or tetracycline complex. If the KD value of the mutant sensor is within an order of magnitude of that of the wild-type sensor tetracycline complex you will conclude that the nucleotides altered were not essential for recognizing tetracycline.
Figure 2.1: Evaluation of ykkCD sensor RNA mutants using binding affinity assays: If >10 (higher KD value) the nucleotide(s) mutated were important for tetracycline recognition. If <10 (low KD value) the nucleotides mutated were not important for tetracycline recognition.
2.4 Review of Nucleic Acid Properties
Before we identify conserved sequence elements and design mutants let us review a few things about nucleic acids. Nucleic acids are macromolecules that contain a chain of nucleotides connected by covalent bonds (phosphodiester bonds). Most nucleic acids contain all four nucleotides: adenine (A), thymine (T) or uracil (U), cytosine (C) and guanine (G). Our toxin sensor is an RNA molecule therefore it contains uracil instead of thymine, but since we perform our mutagenesis in the bacterial DNA we will see T instead of U in the sequence. The sequence of the nucleic acid is determined by the order in which the nucleobases follow each other in the macromolecule, and it determines the properties of the nucleic acid. For example even though the following two nucleic acids, ATCG and GTCA, contain the same bases, since the bases follow each other in different order, the properties of these two nucleic acids will be very different. Secondary structure, the interaction pattern between nucleobases, of a nucleic acid is easily predictable since an A always pairs with a T or a U using two hydrogen bonds and a C always pairs with a G using three hydrogen bonds. These pairs are referred to as Watson-Crick pairs or base pairs. Nucleic acid sequence comparisons and structure predictions are important applications of bioinformatics.
2.5 What is Bioinformatics?
During the last 15 years one of the greatest breakthroughs in biochemistry was the emergence of bioinformatics. Bioinformatics uses 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 