LifeSoaks: a tool for analyzing solvent channels in protein crystals and obstacles for soaking experiments.

Due to the structural complexity of proteins, their corresponding crystal arrangements generally contain a significant amount of solvent-occupied space. These areas allow a certain degree of intracrystalline protein flexibility and mobility of solutes. Therefore, knowledge of the geometry of solvent-filled channels and cavities is essential whenever the dynamics inside a crystal are of interest. Especially in soaking experiments for structure-based drug design, ligands must be able to traverse the crystal solvent channels and reach the corresponding binding pockets. Unsuccessful screenings are sometimes attributed to the geometry of the crystal packing, but the underlying causes are often difficult to understand. This work presents LifeSoaks, a novel tool for analyzing and visualizing solvent channels in protein crystals. LifeSoaks uses a Voronoi diagram-based periodic channel representation which can be efficiently computed. The size and location of channel bottlenecks, which might hinder molecular diffusion, can be directly derived from this representation. This work presents the calculated bottleneck radii for all crystal structures in the PDB and the analysis of a new, hand-curated data set of structures obtained by soaking experiments. The results indicate that the consideration of bottleneck radii and the visual inspection of channels are beneficial for planning soaking experiments.

Analyzing the topology of active sites: on the prediction of pockets and subpockets.

Automated prediction of protein active sites is essential for large-scale protein function prediction, classification, and druggability estimates. In this work, we present DoGSite, a new structure-based method to predict active sites in proteins based on a Difference of Gaussian (DoG) approach which originates from image processing. In contrast to existing methods, DoGSite splits predicted pockets into subpockets, revealing a refined description of the topology of active sites. DoGSite correctly predicts binding pockets for over 92% of the PDBBind and the scPDB data set, being in line with the best-performing methods available. In 63% of the PDBBind data set the detected pockets can be subdivided into smaller subpockets. The cocrystallized ligand is contained in exactly one subpocket in 87% of the predictions. Furthermore, we introduce a more precise prediction performance measure by taking the pairwise ligand and pocket coverage into account. In 90% of the cases DoGSite predicts a pocket that contains at least half of the ligand. In 70% of the cases additionally more than a quarter of the respective pocket itself is covered by the cocrystallized ligand. Consideration of subpockets produces an increase in coverage yielding a success rate of 83% for the latter measure.

Conformational sampling for large-scale virtual screening: accuracy versus ensemble size.

We introduce the TrixX Conformer Generator (TCG), a novel tool for generating conformational ensembles. The tool addresses especially the requirements of large-scale computer-aided drug design applications using conformer databases. For these, the trade-off between accuracy, i.e. rmsd to biologically active conformers, and database size, i.e. the number of conformers in an ensemble, is of central interest. Based on a tree data structure representing the molecule, conformations are generated incrementally in a best-first-search build-up process employing an internal rmsd clustering. This way TCG builds conformational ensembles of low energy conformers utilizing conformational energy as a scoring function. A crucial parameter is the amount of search space to be covered in the build-up process. This parameter is determined according to an exponential function employing a user-specified quality level as base and an exponent which depends on the molecule's flexibility. The quality level allows the user to set the aforementioned trade-off while taking into account the exponentially growing number of combinations of torsion angles. Tested on a set of 778 molecules, we show that on average 20 conformers per ensemble suffice to achieve an average accuracy of 1.13 A. We observed that an improvement in accuracy goes along with an exponential rise of the number of conformations per ensemble (e.g., 100 conformations per ensemble yield an accuracy of 0.99 A). Furthermore, we show that for molecules with less than nine rotatable bonds, ensembles with an average accuracy better than 1 A can be generated with an average ensemble size of 20 conformers. However, this value deteriorates for more flexible molecules. A comparison to CATALYST and OMEGA shows that TCG achieves a comparable performance in terms of accuracy. Furthermore, it performs well with respect to the trade-off between accuracy and ensemble size.