Using pharmacophore models to gain insight into structural binding and virtual screening: an application study with CDK2 and human DHFR.

This study provides results from two case studies involving the application of the HypoGenRefine algorithm within Catalyst for the automated generation of excluded volume from ligand information alone. A limitation of pharmacophore feature hypothesis alone is that activity prediction is based purely on the presence and arrangement of pharmacophoric features; steric effects remained unaccounted. Recently reported studies have illustrated the usefulness of combining excluded volumes to the pharmacophore models.

SitePrint: three-dimensional pharmacophore descriptors derived from protein binding sites for family based active site analysis, classification, and drug design.

Integrating biological and chemical information is one key task in drug discovery, and one approach to attaining this goal is via three-dimensional pharmacophore descriptors derived from protein binding sites. The SitePrint program generates, aligns, scores, and classifies three-dimensional pharmacophore descriptors, active site grids, and ligand surfaces. The descriptors are formed from molecular fragments that have been docked, minimized, filtered, and clustered in protein active sites.

A molecular basis for the selectivity of thiadiazole urea inhibitors with stromelysin-1 and gelatinase-A from generalized born molecular dynamics simulations.

Matrix metalloproteinases (MMPs) represent a potentially important class of therapeutic targets for the treatment of diseases such as cancer. Selective inhibition of MMPs will be required given the high sequence identity across the family and the discovery that individual MMPs also regulate the natural angiogenesis inhibitor angiostatin. In this study, we have used computational methods to model the selectivity for six thiadiazole urea inhibitors with stromelysin-1 and gelatinase-A, two homologous MMPs that have been implicated in breast cancer.

Small molecule affinity fingerprinting. A tool for enzyme family subclassification, target identification, and inhibitor design.

Classifying proteins into functionally distinct families based only on primary sequence information remains a difficult task. We describe here a method to generate a large data set of small molecule affinity fingerprints for a group of closely related enzymes, the papain family of cysteine proteases. Binding data was generated for a library of inhibitors based on the ability of each compound to block active-site labeling of the target proteases by a covalent activity based probe (ABP).