Two personal perspectives on a key issue in contemporary 3D QSAR

Identification of Thyroid Hormone Disruptors among HO-PBDEs: In Vitro Investigations and Coregulator Involved Simulations

Some hydroxylated polybrominated diphenyl ethers (HO-PBDEs), that have been widely detected in the environment and tissues of humans and wildlife, bind to thyroid hormone (TH) receptor (TR) and can disrupt functioning of systems modulated by the TR. However, mechanisms of TH disrupting effects are still equivocal. Here, disruption of functions of TH modulated pathways by HO-PBDEs was evaluated by assays of competitive binding, coactivator recruitment, and proliferation of GH3 cells. In silico simulations considering effects of coregulators were carried out to investigate molecular mechanisms and to predict potencies for disrupting functions of the TH. Some HO-PBDEs were able to bind to TR with moderate affinities but were not agonists. In GH3 proliferation assays, 13 out of 16 HO-PBDEs were antagonists for the TH. In silico simulations of molecular dynamics revealed that coregulators were essential for identification of TH disruptors. Among HO-PBDEs, binding of passive antagonists induced repositioning of H12, blocking AF-2 (transactivation function 2) and preventing recruitment of the coactivator. Binding of active antagonists exposed the coregulator binding site, which tended to bind to the corepressor rather than the coactivator. By considering both passive and active antagonisms, anti-TH potencies of HO-PBDEs could be predicted from free energy of binding.

Machine-learning techniques applied to antibacterial drug discovery

The emergence of drug-resistant bacteria threatens to revert humanity back to the preantibiotic era. Even now, multidrug-resistant bacterial infections annually result in millions of hospital days, billions in healthcare costs, and, most importantly, tens of thousands of lives lost. As many pharmaceutical companies have abandoned antibiotic development in search of more lucrative therapeutics, academic researchers are uniquely positioned to fill the pipeline. Traditional high-throughput screens and lead-optimization efforts are expensive and labor intensive. Computer-aided drug-discovery techniques, which are cheaper and faster, can accelerate the identification of novel antibiotics, leading to improved hit rates and faster transitions to preclinical and clinical testing. The current review describes two machine-learning techniques, neural networks and decision trees, that have been used to identify experimentally validated antibiotics. We conclude by describing the future directions of this exciting field.

X-ray crystallographic structures as a source of ligand alignment in 3D-QSAR

Three-dimensional quantitative structure-activity relationships (3D-QSAR) analyses are methods correlating a pharmacological property with a mathematical representation of a molecular property distribution around three-dimensional molecular models for a set of congeners. 3D-QSAR methods are known to be highly sensitive to ligand conformation and alignment method. The current study collects 32 unique positions of congeneric ligands co-crystallized with the binding domain of AMPA receptors and aligns them using protein coordinates. Thus, it allows for a unique opportunity to consider a ligands' orientation aligned by their mode of binding in a native molecular target. Comparative molecular field analysis (CoMFA) models were generated for this alignment and compared with the results of analogous modeling using standard structure-based alignment or obtained in docking simulations of the ligands' molecules. In comparison with classically derived models, the model based on X-ray crystallographic studies showed much better performance and statistical significance. Although the 3D-QSAR methods are mainly employed when crystallographic information is limited, the current study underscores the importance that the selection of inappropriate molecular conformations and alignment methods can lead to generation of erroneous models and false conclusions.