Computational Methods for Detecting Ligand Accessible Pathways – Approaches and Benchmarking Study

L. Pravda, R. Svobodová Vařeková, D. Jaiswal, D. Sehnal,

C. M. Ionescu, and J. Koča

National Centre for Biomolecular Research and CEITEC - Central European Institute of Technology, Masaryk University, Kamenice 5, 625 00 Brno-Bohunice, Czech Republic, luky.pravda@gmail.com

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 behaviour 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. With respect to the applied approach for detecting different structural features, algorithms can be divided among a few classes [1]: grid-based methods, probe sphere filling methods, methods which utilize Voronoi diagrams and slice and optimization methods.

The software tools developed based on these various principles differ dramatically in time complexity, efficiency, and interactivity. Here we present an overview of the main approaches for finding tunnels, as well as a benchmarking study of the related software tools (i.e., Mole 2.0, Mole 1.4 [2], MolAxis [3], Caver [4]). The proteins used for benchmarking have been chosen as representatives of the classes of proteins which are generally accepted as interesting for the research of tunnels. The presented software tools are compared with emphasis on the speed, accuracy and user experience.

1. Lee PH, Helms V: Identifying continuous pores in protein structures with PROPORES by computational repositioning of gating residues. Proteins: Structure, Function and Bioinformatics 2012, 80:421-432.

2. Petřek M, Kosinová P, Koča J, Otyepka M: MOLE: a Voronoi diagram-based explorer of molecular channels, pores, and tunnels, Structure 2007, 15:1357-1363.

3. Yaffe E, Fishelovitch D, Wolfson HJ, Halperin D, Nussinov R: MolAxis: efficient and accurate identification of channels in macromolecules, Proteins 2008, 73:72-86.

4. Petřek M, Otyepka M, Banáš P, Kosinová P, Koča J, Damborský J: CAVER: a new tool to explore routes from protein clefts, pockets and cavities, BMC Bioinformatics 2006, 7:316.