Structure-based predictions of activity cliffs.

In drug discovery, it is generally accepted that neighboring molecules in a given descriptor's space display similar activities. However, even in regions that provide strong predictability, structurally similar molecules can occasionally display large differences in potency. In QSAR jargon, these discontinuities in the activity landscape are known as "activity cliffs". In this study, we assessed the reliability of ligand docking and virtual ligand screening schemes in predicting activity cliffs. We performed our calculations on a diverse, independently collected database of cliff-forming cocrystals. Starting from ideal situations, which allowed us to establish our baseline, we progressively moved toward simulating more realistic scenarios. Ensemble- and template-docking achieved a significant level of accuracy, suggesting that, despite the well-known limitations of empirical scoring schemes, activity cliffs can be accurately predicted by advanced structure-based methods.

Molecular mechanism of serotonin transporter inhibition elucidated by a new flexible docking protocol.

The two main groups of antidepressant drugs, the tricyclic antidepressants (TCAs) and the selective serotonin reuptake inhibitors (SSRIs), as well as several other compounds, act by inhibiting the serotonin transporter (SERT). However, the binding mode and molecular mechanism of inhibition in SERT are not fully understood. In this study, five classes of SERT inhibitors were docked into an outward-facing SERT homology model using a new 4D ensemble docking protocol. Unlike other docking protocols, where protein flexibility is not considered or is highly dependent on the ligand structure, flexibility was here obtained by side chain sampling of the amino acids of the binding pocket using biased probability Monte Carlo (BPMC) prior to docking. This resulted in the generation of multiple binding pocket conformations that the ligands were docked into. The docking results showed that the inhibitors were stacked between the aromatic amino acids of the extracellular gate (Y176, F335) presumably preventing its closure. The inhibitors interacted with amino acids in both the putative substrate binding site and more extracellular regions of the protein. A general structure-docking-based pharmacophore model was generated to explain binding of all studied classes of SERT inhibitors. Docking of a test set of actives and decoys furthermore showed that the outward-facing ensemble SERT homology model consistently and selectively scored the majority of active compounds above decoys, which indicates its usefulness in virtual screening.

Improved docking, screening and selectivity prediction for small molecule nuclear receptor modulators using conformational ensembles.

Nuclear receptors (NRs) are ligand dependent transcriptional factors and play a key role in reproduction, development, and homeostasis of organism. NRs are potential targets for treatment of cancer and other diseases such as inflammatory diseases, and diabetes. In this study, we present a comprehensive library of pocket conformational ensembles of thirteen human nuclear receptors (NRs), and test the ability of these ensembles to recognize their ligands in virtual screening, as well as predict their binding geometry, functional type, and relative binding affinity. 157 known NR modulators and 66 structures were used as a benchmark. Our pocket ensemble library correctly predicted the ligand binding poses in 94% of the cases. The models were also highly selective for the active ligands in virtual screening, with the areas under the ROC curves ranging from 82 to a remarkable 99%. Using the computationally determined receptor-specific binding energy offsets, we showed that the ensembles can be used for predicting selectivity profiles of NR ligands. Our results evaluate and demonstrate the advantages of using receptor ensembles for compound docking, screening, and profiling.

The flexible pocketome engine for structural chemogenomics.

Biological metabolites, substrates, cofactors, chemical probes, and drugs bind to flexible pockets in multiple biological macromolecules to exert their biological effect. The rapid growth of the structural databases and sequence data, including SNPs and disease-related genome modifications, complemented by the new cutting-edge 3D docking, scoring, and profiling methods has created a unique opportunity to develop a comprehensive structural map of interactions between any small molecule and biopolymers. Here we demonstrate that a comprehensive structural genomics engine can be built using multiple pocket conformations, experimentally determined or generated with a variety of modeling methods, and new efficient ensemble docking algorithms. In contrast to traditional ligand-activity-based engines trained on known chemical structures and their activities, the structural pocketome and docking engine will allow prediction of poses and activities for new, previously unknown, protein binding sites, and new, previously uncharacterized, chemical scaffolds. This de novo structure-based activity prediction engine may dramatically accelerate the discovery of potent and specific therapeutics with reduced side effects.

Type-II kinase inhibitor docking, screening, and profiling using modified structures of active kinase states.

Type-II kinase inhibitors represent a class of chemicals that trap their target kinases in an inactive, so-called DFG-out state, occupying a hydrophobic pocket adjacent to the ATP binding site. These compounds are often more specific than those that target active DFG-in kinase conformations. Unfortunately, the discovery of novel type-II scaffolds presents a considerable challenge, partially because the lack of compatible kinase structures makes structure-based methods inapplicable. We present a computational protocol for converting multiple available DFG-in structures of various kinases (approximately 70% of mammalian structural kinome) into accurate and specific models of their type-II bound state. The models, described as deletion-of-loop Asp-Phe-Gly-in (DOLPHIN) kinase models, demonstrate exceptional performance in various inhibitor discovery applications, including compound pose prediction, screening, and in silico activity profiling. Given the abundance of the DFG-in structures, the presented approach opens possibilities for kinome-wide discovery of specific molecules targeting inactive kinase states.

Optimization of high throughput virtual screening by combining shape-matching and docking methods.

Receptor flexibility is a critical issue in structure-based virtual screening methods. Although a multiple-receptor conformation docking is an efficient way to account for receptor flexibility, it is still too slow for large molecular libraries. It was reported that a fast ligand-centric, shape-based virtual screening was more consistent for hit enrichment than a typical single-receptor conformation docking. Thus, we designed a "distributed docking" method that improves virtual high throughput screening by combining a shape-matching method with a multiple-receptor conformation docking. Database compounds are classified in advance based on shape similarities to one of the crystal ligands complexed with the target protein. This classification enables us to pick the appropriate receptor conformation for a single-receptor conformation docking of a given compound, thereby avoiding time-consuming multiple docking. In particular, this approach utilizes cross-docking scores of known ligands to all available receptor structures in order to optimize the algorithm. The present virtual screening method was tested for reidentification of known PPARgamma and p38 MAP kinase active compounds. We demonstrate that this method improves the enrichment while maintaining the computation speed of a typical single-receptor conformation docking.