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Inspired by the current representation of the ligand-receptor binding process, a normal-mode-based methodology is presented to incorporate receptor flexibility in ligand docking and virtual screening. However, the systematic representation of the deformation space grows geometrically with the number of modes, and furthermore, midscale loop rearrangements like those found in protein kinase binding pockets cannot be accounted for with the first lowest-frequency modes. We thus introduced a measure of relevance of normal modes on a given region of interest and showed that only very few modes in the low-frequency range are necessary and sufficient to describe loop flexibility in cAMP-dependent protein kinase. We used this approach to generate an ensemble of representative receptor backbone conformations by perturbing the structure along a combination of relevant modes. Each ensemble conformation is complexed with known non-native binders to optimize the position of the binding-pocket side chains through a full flexible docking procedure. The multiple receptor conformations thus obtained are used in a small-scale virtual screening using receptor ensemble docking. We evaluated this algorithm on holo and apo structures of cAMP-dependent protein kinase that exhibit backbone rearrangements on two independent loop regions close to the binding pocket. Docking accuracy is improved, since the ligands considered in the virtual screening docked within 1.5 A to at least one of the structures. The discrimination between binders and nonbinders is also enhanced, as shown by the improvement of the enrichment factor. This constitutes a new step toward the systematic integration of flexible ligand-flexible receptor docking tools in structure-based drug discovery.

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The glucocorticoid receptor (GR) is phosphorylated at multiple sites within its N terminus (S203, S211, S226), yet the role of phosphorylation in receptor function is not understood. Using a range of agonists and GR phosphorylation site-specific antibodies, we demonstrated that GR transcriptional activation is greatest when the relative phosphorylation of S211 exceeds that of S226. Consistent with this finding, a replacement of S226 with an alanine enhances GR transcriptional response. Using a battery of compounds that perturb different signaling pathways, we found that BAPTA-AM, a chelator of intracellular divalent cations, and curcumin, a natural product with antiinflammatory properties, reduced hormone-dependent phosphorylation at S211. This change in GR phosphorylation was associated with its decreased nuclear retention and transcriptional activation. Molecular modeling suggests that GR S211 phosphorylation promotes a conformational change, which exposes a novel surface potentially facilitating cofactor interaction. Indeed, S211 phosphorylation enhances GR interaction with MED14 (vitamin D receptor interacting protein 150). Interestingly, in U2OS cells expressing a nonphosphorylated GR mutant S211A, the expression of IGF-binding protein 1 and interferon regulatory factor 8, both MED14-dependent GR target genes, was reduced relative to cells expressing wild-type receptor across a broad range of hormone concentrations. In contrast, the induction of glucocorticoid-induced leucine zipper, a MED14-independent GR target, was similar in S211A- and wild-type GR-expressing cells at high hormone levels, but was reduced in S211A cells at low hormone concentrations, suggesting a link between GR phosphorylation, MED14 involvement, and receptor occupancy. Phosphorylation also affected the magnitude of repression by GR in a gene-selective manner. Thus, GR phosphorylation at S211 and S226 determines GR transcriptional response by modifying cofactor interaction. Furthermore, the effect of GR S211 phosphorylation is gene specific and, in some cases, dependent upon the amount of activated receptor.

Melanin-concentrating hormone receptor 1 (MCH-R1) is a G-protein-coupled receptor (GPCR) and a target for the development of therapeutics for obesity. The structure-based development of MCH-R1 and other GPCR antagonists is hampered by the lack of an available experimentally determined atomic structure. A ligand-steered homology modeling approach has been developed (where information about existing ligands is used explicitly to shape and optimize the binding site) followed by docking-based virtual screening. Top scoring compounds identified virtually were tested experimentally in an MCH-R1 competitive binding assay, and six novel chemotypes as low micromolar affinity antagonist "hits" were identified. This success rate is more than a 10-fold improvement over random high-throughput screening, which supports our ligand-steered method. Clearly, the ligand-steered homology modeling method reduces the uncertainty of structure modeling for difficult targets like GPCRs.

We compiled a G protein-coupled receptor (GPCR) ligand library (GLL) for 147 targets, selecting for each ligand 39 decoy molecules, collected in the GPCR Decoy Database (GDD). Decoys were chosen ensuring a ligand-decoy similarity of six physical properties, while enforcing ligand-decoy chemical dissimilarity. The performance in docking of the GDD was evaluated on 19 GPCRs, showing a marked decrease in enrichment compared to bias-uncorrected decoy sets. Both the GLL and GDD are freely available for the scientific community.

Consensus-scoring methods are commonly used with molecular docking in virtual screening campaigns to filter potential ligands for a protein target. Traditional consensus methods combine results from different docking programs by averaging the score or rank of each molecule obtained from individual programs. Unfortunately, these methods fail if one of the docking programs has poor performance, which is likely to occur due to training-set dependencies and scoring-function parameterization. In this work, we introduce a novel consensus method that overcomes these limitations. We combine the results from individual docking programs using a sum of exponential distributions as a function of the molecule rank for each program. We test the method over several benchmark systems using individual and ensembles of target structures from diverse protein families with challenging decoy/ligand datasets. The results demonstrate that the novel method outperforms the best traditional consensus strategies over a wide range of systems. Moreover, because the novel method is based on the rank rather than the score, it is independent of the score units, scales and offsets, which can hinder the combination of results from different structures or programs. Our method is simple and robust, providing a theoretical basis not only for molecular docking but also for any consensus strategy in general.

Inhibition of distinct ubiquitin E3 ligases might represent a powerful therapeutic tool. ITCH is a HECT domain-containing E3 ligase that promotes the ubiquitylation and degradation of several proteins, including p73, p63, c-Jun, JunB, Notch and c-FLIP, thus affecting cell fate. Accordingly, ITCH depletion potentiates the effect of chemotherapeutic drugs, revealing ITCH as a potential pharmacological target in cancer therapy. Using high throughput screening of ITCH auto-ubiquitylation, we identified several putative ITCH inhibitors, one of which is clomipramine—a clinically useful antidepressant drug. Previously, we have shown that clomipramine inhibits autophagy by blocking autophagolysosomal fluxes and thus could potentiate chemotherapy in vitro. Here, we found that clomipramine specifically blocks ITCH auto-ubiquitylation, as well as p73 ubiquitylation. By screening structural homologs of clomipramine, we identified several ITCH inhibitors and putative molecular moieties that are essential for ITCH inhibition. Treating a panel of breast, prostate and bladder cancer cell lines with clomipramine, or its homologs, we found that they reduce cancer cell growth, and synergize with gemcitabine or mitomycin in killing cancer cells by blocking autophagy. We also discuss a potential mechanism of inhibition. Together, our study (i) demonstrates the feasibility of using high throughput screening to identify E3 ligase inhibitors and (ii) provides insight into how clomipramine and its structural homologs might interfere with ITCH and other HECT E3 ligase catalytic activity in (iii) potentiating chemotherapy by regulating autophagic fluxes. These results may have direct clinical applications.