José L. Medina-Franco

Recognizing Pitfalls in Virtual Screening: A Critical Review

The aim of virtual screening (VS) is to identify bioactive compounds through computational means, by employing knowledge about the protein target (structure-based VS) or known bioactive ligands (ligand-based VS). In VS, a large number of molecules are ranked according to their likelihood to be bioactive compounds, with the aim to enrich the top fraction of the resulting list (which can be tested in bioassays afterward). At its core, VS attempts to improve the odds of identifying bioactive molecules by maximizing the true positive rate, that is, by ranking the truly active molecules as high as possible (and, correspondingly, the truly inactive ones as low as possible). In choosing the right approach, the researcher is faced with many questions: where does the optimal balance between efficiency and accuracy lie when evaluating a particular algorithm; do some methods perform better than others and in what particular situations; and what do retrospective results tell us about the prospective utility of a particular method? Given the multitude of settings, parameters, and data sets the practitioner can choose from, there are many pitfalls that lurk along the way which might render VS less efficient or downright useless. This review attempts to catalogue published and unpublished problems, shortcomings, failures, and technical traps of VS methods with the aim to avoid pitfalls by making the user aware of them in the first place.

Novel and selective DNA methyltransferase inhibitors: Docking-based virtual screening and experimental evaluation.

The DNA methyltransferase (DNMT) enzyme family consists of four members with diverse functions and represents one of the most promising targets for the development of novel anticancer drugs. However, the standard drugs for DNMT inhibition are non-selective cytosine analogues with considerable cytotoxic side-effects that have been developed several decades ago. In this work, we conducted a virtual screening of more than 65,000 lead-like compounds selected from the National Cancer Institute collection using a multistep docking approach with a previously validated homology model of the catalytic domain of human DNMT1. Experimental evaluation of top-ranked molecules led to the discovery of novel small molecule DNMT1 inhibitors. Virtual screening hits were further evaluated for DNMT3B inhibition revealing several compounds with selectivity towards DNMT1. These are the first small molecules reported with biochemical selectivity towards an individual DNMT enzyme capable of binding in the same pocket as the native substrate cytosine, and are promising candidates for further rational optimization and development as anticancer drugs. The availability of enzyme-selective inhibitors will also be of great significance for understanding the role of individual DNMT enzymes in epigenetic regulation.

Chemoinformatic Analysis of Combinatorial Libraries, Drugs, Natural Products and Molecular Libraries Small Molecule Repository

A multiple criteria approach is presented, that is used to perform a comparative analysis of four recently developed combinatorial libraries to drugs, Molecular Libraries Small Molecule Repository (MLSMR) and natural products. The compound databases were assessed in terms of physicochemical properties, scaffolds, and fingerprints. The approach enables the analysis of property space coverage, degree of overlap between collections, scaffold and structural diversity, and overall structural novelty. The degree of overlap between combinatorial libraries and drugs was assessed using the R-NN curve methodology, which measures the density of chemical space around a query molecule embedded in the chemical space of a target collection. The combinatorial libraries studied in this work exhibit scaffolds that were not observed in the drug, MLSMR, and natural products databases. The fingerprint-based comparisons indicate that these combinatorial libraries are structurally different than current drugs. The R-NN curve methodology revealed that a proportion of molecules in the combinatorial libraries is located within the property space of the drugs. However, the R-NN analysis also showed that there are a significant number of molecules in several combinatorial libraries that are located in sparse regions of the drug space.

Visualization of the Chemical Space in Drug Discovery

Chemical space has become a key concept in drug discovery. The continued growth in the number of molecules available raises the question regarding how many compounds may exist and which ones have the potential to become drugs. Analysis and visualization of the chemical space covered by public, commercial, in-house and virtual compound collections have found multiple applications in diversity analysis, in silico property profiling, data mining, virtual screening, library design, prioritization in screening campaigns, and acquisition of compound collections, among others. This review covers several techniques, computational programs and approaches that have been developed to visualize, navigate and study the chemical space of molecular databases. Techniques developed in our group are presented including a quantitative assessment of the multi-fusion similarity maps. Additionally an application of 3D-similarity, based on the overlay of chemical structures, to represent the chemical space is introduced. Several comparisons of the chemical space covered by compound collections from different sources such as combinatorial libraries, drugs and natural products, or directed to specific therapeutic areas are also discussed.

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.

Expanding the medicinally relevant chemical space with compound libraries

Analysis of marketed drugs and commercial vendor libraries used in high-throughput screening suggests that the medicinally relevant chemical space may be expanded to unexplored regions. Novel regions of the chemical space can be conveniently explored with structurally unique molecules with increased complexity and balanced physicochemical properties. As a case study, we discuss the chemoinformatic profile of natural products in the Traditional Chinese Medicine (TCM) database and a large collection assembled from 30 small-molecule combinatorial libraries with emphasis on assessing molecular complexity. The herein surveyed combinatorial libraries have been successfully used over the past 20 years to identify novel bioactive compounds across different therapeutic areas. Combinatorial libraries and natural products are suitable sources to expand the traditional relevant medicinal chemistry space.

Nanaomycin A Selectively Inhibits DNMT3B and Reactivates Silenced Tumor Suppressor Genes in Human Cancer Cells

Enzymes involved in the epigenetic regulation of the genome represent promising starting points for therapeutic intervention by small molecules, and DNA methyltransferases (DNMT) are emerging targets for the development of a new class of cancer therapeutics. In this work, we present nanaomycin A, initially identified by a virtual screening for inhibitors against DNMT1, as a compound inducing antiproliferative effects in three different tumor cell lines originating from different tissues. Nanaomycin A treatment reduced the global methylation levels in all three cell lines and reactivated transcription of the RASSF1A tumor suppressor gene. In biochemical assays, nanaomycin A revealed selectivity toward DNMT3B. To the best of our knowledge, this is the first DNMT3B-selective inhibitor identified to induce genomic demethylation. Our study thus establishes the possibility of selectively inhibiting individual DNMT enzymes. Mol Cancer Ther; 9(11); 3015–23. ©2010 AACR.

Synthesis and Biochemical Evaluation of Δ2-Isoxazoline Derivatives as DNA Methyltransferase 1 Inhibitors

A series of Δ(2)-isoxazoline constrained analogues of procaine/procainamide (7a-k and 8a-k) were prepared and their inhibitory activity against DNA methyltransferase 1 (DNMT1) was tested. Among them, derivative 7b is far more potent in vitro (IC(50) = 150 μM) than other non-nucleoside inhibitors and also exhibits a strong and dose-dependent antiproliferative effect against HCT116 human colon carcinoma cells. The binding mode of 7b with the enzyme was also investigated by means of a simple competition assay as well as of docking simulations conducted using the recently published crystallographic structure of human DNMT1. On the basis of the findings, we assessed that the mode of inhibition of 7b is consistent with a competition with the cofactor and propose it as a novel lead compound for the development of non-nucleoside DNMT inhibitors.

Molecular modeling and molecular dynamics studies of hydralazine with human DNA methyltransferase 1.

A series of DNA methyltransferase 1 (DNMT1) inhibitors were modeled by docking and molecular dynamics studies to rationalize their activity. Our findings will be valuable in guiding research efforts toward the rational design and virtual screening of novel DNMT inhibitors.

Antidiabetic activity of some pentacyclic acid triterpenoids, role of PTP-1B: in vitro, in silico, and in vivo approaches.

The aim of the current study was to investigate the oral antidiabetic activity of four structurally-related triterpenic acids: ursolic (RE-01), oleanolic (RE-02), moronic (RE-03) and morolic (RE-04) acids. STZ-nicotinamide diabetic rats were treated with these triterpenes (50 mg/kg) and the antidiabetic effects in acute experiment were determined. All compounds showed significant antidiabetic activity in comparison with control group (p<0.05). The in vitro inhibitory activity of compounds against protein tyrosine phosphatase 1B (PTP-1B) was also evaluated. At 50 μM, the enzymatic activity was almost completely inhibited. All compounds were docked with a crystal structure of PTP-1B. Docking results suggested the potential binding of the triterpenic acids in a binding pocket next to the catalytic site. An extensive hydrogen bond network with the carboxyl group and Van der Waals interactions stabilize the protein-ligand complexes.