Computer-aided Discovery Research Articles

Natural products as DNA methyltransferase inhibitors: a computer-aided discovery approach

DNA methyltransferases (DNMTs) represent promising targets for the development of unique anticancer drugs. However, all DNMT inhibitors currently in clinical use are nonselective cytosine analogs with significant cytotoxic side-effects. Several natural products, covering diverse chemical classes, have indicated DNMT inhibitory activity, but these effects have yet to be systematically evaluated. In this study, we provide experimental data suggesting that two of the most prominent natural products associated with DNA methylation inhibition, (-)-epigallocathechin-3-gallate (EGCG) and curcumin, have little or no pharmacologically relevant inhibitory activity. We therefore conducted a virtual screen of a large database of natural products with a validated homology model of the catalytic domain of DNMT1. The virtual screening focused on a lead-like subset of the natural products docked with DNMT1, using three docking programs, following a multistep docking approach. Prior to docking, the lead-like subset was characterized in terms of chemical space coverage and scaffold content. Consensus hits with high predicted docking affinity for DNMT1 by all three docking programs were identified. One hit showed DNMT1 inhibitory activity in a previous study. The virtual screening hits were located within the biological-relevant chemical space of drugs, and represent potential unique DNMT inhibitors of natural origin. Validation of these virtual screening hits is warranted.

Ligand-Based Computer-Aided Discovery of Tyrosinase Inhibitors. Applications of the TOMOCOMD-CARDD Method to the Elucidation of New Compounds

In this review an overview of the application of computational approaches is given. Specifically, the uses of Quantitative Structure-Activity Relationship (QSAR) methods for in silico identification of new families of compounds as novel tyrosinase inhibitors are revised. Assembling, validation of models through prediction series, and virtual screening of external data sets are also shown, to prove the accuracy of the QSAR models obtained with the TOMOCOMD-CARDD (TOpological MOlecular COMputational Design-Computer-Aided Rational Drug Design) software and Linear Discriminant Analysis (LDA) as statistical technique. Together with this, a database is collected for these QSAR studies, and could be considered a useful tool in future QSAR modeling of tyrosinase activity and for scientists that work in the field of this enzyme and its inhibitors. Finally, a translation to real world applications is shown by the use of QSAR models in the identification and posterior in-vitro evaluation of different families of compounds. Several different classes of compounds from various sources (natural and synthetic) were identified. Between them, we can find tetraketones, cycloartanes, ethylsteroids, lignans, dicoumarins and vanilloid derivatives. Finally, some considerations are discussed in order to improve the identification of novel drug-like compounds based on the use of QSAR-Ligand-Based Virtual Screening (LBVS).

Computer-Aided Discovery, Validation, and Mechanistic Characterization of Novel Neolignan Activators of Peroxisome Proliferator-Activated Receptor γ

Peroxisome proliferator-activated receptor gamma (PPAR gamma) agonists are used for the treatment of type 2 diabetes and metabolic syndrome. However, the currently used PPAR gamma agonists display serious side effects, which has led to a great interest in the discovery of novel ligands with favorable properties. The aim of our study was to identify new PPARgamma agonists by a PPAR gamma pharmacophore-based virtual screening of 3D natural product libraries. This in silico approach led to the identification of several neolignans predicted to bind the receptor ligand binding domain (LBD). To confirm this prediction, the neolignans dieugenol, tetrahydrodieugenol, and magnolol were isolated from the respective natural source or synthesized and subsequently tested for PPAR gamma receptor binding. The neolignans bound to the PPAR gamma LBD with EC(50) values in the nanomolar range, exhibiting a binding pattern highly similar to the clinically used agonist pioglitazone. In intact cells, dieugenol and tetrahydrodieugenol selectively activated human PPAR gamma-mediated, but not human PPAR alpha- or -beta/delta-mediated luciferase reporter expression, with a pattern suggesting partial PPAR gamma agonism. The coactivator recruitment study also demonstrated partial agonism of the tested neolignans. Dieugenol, tetrahydrodieugenol, and magnolol but not the structurally related eugenol induced 3T3-L1 preadipocyte differentiation, confirming effectiveness in a cell model with endogenous PPAR gamma expression. In conclusion, we identified neolignans as novel ligands for PPAR gamma, which exhibited interesting activation profiles, recommending them as potential pharmaceutical leads or dietary supplements.

Computer-Aided Identification of Ligands for GPCR Anti-Obesity Targets

Many orphan G-protein-coupled receptors (GPCR) have emerged as potential obesity targets. The authors are interested in the computer-aided discovery and development of small molecule anti-obesity agents targeting GPCR. Computational modeling studies have mainly been conducted on ghrelin receptor (GHS-R), melanocortin 4-receptor (MC4R), melanin-concentrating hormone 1 receptor (MCH1R) and cannabinoid 1 receptor (CB1R) in recent publications. Here, a homology modeling strategy for these receptors will be introduced, and the key structural features of their active and inactive conformations will be reviewed. In addition, the authors describe the uses of virtual screening methods to identify novel antagonists of MCH1R and CB1R, which provide valuable examples of the computer-aided design and structural optimization of GPCR ligands.

Computer-Aided Discovery of Anti-Inflammatory Thiazolidinones with Dual Cyclooxygenase/Lipoxygenase Inhibition

New anti-inflammatory agents possessing dual cyclooxygenase/lipoxygenase (COX/LOX) inhibition were discovered by computer-aided prediction of biological activity for 573 virtually designed chemical compounds. Prediction of biological activity was performed by PASS, and prediction results were analyzed with PharmaExpert software. Nine 2-(thiazole-2-ylamino)-5-phenylidene-4-thiazolidinone derivatives differing by the phenyl group substitution were selected for synthesis and experimental testing as potential COX/LOX inhibitors. Eight tested compounds exhibited anti-inflammatory activity in the carrageenin-induced paw edema. It was shown that seven tested compounds (77.8%) were LOX inhibitors, seven compounds were COX inhibitors (77.8%), and six tested compounds (66.7%) were dual COX/LOX inhibitors. Analysis of lipophilicity of the compounds showed a negative correlation with inhibition of edema formation. The binding modes of the most active compounds of this series (2-(thiazole-2-ylamino)-5-( m-chlorophenylidene)-4-thiazolidinone for COX-1 and COX-2, and 2-(thiazole-2-ylamino)-5-( m-nitrophenylidene)-4-thiazolidinone for 15-LOX) were proposed on the basis of docking studies.

Computer-aided discovery of Smac-mimetics within the plant kingdom

Pharmacophore-based virtual screening of natural product databases is an efficient and trendsetting strategy to exploit nature's multitude of bioactivities [1]. Endogenous Smac (second mitochondrial derived activator of caspase) binds at the Bir3 domain of XIAP (X-linked inhibitor of apoptosis protein) and induces apoptosis in this way. Using a computational approach we intend to isolate new potent Smac-mimetics for the discovery of chemosensitizers upon treatment with conventional chemotherapeutics [2].

Computer-Aided Causal Knowledge Discovery from Text Documents of Process and Equipment Malfunctions

The importance of recording data of process and equipment malfunctions has been widely addressed and an enormous quantity of collections now exists. With problems regarding confidentiality being gradually solved and now with widespread use of computers, the need to develop computer-aided systems to help digest the large volumes of data is critical. Work so far has mainly concentrated on developing systems to help retrieval. Besides retrieval, the current approaches are not able to indicate further uses of the data. In this contribution, a prototype system is described that can be used interactively to discover causal knowledge from collections of text documents about process malfunctions and represent the knowledge in terms of rules and graphical models. This is a flexible system that gives users the freedom to control the knowledge discovery process, while at the same time providing users with maximum support of automatic functions. The system is also featured with a learning mechanism that allows continuous improvement of system performance during use. The graphical networks can also be used to retrieve the original text records from which a causal relationship is extracted.

The Computer-aided Discovery of a Theorem about Young Tableaux

Recently a relationship was discovered between the number of permutations of n letters that have no increasing subsequence of length > k, on the one hand, and the number of Young tableaux of n cells whose first row is of length ≤ k, on the other. The proof seemed quite unmotivated and artificial. Here we describe the sequence of events that led to the discovery, via computer algebra, of this relationship.