In silico identification of peptides with PPARγ antagonism in protein hydrolysate from rice (Oryza sativa) - Ruíz López, Felipe de Jesús; Espinosa Rodríguez, Bryan Alejandro; Silva Mares, David Arturo; González Martínez, Blanca Edelia; López Cabanillas Lomelí, Manuel; Méndez López, Luis Fernando; Vázquez Rodríguez, Jesús Alberto (Multidisciplinary Digital Publishing Institute, 2023)
At least half the population in industrialized countries suffers from obesity due to excessive accumulation of adipose tissue. Recently, rice (Oryza sativa) proteins have been considered valuable sources of bioactive peptides with antiadipogenic potential. In this study, the digestibility and bioaccessibility in vitro of a novel protein concentrate (NPC) from rice were determined through INFOGEST protocols. Furthermore, the presence of prolamin and glutelin was evaluated via SDSPAGE, and their potential digestibility and the bioactivity of ligands against peroxisome proliferatoractivated receptor gamma (PPARγ) were explored by BIOPEP UWM and HPEPDOCK. For the top candidates, molecular simulations were conducted using Autodock Vina to evaluate their binding affinity against the antiadipogenic region of PPARγ and their pharmacokinetics and drug-likeness using SwissADME. Simulating gastrointestinal digestion showed a recovery of 43.07% and 35.92% bioaccessibility. The protein banding patterns showed the presence of prolamin (57 kDa) and glutelin (12 kDa) as the predominant proteins in the NPC. The in silico hydrolysis predicts the presence of three and two peptide ligands in glutelin and prolamin fraction, respectively, with high affinity for PPARγ (≤160). Finally, the docking studies suggest that the prolamin-derived peptides QSPVF and QPY (−6.38 & −5.61 kcal/mol, respectively) have expected affinity and pharmacokinetic properties to act as potential PPARγ antagonists. Hence, according to our results, bioactive peptides resulting from NPC rice consumption might have an antiadipogenic effect via PPARγ interactions, but further experimentation and validation in suitable biological model systems are necessary to gain more insight and to provide evidence to support our in silico findings.