Giovanni Bottegoni

A catalytically silent FAAH-1 variant drives anandamide transport in neurons.

The endocannabinoid anandamide is removed from the synaptic space by a selective transport system, expressed in neurons and astrocytes, that remains molecularly uncharacterized. Here we describe a partly cytosolic variant of the intracellular anandamide-degrading enzyme fatty acid amide hydrolase-1 (FAAH-1), termed FAAH-like anandamide transporter (FLAT), that lacked amidase activity but bound anandamide with low micromolar affinity and facilitated its translocation into cells. Known anandamide transport inhibitors, such as AM404 and OMDM-1, blocked these effects. We also identified a competitive antagonist of the interaction of anandamide with FLAT, the phthalazine derivative ARN272, that prevented anandamide internalization in vitro, interrupted anandamide deactivation in vivo and exerted profound analgesic effects in rodent models of nociceptive and inflammatory pain, which were mediated by CB1 cannabinoid receptors. The results identify FLAT as a critical molecular component of anandamide transport in neural cells and a potential target for therapeutic drugs.

Four-dimensional docking: a fast and accurate account of discrete receptor flexibility in ligand docking.

Many available methods aimed at incorporating the receptor flexibility in ligand docking are computationally expensive, require a high level of user intervention, and were tested only on benchmarks of limited size and diversity. Here we describe the four-dimensional (4D) docking approach that allows seamless incorporation of receptor conformational ensembles in a single docking simulation and reduces the sampling time while preserving the accuracy of traditional ensemble docking. The approach was tested on a benchmark of 99 therapeutically relevant proteins and 300 diverse ligands (half of them experimental or marketed drugs). The conformational variability of the binding pockets was represented by the available crystallographic data, with the total of 1113 receptor structures. The 4D docking method reproduced the correct ligand binding geometry in 77.3% of the benchmark cases, matching the success rate of the traditional approach but employed on average only one-fourth of the time during the ligand sampling phase.

Recipes for the Selection of Experimental Protein Conformations for Virtual Screening

The use of multiple X-ray protein structures has been reported to be an efficient alternative for the representation of the binding pocket flexibility needed for accurate small molecules docking. However, the docking performance of the individual single conformations varies widely, and adding certain conformations to an ensemble is even counterproductive. Here we used a very large and diverse benchmark of 1068 X-ray protein conformations of 99 therapeutically relevant proteins, first, to compare the performance of the ensemble and single-conformation docking and, second, to find the properties of the best-performing conformers that can be used to select a smaller set of conformers for ensemble docking. The conformer selection has been validated through retrospective virtual screening experiments aimed at separating known ligand binders from decoys. We found that the conformers cocrystallized with the largest ligands displayed high selectivity for binders, and when combined in ensembles they consistently provided better results than randomly chosen protein conformations. The use of ensembles encompassing between 3 and 5 experimental conformations consistently improved the docking accuracy and binders vs decoys separation.

Consistent Improvement of Cross-Docking Results Using Binding Site Ensembles Generated with Elastic Network Normal Modes

The representation of protein flexibility is still a challenge for the state-of-the-art flexible ligand docking protocols. In this article, we use a large and diverse benchmark to prove that is possible to improve consistently the cross-docking performance against a single receptor conformation, using an equilibrium ensemble of binding site conformers. The benchmark contained 28 proteins, and our method predicted the top-ranked near native ligand poses 20% more efficiently than using a single receptor. The multiple conformations were derived from the collective variable space defined by all heavy-atom elastic network normal modes, including backbone and side chains. We have found that the binding site displacements for best positioning of the ligand seem rather independent from the global collective motions of the protein. We also found that the number of binding site conformations needed to represent nonredundant flexibility was < 100. The ensemble of receptor conformations can be generated at our Web site at http://abagyan.scripps.edu/MRC.

The role of fragment-based and computational methods in polypharmacology.

Polypharmacology-based strategies are gaining increased attention as a novel approach to obtaining potentially innovative medicines for multifactorial diseases. However, some within the pharmaceutical community have resisted these strategies because they can be resource-hungry in the early stages of the drug discovery process. Here, we report on fragment-based and computational methods that might accelerate and optimize the discovery of multitarget drugs. In particular, we illustrate that fragment-based approaches can be particularly suited for polypharmacology, owing to the inherent promiscuous nature of fragments. In parallel, we explain how computer-assisted protocols can provide invaluable insights into how to unveil compounds theoretically able to bind to more than one protein. Furthermore, several pragmatic aspects related to the use of these approaches are covered, thus offering the reader practical insights on multitarget-oriented drug discovery projects.

Systematic Exploitation of Multiple Receptor Conformations for Virtual Ligand Screening

The role of virtual ligand screening in modern drug discovery is to mine large chemical collections and to prioritize for experimental testing a comparatively small and diverse set of compounds with expected activity against a target. Several studies have pointed out that the performance of virtual ligand screening can be improved by taking into account receptor flexibility. Here, we systematically assess how multiple crystallographic receptor conformations, a powerful way of discretely representing protein plasticity, can be exploited in screening protocols to separate binders from non-binders. Our analyses encompass 36 targets of pharmaceutical relevance and are based on actual molecules with reported activity against those targets. The results suggest that an ensemble receptor-based protocol displays a stronger discriminating power between active and inactive molecules as compared to its standard single rigid receptor counterpart. Moreover, such a protocol can be engineered not only to enrich a higher number of active compounds, but also to enhance their chemical diversity. Finally, some clear indications can be gathered on how to select a subset of receptor conformations that is most likely to provide the best performance in a real life scenario.

Combining Galantamine and Memantine in Multitargeted, New Chemical Entities Potentially Useful in Alzheimer’s Disease

Herein we report on a novel series of multitargeted compounds obtained by linking together galantamine and memantine. The compounds were designed by taking advantage of the crystal structures of acetylcholinesterase (AChE) in complex with galantamine derivatives. Sixteen novel derivatives were synthesized, using spacers of different lengths and chemical composition. The molecules were then tested as inhibitors of AChE and as binders of the N-methyl-d-aspartate (NMDA) receptor (NMDAR). Some of the new compounds were nanomolar inhibitors of AChE and showed micromolar affinities for NMDAR. All compounds were also tested for selectivity toward NMDAR containing the 2B subunit (NR2B). Some of the new derivatives showed a micromolar affinity for NR2B. Finally, selected compounds were tested using a cell-based assay to measure their neuroprotective activity. Three of them showed a remarkable neuroprotective profile, inhibiting the NMDA-induced neurotoxicity at subnanomolar concentrations (e.g., 5, named memagal, IC(50) = 0.28 nM).

Multitarget Drug Discovery for Alzheimer's Disease: Triazinones as BACE‐1 and GSK‐3β Inhibitors

Cumulative evidence strongly supports that the amyloid and tau hypotheses are not mutually exclusive, but concomitantly contribute to neurodegeneration in Alzheimers disease (AD). Thus, the development of multitarget drugs which are involved in both pathways might represent a promising therapeutic strategy. Accordingly, reported here in is the discovery of 6-amino-4-phenyl-3,4-dihydro-1,3,5-triazin-2(1H)-ones as the first class of molecules able to simultaneously modulate BACE-1 and GSK-3β. Notably, one triazinone showed well-balanced in vitro potencies against the two enzymes (IC50 of (18.03±0.01) μM and (14.67±0.78) μM for BACE-1 and GSK-3β, respectively). In cell-based assays, it displayed effective neuroprotective and neurogenic activities and no neurotoxicity. It also showed good brain permeability in a preliminary pharmacokinetic assessment in mice. Overall, triazinones might represent a promising starting point towards high quality lead compounds with an AD-modifying potential.

A comparative study on the application of hierarchical-agglomerative clustering approaches to organize outputs of reiterated docking runs.

Reiterated runs of standard docking protocols usually provide a collection of possible binding modes rather than pinpoint a single solution. Usually, this ensemble is then ranked by means of an energy-based scoring function. However, since many degrees of approximation have to be introduced in the computation of the binding free energy, scoring functions cannot always rank the experimental pose among the top scorers. Cluster analysis might help to overcome this limit, provided that data clusterability has been earlier assessed. In this paper, first, we present a modified version of a test earlier developed by Hopkins to assess whether or not docking outputs show the natural tendency to be grouped in clusters. Then, we report the results of a comparative study on the application of different hierarchical-agglomerative cluster rules to partition docking outputs. The rule that was able to best manage the observed data was finally applied to the whole ensemble of poses collected from several docking tools. The combination of the average linkage rule with the cutting function developed by Sutcliffe and co-workers turned out to be an approach that meets all of the criteria required for a robust clustering protocol. Furthermore, a consensus clustering allowed us to identify the pose closest to the experimental one within a statistically significant cluster, whose number was always of few units.

The ligand binding mechanism to purine nucleoside phosphorylase elucidated via molecular dynamics and machine learning

The study of biomolecular interactions between a drug and its biological target is of paramount importance for the design of novel bioactive compounds. In this paper, we report on the use of molecular dynamics (MD) simulations and machine learning to study the binding mechanism of a transition state analogue (DADMe–immucillin-H) to the purine nucleoside phosphorylase (PNP) enzyme. Microsecond-long MD simulations allow us to observe several binding events, following different dynamical routes and reaching diverse binding configurations. These simulations are used to estimate kinetic and thermodynamic quantities, such as kon and binding free energy, obtaining a good agreement with available experimental data. In addition, we advance a hypothesis for the slow-onset inhibition mechanism of DADMe–immucillin-H against PNP. Combining extensive MD simulations with machine learning algorithms could therefore be a fruitful approach for capturing key aspects of drug–target recognition and binding.