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3D‐QSAR Applied to the Quantitative Prediction of Penicillin G Amidase Selectivity

A new approach for predicting the selectivity of penicillin G amidase (PGA) - expressed as k cat / K M - is here described. Regression models were constructed correlating the experimentally determined k cat /K M of a limited number of substrates to molecular descriptors calculated by using methods generally employed in drug discovery for quantitative structure-activity relationship (3D-QSAR methods). Two different methods for the calculation of molecular descriptors have been tested, namely GRIND and Volsurf. The real predictions, made on molecules not used for constructing the models, had an accuracy sufficient for being useful in the experimental practice. Both approaches led to models able to predict substrate selectivity even without modelling the enzyme-substrate complex, whereas the prediction of enantioselectivity was feasible only by combining the GRIND approach with the conformational analysis of the substrates inside the enzyme's active site. The present approach represents an actual alternative to screening procedures since it allows one to develop a whole predicting model in a few hours, once a small set of experimental data is made available.