PubMed Health⌕ Search

Biomedical subjects

Shane D Peterson

Publications and source records attributed to Shane D Peterson.

2 recordsLinked to original sources

Phenylglycine as a novel P2 scaffold in hepatitis C virus NS3 protease inhibitors.

Molecular modeling and inhibitory potencies of tetrapeptide protease inhibitors of HCV NS3 proposed phenylglycine as a new promising P2 residue. The results suggest that phenylglycine might be capable of interacting with the NS3 (protease-helicase/NTPase) in ways not possible for the common P2 proline-based inhibitors. Thus, a series of tripeptides, both linear and macrocyclic, based on p-hydroxy-phenylglycine in the P2 position were prepared and their inhibitory effect determined. When the p-hydroxy group was replaced by methoxy, isoquinolin-, or quinolinyloxy functions, inhibitors with improved potencies were obtained. The P2 phenylglycine-based inhibitors were further optimized by C-terminal extension to acyl sulfonamides and by P1-P3 cyclization, which gave products with inhibition constants in the nanomolar range ( approximately 75nM).

Antiviral Agents↗

Improved CoMFA modeling by optimization of settings.

The possibility of improving the predictive ability of comparative molecular field analysis (CoMFA) by settings optimization has been evaluated to show that CoMFA predictive ability can be improved. Ten different CoMFA settings are evaluated, producing a total of 6120 models. This method has been applied to nine different data sets, including the widely used benchmark steroid data set, as well as eight other data sets proposed as QSAR benchmarking data sets by Sutherland et al. (J. Med. Chem. 2004, 47, 5541-5554). All data sets have been studied using training and test sets to allow for both internal (q(2)) and external (r(2)(pred)) predictive ability assessment. CoMFA settings optimization was successful in developing models with improved q(2) and r(2)(pred) as compared to default CoMFA modeling. Optimized CoMFA is compared with comparative molecular similarity indices analysis (CoMSIA) and holographic quantitative structure-activity relationship (HQSAR) models and found to consistently produce models with improved or equivalent q(2) and r(2)(pred). The ability of settings optimization to improve model predictive ability has been validated using both internal and external predictions, and the risk of chance correlation has been evaluated using response variable randomization tests.

Journal Article↗