evolutionary relatedness between two species. In contrast, local sequence alignment focuses on stretches of high similarity, and thus, it compares discrete parts of the sequence of interest to sequences in the GenBank. Pairwise alignment aims to find the best way to match two sequences. Multiple sequence alignment compares the sequence of interest to many other sequences in the GenBank to find regions of the sequence that are conserved through evolution (Fig. 2.2).
Figure 2.2: Global alignment versus local alignment: Global sequence alignment aims to find a GenBank sequence that shows significant overall similarity to the query sequence. Local sequence alignment attempts to find a GenBank sequence that shows discrete regions of significant similarity.
The two major problems encountered when performing sequence alignments are: (1) Due to the vast amount of data available in the GenBank, it takes considerable time to perform a thorough comparison, and (2) sequences can be similar at random. To overcome these problems (a) sequences are divided into short segments (words) and these segments are simultaneously compared to sequences in the GenBank and (b) alignments are scored using a scoring function. The most commonly used alignment algorithm is Basic Local Sequence Alignment Tool or BLAST. BLAST uses a 7-15 nucleotide word size. Every time there is a nucleotide match between the query and the subject a +1 is added to the score. If there is nucleotide mismatch, a -2 is subtracted from the score. Often it is useful to introduce breaks (gap) into the alignment to generate a better overall match between two words. Introducing a gap results in a -3 penalty while extending a gap results in a -1 penalty. The alignment with the highest overall score is the best.
An example of how alignments are scored is seen below:
Query CCC Score = 1 match + 2 mismatches = (+1) + 2 x (-2) = -3
After shifting the query to the right:
Query CCC Score = 2 matches + 1 mismatch = 2 x (+1) + 1 x (-2) = 0
The second alignment has the highest score This means, it is the best.
An example of how the introduction of gaps improves alignments is seen below:
Query AGCAC Score = 2 matches + 2 mismatches = 2 x (+1) + 2 x (-2) = -2
After introducing a gap
Query AGCAC Score = 4 matches + gap = 4 x (+1) + 1 x (-3) = -1
The alignment with the gap is a better match between the two sequences. BLAST divides the sequence of interest to words. Once the highest scoring arrangements are found the alignment is extended to find the best overall alignment. To account for similarity between sequences that takes place at random the Expected value or E-value is introduced. The E-value is a parameter that describes the number of random hits with a particular word size. Essentially the E-value represents the background noise (significance threshold) of an alignment. The closer the E-value is to zero the better is the alignment.
Secondary structure prediction of nucleic acids is straightforward, because secondary structure formation in nucleic acids follows the simple rule of Watson- Crick base pairing: A pairs with T or U using two H-bonds whereas G pairs with C using three H-bonds, thus G-C pairs are more stable than A-T pairs. Among the potential secondary structures the one with the lowest free energy is the most likely structure. The most popular algorithm to predict RNA structure is Mfold, where M stands for multiple, meaning that it generates more than one potential structure and fold stands for structure.
2.6 Identifying Conserved Sequence Elements (Invariable Blocks)
First you will use the sequence of the sensor from the model organism Bacillus subtilis to find toxin sensor sequences in different organisms. Then, you will compare the sequences of the toxin sensor in these different organisms using multiple sequence alignment to identify blocks in the sequence that did not change throughout evolution. Once you have identified the invariable blocks of the toxin sensor, you will choose one that you subject to mutagenesis. You will delete or insert nucleotides into the invariab