QSAR STUDY OF DI AND TRI-PEPTIDES CONTAINING CYSTEINE AS ANTIOXIDANTS AGENTS

GARRO, LUCAS; ANDRADA, MATIAS F.; VEGA-HISSI ESTEBAN; GARRO MARTINEZ, JUAN C.; BARBERIS, SONIA ESTHER

San Juan, Argentina

Encuentro; XLI Reunión Científica Anual de la Sociedad de Biología de Cuyo; 2023

Sociedad de Biología de Cuyo

Antioxidants agents play an essential role in the food industry improving the oxidative stability of food products. In the last years, the search for new natural antioxidants has increased due to the potentially high toxicity of chemical additives. Therefore, the synthesis and evaluation of the antioxidant activity in peptides is a field of current research.In this study, we performed a Quantitative Structure Activity Relationship analysis (QSAR) of 19 di-peptide and 19 tri-peptides containing cysteine to bring information on the relationship between the structure of peptides and their antioxidant activity. The QSAR is a mathematical-statistical hypothesis based on the fact that the biological activity of 75 a compound is a consequence of the molecular structure. We calculated 1D and 2D molecular descriptors using the PaDEL software, which provides information about the structure, shape, size, charge, polarity, solubility and otheraspects of the compounds. To develop different QSAR models for di and tri-peptides the whole set of peptides was divided into a training set (80%) and a test set (20%) through different techniques or algorithm as k-means clustering,based on the biological activity and by random selection. To select the best molecular descriptors we performed about 1300 multiple lineal regressions for di-peptides and 6650 for tri-peptides models. The statistic parameters for dipeptides models (R2 train=0.947 and R2 test=0.804) and for tri-peptide models (R2 train=0.863 and R2 test=0.789) indicate the high predictive capacity of the generated mathematical models. Thus, these simple QSAR models could be used to predictthe antioxidant activities of new di and tri-peptides.