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Classification-based QSAR models for the pbkp_rediction of the bioactivity of ACE-inhibitor peptides

Abstract:

Background: Local classification models were used to establish Quantitative Struc-ture−Activity Relationships (QSARs) of bioactive di−, tri− and tetrapeptides, with their capacity to inhibit Angiotensin Converting Enzyme (ACE). These discrete models can thus pbkp_redict this activity for other peptides obtained from functional foods. These types of peptides allow some foods to be considered nutraceuticals. Method: A database of 313 molecules of di−, tri− and tetrapeptides was investigated and antihypertensive activities of peptides, expressed as log (1/IC 50 ), were separated into two qualitative classes: low activity (inactive) was associated with experimental values under the 66 th percentile and active peptides with values above this threshold. Chemicals were divided into a training set, including 70% of the peptides, and a test set for external validation. Genetic algorithms-variable subset selection coupled with the kNN and N3 local classifiers were applied to select the best subset of molecular descriptors from a pool of 953 Dragon descriptors. Both models were validated on the test peptides. Results: The N3 model turned out to be superior to the kNN model when the classification focused on identifying the most active peptides.