part of the FASTA format. The numbered search description line will looksomething like this, 1: P19237. Reports Troponin I, slow ...[gi:1351298] and should be omitted from the ClustalW search query. Also, check that the description header for the sequencein FASTA format does not take up more than one line. If it does, shorten the description so that it only occupies one line.Press the "Run" button, and click on the link for the browser window that displays the results, if this option is given. A text page will appearwith the alignment scores.