QSAR Study of Di and Tri-Peptides Containing Cysteine as Antioxidants Agents

LUCAS A. GARRO; MATIAS F. ANDRADA; ESTEBAN G. VEGA HISSI; JUAN C. GARRO MARTINEZ; SONIA E. BARBERIS

San Juan

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

Sociedad de Biología de Cuyo (SBC)

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 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 other aspects 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 di-peptides models (R2train=0.947 and R2test=0.804) and for tri-peptide models (R2train=0.863 and R2test=0.789) indicate the high predictive capacity of the generated mathematical models. Thus, these simple QSAR models could be used to predict the antioxidant activities of new di and tri-peptides.