Predicting anti-HIV activity of PETT derivatives: CoMFA approach

HIV-1 (Human Immunodeficiency Virus Type-1) is the pathogenic retrovirus and causative agent of AIDS. HIV-1 RT is one of the key enzymes in the duplication of HIV-1. Inhibitors of HIV-1 RT are classified as NNRTIs and NRTIs. NNRTIs bind in a region not associated with the active site of the enzyme. Within the NNRTIs category, there is a set of inhibitors commonly referred to as phenyl ethyl thiazolyl thiourea (PETT) derivatives. The present 3D QSAR study attempts to explore the structural requirements of phenyl ethyl thiazolyl thiourea (PETT) derivatives for anti-HIV activity. Based on the structures and biodata of previous PETT analogs, 3D-QSAR (CoMFA) study has been performed with a training set consisting of 60 molecules, which resulted in a reliable computational model with q(2)=0.657, S(PRESS)=0.957, r(2)=0.938, and standard error of estimation (SEE)=0.270 with the number of partial least square (PLS) components being 5. It is shown that the steric and electrostatic properties predicted by CoMFA contours can be related to the anti-HIV activity. The predictive ability of the resultant model was evaluated using a test set comprised of 11 molecules and the predicted r(2)=0.893. This model is a more significant guide to trace the features that really matter especially with respect to the design of novel compounds.     

Comparative molecular field analysis and comparative molecular similarity indices analysis of human thymidine kinase 1 substrates

Thymidine kinase 1 (TK1) is a key target for antiviral and anticancer chemotherapy. Three-dimensional quantitative structure-activity relationship (3D-QSAR) using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques was applied to analyze the phosphorylation capacity of a series of 31 TK1 substrates. The optimal predictive CoMFA model with 26 molecules provided the following values: cross-validated r(2) (q(2))=0.651, non-cross-validated r(2)=0.980, standard error of estimate (s)=0.207, F=129.3. For the optimal CoMSIA model the following values were found: q(2)=0.619, r(2)=0.994, s=0.104, F=372.2. The CoMSIA model includes steric, electrostatic, and hydrogen bond donor fields. The predictive capacity of both models was successfully validated by calculating known phosphorylation rates of five TK1 substrates that were not included in the training set. Contour maps obtained from CoMFA and CoMSIA models correlated with the experimentally developed SAR.