Drug discovery of small molecules targeting the higher-order hTERT promoter G-quadruplex.

DNA G-quadruplexes (G4s) are now widely accepted as viable targets in the pursuit of anticancer therapeutics. To date, few small molecules have been identified that exhibit selectivity for G4s over alternative forms of DNA, such as the ubiquitous duplex. We posit that the lack of current ligand specificity arises for multiple reasons: G4 atomic models are often small, monomeric, single quadruplex structures with few or no druggable pockets; targeting G-tetrad faces frequently results in the enrichment of extended electron-deficient polyaromatic end-pasting scaffolds; and virtual drug discovery efforts often under-sample chemical search space.

Evidence for the contribution of non-covalent steroid interactions between DNA and topoisomerase in the genotoxicity of steroids.

Fifty two steroids and 9 Vitamin D analogs were docked into ten crystallographically-defined DNA dinucleotide sites and two human topoisomerase II ATP binding sites using two computational programs, Autodock and Surflex. It is shown that both steroids and Vitamin D analogs exhibit a propensity for non-covalent intercalative binding to DNA. A higher predicted binding affinity was found, however, for steroids and the ATP binding site of topoisomerase; in fact these drugs exhibited among the highest topo II binding observed in over 1370 docked drugs.

Prediction of noncovalent Drug/DNA interaction using computational docking models: studies with over 1350 launched drugs.

Noncovalent chemical/DNA interactions, for example, intercalation and groove-binding, may be more important to genomic integrity than previously appreciated, and there may very well be genotoxic consequences of that binding. It is of importance, then, to develop methods allowing a determination or prediction of such interactions. This would have particular utility in the pharmaceutical industry where genotoxicity is, for the most part, disallowed in new drug entities.

Global structure-activity relationship model for nonmutagenic carcinogens using virtual ligand-protein interactions as model descriptors.

Structure-activity relationship (SAR) models are powerful tools to investigate the mechanisms of action of chemical carcinogens and to predict the potential carcinogenicity of untested compounds. We describe the use of a traditional fragment-based SAR approach along with a new virtual ligand-protein interaction-based approach for modeling of nonmutagenic carcinogens. The ligand-based SAR models used descriptors derived from computationally calculated ligand-binding affinities for learning set agents to 5495 proteins.

Structure-based drug design: from nucleic acid to membrane protein targets.

The in silico methods for drug discovery are becoming increasingly powerful and useful. That, in combination with increasing computer processor power, in our case using a novel distributed computing grid, has enabled us to greatly enhance our virtual screening efforts. Herein we review some of these efforts using both receptor and ligand-based virtual screening, with the goal of finding new anti-cancer agents.