The number of protein and nucleic acid structures stored in the RCSB Protein Data Bank has risen exponentially in recent years. This offers the possibility to study the relationship between the structure of proteins and their function. Information about empty spaces in proteins, i.e. pockets, cavities, pores or tunnels, can provide valuable insight into protein behavior and properties. Finding and characterizing tunnels is fundamental to understanding the mechanism and intensity of many biochemical processes, such as the interaction of these proteins with their substrates or with drug molecules. This knowledge can find immediate applications in rational drug design, protein engineering, enzymology, etc.
Numerous algorithms for empty space detection in proteins have been developed and implemented. These algorithms are generally specialized for different types of volumes, such as shallow clefts called pockets, buried active sites with accessible paths called tunnels, buried volumes called cavities, or simply pores in membrane proteins. The software tools based on these various principles differ dramatically in time complexity, efficiency, and interactivity.
This work is focused on methods for detecting ligand accessible pathways. Particular goals of the work are:

• Review available approaches for finding voids in proteins. Find approaches and software tools, which are specialized in detection of tunnels.
• Find proteins and protein families, which are frequent targets for detection of tunnels. Prepare a test set of proteins, which cover these proteins and protein families.
• Analyze the weak and strong points of tunnel searching tools on proteins from the test set.
• Evaluate capabilities of selected tunnel searching tool on a large set of proteins from different protein families.