Abstract
Background: Aminopeptidase N also known as APN/CD13 is a zinc-dependent type II membrane-bond ectopeptidase that is overexpressed on cancer cells. APN/CD13 is considered as an attractive target for anti-cancer drug design due to key roles in tumor invasion, angiogenesis and metastasis. Therefore, study of quantitative relationships between activity and structures of APN/CD13 inhibitors can provide useful information for the designing and synthesis of novel APN inhibitors.
Methods: In this study, the linear method was used to develop QSAR models in order to predict the activities of APN/CD13 inhibitors. A dataset that consisted of 39 leucine ureido derivatives was divided into the training and test subsets. Genetic algorithm and stepwise methods have been employed for selection of relevant descriptors.
Results: Multiple linear regression analysis with GA selection was used to model the structureactivity relationships. In this model, R2 was 0.84 for the training set and 0.67 for the test set. We also applied the SW technique as variable selection procedure to the same data set. With the use of SW, the 6 most relevant descriptors were selected to build the model. The value of R2 of the SWMLR model was 0.87 for the training set and 0.77 for the test set. Both models were validated by leave-one-out (LOO) cross-validation.
Conclusion: The results of SW-MLR and of GA-MLR were confirmed and is appeared that the SW-MLR model had more power to predict APN inhibitory activity. On the basis of QSAR models, charge polarization, the atomic masses, polarizability, the atomic van der Waals volumes, the molecular symmetry and aromaticity index were found to be important factors controlling the APN inhibitory activity.
Keywords: Aminopeptidase, QSAR, multiple linear regressions, stepwise, genetic algorithm, leucine ureido derivatives.
Graphical Abstract
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