Three-Dimensional Quantitative Structure−Activity Relationship Study on Cyclic Urea Derivatives as HIV-1 Protease Inhibitors:  Application of Comparative Molecular Field Analysis

Abstract

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models have been developed using comparative molecular field analysis (CoMFA) on a large data set (118 compounds) of diverse cyclic urea derivatives as protease inhibitors against the human immunodeficiency virus type 1 (HIV-1). X-ray crystal structures of HIV-1 protease bound with this class of inhibitors were used to derive the most probable bioactive conformations of the inhibitors. The enzyme active site was used as a constraint to limit the number of possible conformations that are sterically accessible. The test sets have been created keeping in mind structural diversity as well as the uniform simple statistical criteria (mean, standard deviation, high and low values) of the protease inhibitory activities of the molecules compared to the training sets. Multiple predictive models have been developed with the training sets (93 compounds in each set) and validated with the corresponding test sets (25 compounds in each set). All the models yielded high predictive correlation coefficients (q2 from 0.699 to 0.727), substantially high fitted correlation coefficients (r2 from 0.965 to 0.973), and reasonably low standard errors of estimates (S from 0. 239 to 0.265). The steric and electrostatic effects have approximately equal contributions, 45% and 55% (approximately), respectively, toward explaining protease inhibitory activities. This analysis yielded models with significant information on steric and electrostatic interactions clearly discerned by the respective coefficient contour plots when overlapped on the X-ray structure of the HIV-1 protease. The HINT CoMFA study revealed significant contribution of hydrophobicity toward protease inhibitory activity. The 3D visualization technique utilizing these contour plots as well as the receptor site geometry may significantly improve our understanding of the inhibitor-protease (HIV-1) interactions and help in designing compounds with improved activity.

Structure Based 3D-QSAR Studies on Cholinesterase Inhibitors

“…These models can be used to identify important protein-ligand interactions and are found to be consistent with the crystal structure of the protein-ligand complex (Prathipati et al, 2005). The availability of X-ray crystal coordinates of inhibitors bound with the receptor have contributed to formulate effective predictive 3D-QSAR models based on (1) identification of possible conformations of related inhibitors in the active site and (2) understanding of the interactions of the inhibitors with the receptor in three-dimension (Debnath, 1999). A 3D-QSAR experiment performs two functions: the derivation of a statistically significant and highly predictive model that is used to estimate and rank new compounds for planned synthesis and the provision of an easily interpretable graphical tool which can identify a particular physicochemical property for increased affinity and selectivity (Klebe, 1998).…”

Designing hypothesis of substituted benzoxazinones as HIV-1 reverse transcriptase inhibitors: QSAR approach

“…The 2D QSAR studies have been carried out for anti-HIV activities of different groups of compounds [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] . Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) have also been carried out for anti-HIV activity of a given set of molecules in rational drug design and its related applications [24][25][26][27][28][29][30][31][32][33][34][35][36][37] .…”

QSAR Modeling of HIV‐1 Protease Inhibition on Six‐ and Seven‐membered Cyclic Ureas

“…Debnath [14] studied a set of 29 symmetrical ureas and found a model with a good statistical quality with R 2 ¼ 0.97, q 2 ¼ 0.72, and which also could predict the activity of testing compounds. Debnath also studied a larger set, a total of 118 cyclic ureas [15] and found models with R 2 up to 0.97, q 2 up to 0.73, and the validation sets R 2 up to 0.64. Later, Tervo et al [16] studied almost an equally large set using both CoMFA and CoMSIA approaches.…”