dxTuber: detecting protein cavities, tunnels and clefts based on protein and solvent dynamics

Empty space in a protein structure can provide valuable insight into protein properties such as internal hydration, structure stabilization, substrate translocation, storage compartments or binding sites. This information can be visualized by means of cavity analysis. Numerous tools are available depicting cavities directly or identifying lining residues. So far, all available techniques base on a single conformation neglecting any form of protein and cavity dynamics. Here we report a novel, grid-based cavity detection method that uses protein and solvent residence probabilities derived from molecular dynamics simulations to identify (I) internal cavities, (II) tunnels or (III) clefts on the protein surface. Driven by a graphical user interface, output can be exported in PDB format where cavities are described as individually selectable groups of adjacent voxels representing regions of high solvent residence probability. Cavities can be analyzed in terms of solvent density, cavity volume and cross-sectional area along a principal axis. To assess dxTuber performance we performed test runs on a set of six example proteins representing the three main classes of protein cavities and compared our findings to results obtained with SURFNET, CAVER and PyMol.