Promiscuity and the Conformational Rearrangement of Drug-Like Molecules: Insight from the Protein Data Bank

Influence of noncovalent intramolecular and host–guest interactions on imatinib binding to MoS2 sheets: a PXRD/DFT study

The revealed pattern of imatinib drug binding to MoS2 sheets is promising for the combined exploitation of these species for therapeutic purposes.

Unravelling conformational and crystal packing preferences of cyclohexane-5-spirohydantoin derivatives incorporating a halogenated benzoyl group

The conformational and supramolecular diversity of spirohydantoins have been investigated.

Ligity: A Non-Superpositional, Knowledge-Based Approach to Virtual Screening

We present Ligity, a hybrid ligand-structure-based, non-superpositional method for virtual screening of large databases of small molecules. Ligity uses the relative spatial distribution of pharmacophoric interaction points (PIPs) derived from the conformations of small molecules. These are compared with the PIPs derived from key interaction features found in protein-ligand complexes and are used to prioritize likely binders. We investigated the effect of generating PIPs using the single lowest energy conformer versus an ensemble of conformers for each screened ligand, using different bin sizes for the distance between two features, utilizing triangular sets of pharmacophoric features (3-PIPs) versus chiral tetrahedral sets (4-PIPs), fusing data for targets with multiple protein-ligand complex structures, and applying different similarity measures. Ligity was benchmarked using the Directory of Useful Decoys-Enhanced (DUD-E). Optimal results were obtained using the tetrahedral PIPs derived from an ensemble of bound ligand conformers and a bin size of 1.5 Å, which are used as the default settings for Ligity. The high-throughput screening mode of Ligity, using only the lowest-energy conformer of each ligand, was used for benchmarking against the whole of the DUD-E, and a more resource-intensive, "information-rich" mode of Ligity, using a conformational ensemble of each ligand, were used for a representative subset of 10 targets. Against the full DUD-E database, mean area under the receiver operating characteristic curve (AUC) values ranged from 0.44 to 0.99, while for the representative subset they ranged from 0.61 to 0.86. Data fusion further improved Ligity's performance, with mean AUC values ranging from 0.64 to 0.95. Ligity is very efficient compared to a protein-ligand docking method such as AutoDock Vina: if the time taken for the precalculation of Ligity descriptors is included in the comparason, then Ligity is about 20 times faster than docking. A direct comparison of the virtual screening steps shows Ligity to be over 5000 times faster. Ligity highly ranks the lowest-energy conformers of DUD-E actives, in a statistically significant manner, behavior that is not observed for DUD-E decoys. Thus, our results suggest that active compounds tend to bind in relatively low-energy conformations compared to decoys. This may be because actives-and thus their lowest-energy conformations-have been optimized for conformational complementarity with their cognate binding sites.

How Diverse Are the Protein-Bound Conformations of Small-Molecule Drugs and Cofactors?

Knowledge of the bioactive conformations of small molecules or the ability to predict them with theoretical methods is of key importance to the design of bioactive compounds such as drugs, agrochemicals, and cosmetics. Using an elaborate cheminformatics pipeline, which also evaluates the support of individual atom coordinates by the measured electron density, we compiled a complete set ("Sperrylite Dataset") of high-quality structures of protein-bound ligand conformations from the PDB. The Sperrylite Dataset consists of a total of 10,936 high-quality structures of 4,548 unique ligands. Based on this dataset, we assessed the variability of the bioactive conformations of 91 small molecules-each represented by a minimum of ten structures-and found it to be largely independent of the number of rotatable bonds. Sixty-nine molecules had at least two distinct conformations (defined by an RMSD greater than 1 Å). For a representative subset of 17 approved drugs and cofactors we observed a clear trend for the formation of few clusters of highly similar conformers. Even for proteins that share a very low sequence identity, ligands were regularly found to adopt similar conformations. For cofactors, a clear trend for extended conformations was measured, although in few cases also coiled conformers were observed. The Sperrylite Dataset is available for download from http://www.zbh.uni-hamburg.de/sperrylite_dataset.

Flexibility is important for inhibition of the MDM2/p53 protein-protein interaction by cyclic β-hairpins

Protein-protein interactions that have large, flat and featureless binding sites are difficult drug targets. In the development of their modulators conventional drug discovery strategies are often unsuccessful. Gaining a detailed understanding of the binding mode of protein-protein interaction inhibitors is therefore of vast importance for their future pharmaceutical use. The MDM2/p53 protein pair is a highly promising target for cancer treatment. Disruption of the protein complex using p53 α-helix mimetics has been shown to be a successful strategy to control p53 activity. To gain further insight into the binding of inhibitors to MDM2, the flexibility of four cyclic β-hairpins that act as α-helical mimetics and potential MDM2/p53 interaction inhibitors was investigated in relation to their inhibitory activity. MDM2-binding of the mimetics was determined using fluorescence polarization and surface plasmon resonance assays, whereas their conformation and dynamics in solution was described by the combined experimental and computational NAMFIS analysis. Molecular flexibility was shown to be important for the activity of the cyclic β-hairpin based MDM2 inhibitors.