Structural bioinformatics deals with the analysis, classification and prediction of three-dimensional structures of biomacromolecules. It is becoming increasingly important as the number of structures is growing rapidly. This thesis describes three studies concerned with protein-function prediction and two studies about protein structure validation.
New protein structures are often compared to known structures to find out if they have a known fold, which may provide hints about their function. The functionality and performance of eleven fold-comparison servers were evaluated. None of the tested servers achieved perfect recall, so in practise a combination of servers should be used.
If fold comparison does not provide any hints about the function of a protein, structural motif searches can be employed. A survey of left-handed helices in known protein structures was carried out. The results show that left-handed helices are rare motifs, but most of them occur in active or ligand-binding sites. Their identification can therefore help to pinpoint potentially important residues.
Sometimes all available methods fail to provide hints about the function of a protein. Therefore, the potential of using docking techniques to predict which ligands are likely to bind to a particular protein has been investigated. Initial results show that it will be difficult to build a reliable automated docking protocol that will suit all proteins.
The effect of various phenomena on the precision of accessible surface area calculations was also investigated. The results suggest that it is prudent to report such values with a precision of 50 to 100 Å2.
Finally, a survey of register shifts in known protein structures was carried out. The identified potential register shifts were analysed and classified. A machine-learning approach ("rough sets") was used in an attempt to diagnose register errors in structures.