Alberto Del Rio

Fast and accurate predictions of binding free energies using MM-PBSA and MM-GBSA

In the drug discovery process, accurate methods of computing the affinity of small molecules with a biological target are strongly needed. This is particularly true for molecular docking and virtual screening methods, which use approximated scoring functions and struggle in estimating binding energies in correlation with experimental values. Among the various methods, MM‐PBSA and MM‐GBSA are emerging as useful and effective approaches. Although these methods are typically applied to large collections of equilibrated structures of protein‐ligand complexes sampled during molecular dynamics in water, the possibility to reliably estimate ligand affinity using a single energy‐minimized structure and implicit solvation models has not been explored in sufficient detail. Herein, we thoroughly investigate this hypothesis by comparing different methods for the generation of protein‐ligand complexes and diverse methods for free energy prediction for their ability to correlate with experimental values. The methods were tested on a series of structurally diverse inhibitors of Plasmodium falciparum DHFR with known binding mode and measured affinities. The results showed that correlations between MM‐PBSA or MM‐GBSA binding free energies with experimental affinities were in most cases excellent. Importantly, we found that correlations obtained with the use of a single protein‐ligand minimized structure and with implicit solvation models were similar to those obtained after averaging over multiple MD snapshots with explicit water molecules, with consequent save of computing time without loss of accuracy. When applied to a virtual screening experiment, such an approach proved to discriminate between true binders and decoy molecules and yielded significantly better enrichment curves.

Binding Estimation after Refinement, a New Automated Procedure for the Refinement and Rescoring of Docked Ligands in Virtual Screening

Binding estimation after refinement (BEAR) is a novel automated computational procedure suitable for correcting and overcoming limitations of docking procedures such as poor scoring function and the generation of unreasonable ligand conformations. BEAR makes use of molecular dynamics simulation followed by MM‐PBSA and MM‐GBSA binding free energy estimates as tools to refine and rescore the structures obtained from docking virtual screenings. As binding estimation after refinement relies on molecular dynamics, the entire procedure can be tailored to the needs of the end‐user in terms of computational time and the desired accuracy of the results. In a validation test, binding estimation after refinement and rescoring resulted in a significant enrichment of known ligands among top scoring compounds compared with the original docking results. Binding estimation after refinement has direct and straightforward application in virtual screening for correcting both false‐positive and false‐negative hits, and should facilitate more reliable selection of biologically active molecules from compound databases.

Comparative analysis of pharmacophore screening tools.

The pharmacophore concept is of central importance in computer-aided drug design (CADD) mainly because of its successful application in medicinal chemistry and, in particular, high-throughput virtual screening (HTVS). The simplicity of the pharmacophore definition enables the complexity of molecular interactions between ligand and receptor to be reduced to a handful set of features. With many pharmacophore screening softwares available, it is of the utmost interest to explore the behavior of these tools when applied to different biological systems. In this work, we present a comparative analysis of eight pharmacophore screening algorithms (Catalyst, Unity, LigandScout, Phase, Pharao, MOE, Pharmer, and POT) for their use in typical HTVS campaigns against four different biological targets by using default settings. The results herein presented show how the performance of each pharmacophore screening tool might be specifically related to factors such as the characteristics of the binding pocket, the use of specific pharmacophore features, and the use of these techniques in specific steps/contexts of the drug discovery pipeline. Algorithms with rmsd-based scoring functions are able to predict more compound poses correctly as overlay-based scoring functions. However, the ratio of correctly predicted compound poses versus incorrectly predicted poses is better for overlay-based scoring functions that also ensure better performances in compound library enrichments. While the ensemble of these observations can be used to choose the most appropriate class of algorithm for specific virtual screening projects, we remarked that pharmacophore algorithms are often equally good, and in this respect, we also analyzed how pharmacophore algorithms can be combined together in order to increase the success of hit compound identification. This study provides a valuable benchmark set for further developments in the field of pharmacophore search algorithms, e.g., by using pose predictions and compound library enrichment criteria.

Structure-Based Design of Potent Aromatase Inhibitors by High-Throughput Docking

Cytochrome P450 aromatase catalyzes the conversion of androgen substrates into estrogens. Aromatase inhibitors (AIs) have been used as first-line drugs in the treatment of estrogen-dependent breast cancer in postmenopausal women. However, the search for new, more potent, and selective AIs still remains necessary to avoid the risk of possible resistances and reduce toxicity and side effects of current available drugs. The publication of a high resolution X-ray structure of human aromatase has opened the way to structure-based virtual screening to identify new small-molecule inhibitors with structural motifs different from all known AIs. In this context, a high-throughput docking protocol was set up and led to the identification of nanomolar AIs with new core structures.

Modulation of Epigenetic Targets for Anticancer Therapy: Clinicopathological Relevance, Structural Data and Drug Discovery Perspectives

Research on cancer epigenetics has flourished in the last decade. Nevertheless growing evidence point on the importance to understand the mechanisms by which epigenetic changes regulate the genesis and progression of cancer growth. Several epigenetic targets have been discovered and are currently under validation for new anticancer therapies. Drug discovery approaches aiming to target these epigenetic enzymes with small-molecules inhibitors have produced the first pre-clinical and clinical outcomes and many other compounds are now entering the pipeline as new candidate epidrugs. The most studied targets can be ascribed to histone deacetylases and DNA methyltransferases, although several other classes of enzymes are able to operate post-translational modifications to histone tails are also likely to represent new frontiers for therapeutic interventions. By acknowledging that the field of cancer epigenetics is evolving with an impressive rate of new findings, with this review we aim to provide a current overview of pre-clinical applications of small-molecules for cancer pathologies, combining them with the current knowledge of epigenetic targets in terms of available structural data and drug design perspectives.

Metformin Impairs Glucose Consumption and Survival in Calu-1 Cells by Direct Inhibition of Hexokinase-II

The anti-hyperglycaemic drug metformin has important anticancer properties as shown by the direct inhibition of cancer cells proliferation. Tumor cells avidly use glucose as a source for energy production and cell building blocks. Critical to this phenotype is the production of glucose-6-phosphate (G6P), catalysed by hexokinases (HK) I and II, whose role in glucose retention and metabolism is highly advantageous for cell survival and proliferation. Here we show that metformin impairs the enzymatic function of HKI and II in Calu-1 cells. This inhibition virtually abolishes cell glucose uptake and phosphorylation as documented by the reduced entrapment of 18F-fluorodeoxyglucose. In-silico models indicate that this action is due to metformin capability to mimic G6P features by steadily binding its pocket in HKII. The impairment of this energy source results in mitochondrial depolarization and subsequent cell death. These results could represent a starting point to open effective strategies in cancer prevention and treatment.

Freely accessible databases of commercial compounds for high- throughput virtual screenings.

In the last decades computer-aided drug design techniques have been successfully used to guide the selection of new hit compounds with biological activity. These methods, that include a broad range of chemoinformatic and computational chemistry algorithms, are still disciplines in full bloom. In particular, virtual screening procedures have celebrated a great popularity for the rapid and cost-effective assessment of large chemical libraries of commercial compounds. While the usage of in silico techniques promises an effective speed-up at the early-stage of the development of new active compounds, computational projects starting from scratch with raw chemical data are often associated with resource- and time-consuming preparation protocols, almost blunting the advantages of using these techniques. In order to help facing these difficulties, in the last years several chemoinformatic projects and tools have emerged in literature and have been useful in preparing curated databases of chemical compounds for high-throughput virtual screening purposes. The review will focus on the detailed analysis of free databases of commercial chemical compounds that are currently employed in virtual screening campaigns for drug design. The scope of this review is to compare such databases and suggest the reader on how and in which conditions the usage of these databases could be recommended.

Metformin, cancer and glucose metabolism

Metformin is the first-line treatment for type 2 diabetes. Results from several clinical studies have indicated that type 2 diabetic patients treated with metformin might have a lower cancer risk. One of the primary metabolic changes observed in malignant cell transformation is an increased catabolic glucose metabolism. In this context, once it has entered the cell through organic cation transporters, metformin decreases mitochondrial respiration chain activity and ATP production that, in turn, activates AMP-activated protein kinase, which regulates energy homeostasis. In addition, metformin reduces cellular energy availability and glucose entrapment by inhibiting hexokinase-II, which catalyses the glucose phosphorylation reaction. In this review, we discuss recent findings on molecular mechanisms that sustain the anticancer effect of metformin through regulation of glucose metabolism. In particular, we have focused on the emerging action of metformin on glycolysis in normal and cancer cells, with a drug discovery perspective.

Rejuvenating sirtuins: the rise of a new family of cancer drug targets.

Sirtuins are a family of NAD+-dependent enzymes that was proposed to control organismal life span about a decade ago. While such role of sirtuins is now debated, mounting evidence involves these enzymes in numerous physiological processes and disease conditions, including metabolism, nutritional behavior, circadian rhythm, but also inflammation and cancer. SIRT1, SIRT2, SIRT3, SIRT6, and SIRT7 have all been linked to carcinogenesis either as tumor suppressor or as cancer promoting proteins. Here, we review the biological rationale for the search of sirtuin inhibitors and activators for treating cancer and the experimental approaches to their identification.

A SMYD3 Small-Molecule Inhibitor Impairing Cancer Cell Growth

SMYD3 is a histone lysine methyltransferase that plays an important role in transcriptional activation as a member of an RNA polymerase complex, and its oncogenic role has been described in different cancer types. We studied the expression and activity of SMYD3 in a preclinical model of colorectal cancer (CRC) and found that it is strongly upregulated throughout tumorigenesis both at the mRNA and protein level. Our results also showed that RNAi‐mediated SMYD3 ablation impairs CRC cell proliferation indicating that SMYD3 is required for proper cancer cell growth. These data, together with the importance of lysine methyltransferases as a target for drug discovery, prompted us to carry out a virtual screening to identify new SMYD3 inhibitors by testing several candidate small molecules. Here we report that one of these compounds (BCI‐121) induces a significant reduction in SMYD3 activity both in vitro and in CRC cells, as suggested by the analysis of global H3K4me2/3 and H4K5me levels. Of note, the extent of cell growth inhibition by BCI‐121 was similar to that observed upon SMYD3 genetic ablation. Most of the results described above were obtained in CRC; however, when we extended our observations to tumor cell lines of different origin, we found that SMYD3 inhibitors are also effective in other cancer types, such as lung, pancreatic, prostate, and ovarian. These results represent the proof of principle that SMYD3 is a druggable target and suggest that new compounds capable of inhibiting its activity may prove useful as novel therapeutic agents in cancer treatment. J. Cell. Physiol. 230: 2447–2460, 2015.