3D-QSAR OF ANGIOTENSIN-CONVERTING ENZYME AND THERMOLYSIN INHIBITORS -A COMPARISON OF COMFA MODELS BASED ON DEDUCED AND EXPERIMENTALLY DETERMINED ACTIVE-SITE GEOMETRIES

The ability of comparative molecular field analysis (CoMFA), a three-d imensional, quantitative structure-activity relationship (3-D QSAR) pa radigm, to predict the activity of inhibitors of angiotensin-convertin g enzyme (ACE) and thermolysin was examined. Correlations derived from computationally and experimentally determined alignment rules were co mpared. The correlations derived for the ACE series using alignment ru les determined from a systematic conformational search (Mayer, D.; Nay lor, C. B.; Motoc, I.; Marshall, G. R. J. Comput.-Aided Molec. Des. 19 87, 1, 3-16) were comparable to those derived for the thermolysin inhi bitors using alignment rules defined by crystallographic data. Models derived from potential fields alone, however, were insufficient for ac curately quantifying and predicting the nature of enzyme-inhibitor int eractions. The predictive ability of the ACE model for a series of mol ecules not included in the training set was improved by the addition o f a zinc indicator variable which explicitly defined the nature of the zinc-ligand interaction, an effect not observed within the thermolysi n series. The effects of additional parameters, such as torsional degr ees of freedom and the change in conformational enthalpy, DELTAH(confo rm) = H(aligned) - H(min), were also examined. Experimentally derived alignment rules based on known structures of three-dimensional complex es produced predictive correlations for thermolysin inhibitors compara ble, but not superior, to the correlations for ACE inhibitors based on alignment rules which were computationally deduced. The use of the ac tive analog approach to determine active site geometries in the absenc e of structural data on the receptor is strongly supported by these re sults. Additionally, the correlations indicate that 3-D QSARs based on alignment rules derived from structure-activity data alone can produc e statistically significant predictive correlations for quite diverse, noncongeneric compounds.