Comparative molecular field analysis of fungal squalene epoxidase inhibitors.

Comparative molecular field analysis (CoMFA) of fungal squalene epoxidase inhibitors exhibiting antifungal activity reported in terms of minimum inhibitory concentration (MIC) was performed. Ninety-two molecules belonging to different chemical classes, namely terbinafine analogues, benzylamines, homopropargylamines, and carbon analogues were divided into training set and test set. The initial conformations of the inhibitors obtained from molecular dynamics simulations for 50 ps in aqueous solution were used in the study. Out of three charges used in the study, Gasteiger-H]uckel charges result in models with good internal predictivity. Initial analysis of 92 molecules (analysis A) resulted in models with low predictive r(2) values for activity against three organisms. This data set was modified by exclusion of 13 molecules, and analysis was performed again. This analysis of 79 molecules (analysis B) resulted in improvement in predictivity of the CoMFA models and cross-validated r(2) values of 0.583, 0.509, and 0.502 for Candida albicans, Aspergillus fumigatus, and Trichophyton mentagrophytes, respectively. These models were used to predict the activities of the molecules belonging to the test set. The models from analysis B show better correlative and predictive properties than analysis A. Comparison of CoMFA contour maps for activity against three different fungi revealed differentiating structural requirements.

Understanding the antifungal activity of terbinafine analogues using quantitative structure-activity relationship (QSAR) models.

Terbinafine and its analogues, which are a major class of non-azole antifungal agents, are known to act by inhibition of squalene epoxidase enzyme in fungal cells. We have performed a quantitative structure-activity relationship (QSAR) study on a series of 92 molecules using different types of physicochemical descriptors. Inhibitors were divided into five classes depending upon chemical structure. QSAR models were generated for correlation between antifungal activity against Candida albicans using genetic function approximation (GFA) technique. Equations were evaluated using internal as well as external test set predictions. Models generated for all these classes show that steric properties and conformational rigidity of side chains play an important role for the activity. The present QSAR analysis agrees with the results of the previously reported CoMFA study.

Selectivity analysis of 5-(arylthio)-2,4-diaminoquinazolines as inhibitors of Candida albicans dihydrofolate reductase by molecular dynamics simulations.

A series of 5-(arylthio)-2,4-diaminoquinazolines are known as selective inhibitors of dihydrofolate reductase (DHFR) from Candida albicans. We have performed docking and molecular dynamics simulations of these inhibitors with C. albicans and human DHFR to understand the basis for selectivity of these agents. Study was performed on a selected set of 10 compounds with variation in structure and activity. Molecular dynamics simulations were performed at 300 K for 45 ps with equilibration for 10 ps. Trajectory data was analyzed on the basis of hydrogen bond interactions, energy of binding and conformational energy difference. The results indicate that hydrogen bonds formed between the compound and the active site residues are responsible for inhibition and higher potency. The selectivity index, i.e the ratio of I50 against human DHFR to I50 against fungal DHFR, is mainly determined by the conformation adapted by the compounds within the active site of two enzymes. Since the human DHFR active site is rigid, the compound is trapped in a higher energy conformation. This energy difference between the two conformations deltaE mainly governs the selectivity against fungal DHFR. The information generated from this analysis of potency and selectivity should be useful for further work in the area of antifungal research.