s the power of computer science to analyze biology data and generate new information. This new field of life sciences has emerged, because (1) there has been an exponential increase in biological information due to genome sequencing, gene expression profiling (microarray data) and determination of macromolecular 3D structures, and (2) there has been a vast improvement in the access to computational power. The most common applications of bioinformatics in biochemistry are:
- Performing sequence comparisons to determine macromolecule function by comparing the sequence of a new macromolecule to that of macromolecules with known function.
- Generating sequence alignments to identify regions of a macromolecule that are conserved through evolution. These regions are likely to be important for function.
- Predicting secondary or tertiary structure of a macromolecule by comparing its sequence to macromolecules with known structure.
- Predicting and visualizing macromolecule-ligand interaction to determine how the macromolecule recognizes its specific target.
During this lab course we will familiarize ourselves with most of these applications.
Genome sequences are stored in the gene databank (GenBank) of the National Institute of Health (NIH). GenBank is the annotated collection of publicly available gene sequences. By the end of 2013 there was 169,331,407 sequences stored in the GenBank, which represents a 17-fold increase in data since the sequencing of the human genome in 2000 and an about 280,000-fold increase in data since the creation of GenBank in 1982. This database is available online free of charge to perform sequence comparisons, also called sequence alignments. There are two types of sequence alignments: global alignment and local alignment. Global sequence alignment aims to align as many characters as possible between the sequence of interest, the query, and sequences in the GenBank, the subject. The goal is to find a hit with high overall similarity. This method is slow, but is useful for example to uncover 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 scor