Structural bioinformatics is a branch of science which concerns itself with the analysis and prediction of 3D structure of proteins in particular. Structural bioinformaticians use sequence data, sequence alignment data, NMR data and x-ray crystallographic data along with various visualization, modeling and prediction tools to analyse and predict the structure, function and behavior of their molecules of interest.
Importance of protein Structure
Proteins are the functional units of life. They are involved in everything from gene expression regulation to defense of an organism. Following is a list of most important functions:
|Catalysis||Enzymes such as kinases enable reactions by lowering the activation energy (?G) of a reaction.|
|Movement||Actin and mysin in muscles|
Protein structure leads to its function
Proteins evolved under selective evolutionary pressure to carry out specific tasks. All these functions are defined largely due to interactions with other molecules. The way a protein interacts with molecules in its environment depends on its three dimensional fold. This fold refers to the overall shape, the surface, active sites, and positioning of key amino acids.
The structure of a protein defines what a protein can or cannot do. The distinctive amino acid sequence of proteins allow for the placement of particular chemical groups in specific places in specific places in 3D space. Even a minor modification such as changing one amino acid could change the structure of a protein significantly, thus modifying its function. For example, the sickle cell anemia disease results due to a hemoglobin where the sixth amino acid is changed from glutamic acid to valine. Protein structures are highly diverse and this diversity the functional diversity of these structures is expanded through interactions with smaller molecules.
Some common uses of protein structures are:
- location of mutants and conserved residues
- ligand and functional sites
- useful in drug design
- discovering evolutionary relationships
- for understanding various biological mechanisms
Why use structure data
Both sequences and structures are suitable candidates for predicting protein functions. However, sequence data is much more readily available than structure data. Structure data has an advantage over protein sequence data; structure data is far better conserved than sequence data over evolutionary time. Often coupling sequence data with structure data results in better predictions.
Apparently unrelated sequences can have similar structures. This indicates that the total number of protein folds should be much smaller than the total number of sequences. In fact, nature seems to be quite conservative in its choice of structures, preferring to conserver or slightly modify existing structures rather than invent new ones.
Protein structure greatly simplifies the task of identifying protein function. However, there is no 1-1 relationship between protein structure and function. There are many proteins with similar structure but very different functions.
Quite different sequences can adopt the same structure. This fact can useful in identifying evolutionary relationships. It can, however, identify false relationships as well. Analogous proteins are proteins that have the same function but do not share ancestry. Homologous proteins share ancestry.