Pharmacological inactivation of Skp2 SCF ubiquitin ligase restricts cancer stem cell traits and cancer progression.

Skp2 E3 ligase is overexpressed in numerous human cancers and plays a critical role in cell-cycle progression, senescence, metabolism, cancer progression, and metastasis. In the present study, we identified a specific Skp2 inhibitor using high-throughput in silico screening of large and diverse chemical libraries. This Skp2 inhibitor selectively suppresses Skp2 E3 ligase activity, but not activity of other SCF complexes. It also phenocopies the effects observed upon genetic Skp2 deficiency, such as suppressing survival and Akt-mediated glycolysis and triggering p53-independent cellular senescence. Strikingly, we discovered a critical function of Skp2 in positively regulating cancer stem cell populations and self-renewal ability through genetic and pharmacological approaches. Notably, Skp2 inhibitor exhibits potent antitumor activities in multiple animal models and cooperates with chemotherapeutic agents to reduce cancer cell survival. Our study thus provides pharmacological evidence that Skp2 is a promising target for restricting cancer stem cell and cancer progression.

SULT1A3-mediated regiospecific 7-O-sulfation of flavonoids in Caco-2 cells can be explained by the relevant molecular docking studies.

Flavonoids are polyphenolic compounds with various claimed health benefits, but the extensive metabolism by uridine-5'-diphospho-glucuronosyltransferases (UGTs) and sulfotransferases (SULTs) in liver and intestine led to poor oral bioavailabilities. The effects of structural changes on the sulfonation of flavonoids have not been systemically determined, although relevant effects of structural changes on the glucuronidation of flavonoids had. We performed the regiospecific sulfonation of sixteen flavonoids from five different subclasses of flavonoids, which are represented by apigenin (flavone), genistein (isoflavone), naringenin (flavanone), kaempherol (flavonol), and phloretin (chalcone). Additional studies were performed using 4 monohydroxyl flavonoids with a -OH group at the 3, 4', 5 or 7 position, followed by 5 dihydroxyl flavonoids, and 2 trihydroxyl flavonoids by using expressed human SULT1A3 and Caco-2 cell lysates. We found that these compounds were exclusively sulfated at the 7-OH position by SULT1A3 and primarily sulfated at the 7-OH position in Caco-2 cell lysates with minor amounts of 4'-O-sulfates formed as well. Sulfonation rates measured using SULT1A3 and Caco-2 cell lysates were highly correlated at substrate concentrations of 2.5 and 10 µM. Molecular docking studies provided structural explanations as to why sulfonation only occurred at the 7-OH position of flavones, flavonols and flavanones. In conclusion, molecular docking studies explain why SULT1A3 exclusively mediates sulfonation at the 7-OH position of flavones/flavonols, and correlation studies indicate that SULT1A3 is the main isoform responsible for flavonoid sulfonation in the Caco-2 cells.

Three-dimensional quantitative structure-activity relationship studies on UGT1A9-mediated 3-O-glucuronidation of natural flavonols using a pharmacophore-based comparative molecular field analysis model.

Glucuronidation is often recognized as one of the rate-determining factors that limit the bioavailability of flavonols. Hence, design and synthesis of more bioavailable flavonols would benefit from the establishment of predictive models of glucuronidation using kinetic parameters [e.g., K(m), V(max), intrinsic clearance (CL(int)) = V(max)/K(m)] derived for flavonols. This article aims to construct position (3-OH)-specific comparative molecular field analysis (CoMFA) models to describe UDP-glucuronosyltransferase (UGT) 1A9-mediated glucuronidation of flavonols, which can be used to design poor UGT1A9 substrates. The kinetics of recombinant UGT1A9-mediated 3-O-glucuronidation of 30 flavonols was characterized, and kinetic parameters (K(m), V(max), CL(int)) were obtained. The observed K(m), V(max), and CL(int) values of 3-O-glucuronidation ranged from 0.04 to 0.68 µM, 0.04 to 12.95 nmol/mg/min, and 0.06 to 109.60 ml/mg/min, respectively. To model UGT1A9-mediated glucuronidation, 30 flavonols were split into the training (23 compounds) and test (7 compounds) sets. These flavonols were then aligned by mapping the flavonols to specific common feature pharmacophores, which were used to construct CoMFA models of V(max) and CL(int), respectively. The derived CoMFA models possessed good internal and external consistency and showed statistical significance and substantive predictive abilities (V(max) model: q(2) = 0.738, r(2) = 0.976, r(pred)(2) = 0.735; CL(int) model: q(2) = 0.561, r(2) = 0.938, r(pred)(2) = 0.630). The contour maps derived from CoMFA modeling clearly indicate structural characteristics associated with rapid or slow 3-O-glucuronidation. In conclusion, the approach of coupling CoMFA analysis with a pharmacophore-based structural alignment is viable for constructing a predictive model for regiospecific glucuronidation rates of flavonols by UGT1A9.

Molecular networks in drug discovery.

Despite the dramatic increase of global spending on drug discovery and development, the approval rate for new drugs is declining, due chiefly to toxicity and undesirable side effects. Simultaneously, the growth of available biomedical data in the postgenomic era has provided fresh insight into the nature of redundant and compensatory drug-target pathways. This stagnation in drug approval can be overcome by the novel concept of polypharmacology, which is built on the fundamental concept that drugs modulate multiple targets. Polypharmacology can be studied with molecular networks that integrate multidisciplinary concepts including cheminformatics, bioinformatics, and systems biology. In silico techniques such as structure- and ligand-based approaches can be employed to study molecular networks and reduce costs by predicting adverse drug reactions and toxicity in the early stage of drug development. By amalgamating strides in this informatics-driven era, designing polypharmacological drugs with molecular network technology exemplifies the next generation of therapeutics with less of-target properties and toxicity. In this review, we will first describe the challenges in drug discovery, and showcase successes using multitarget drugs toward diseases such as cancer and mood disorders. We will then focus on recent development of in silico polypharmacology predictions. Finally, our technologies in molecular network analysis will be presented.

Fragment-based Differential Targeting of PPI Stabilizer Interfaces.

Stabilization of protein-protein interactions (PPIs) holds great potential for therapeutic agents, as illustrated by the successful drugs rapamycin and lenalidomide. However, how such interface-binding molecules can be created in a rational, bottom-up manner is a largely unanswered question. We report here how a fragment-based approach can be used to identify chemical starting points for the development of small-molecule stabilizers that differentiate between two different PPI interfaces of the adapter protein 14-3-3. The fragments discriminately bind to the interface of 14-3-3 with the recognition motif of either the tumor suppressor protein p53 or the oncogenic transcription factor TAZ. This X-ray crystallography driven study shows that the rim of the interface of individual 14-3-3 complexes can be targeted in a differential manner with fragments that represent promising starting points for the development of specific 14-3-3 PPI stabilizers.

Targeting ubiquitination for cancer therapies.

Ubiquitination, the structured degradation and turnover of cellular proteins, is regulated by the ubiquitin-proteasome system (UPS). Most proteins that are critical for cellular regulations and functions are targets of the process. Ubiquitination is comprised of a sequence of three enzymatic steps, and aberrations in the pathway can lead to tumor development and progression as observed in many cancer types. Recent evidence indicates that targeting the UPS is effective for certain cancer treatment, but many more potential targets might have been previously overlooked. In this review, we will discuss the current state of small molecules that target various elements of ubiquitination. Special attention will be given to novel inhibitors of E3 ubiquitin ligases, especially those in the SCF family.

Computational prediction of protein hot spot residues.

Most biological processes involve multiple proteins interacting with each other. It has been recently discovered that certain residues in these protein-protein interactions, which are called hot spots, contribute more significantly to binding affinity than others. Hot spot residues have unique and diverse energetic properties that make them challenging yet important targets in the modulation of protein-protein complexes. Design of therapeutic agents that interact with hot spot residues has proven to be a valid methodology in disrupting unwanted protein-protein interactions. Using biological methods to determine which residues are hot spots can be costly and time consuming. Recent advances in computational approaches to predict hot spots have incorporated a myriad of features, and have shown increasing predictive successes. Here we review the state of knowledge around protein-protein interactions, hot spots, and give an overview of multiple in silico prediction techniques of hot spot residues.