Quantitative analysis of the relationship between structure and antioxidant activity of tripeptides

Abstract

Funding information Kewpie Corporation, JapanPeptides from enzymatic hydrolysates of food proteins exhibit significant antioxidant activity. Several studies have attempted to determine the factors contributing to the antioxidant activity of peptides; however, the physicochemical properties and factors essential for the antioxidant activity of peptides are still unclear. In this study, in order to clarify the factors important for peptide antioxidant activity based on the properties of component amino acids, 55 tripeptides were synthesized from 20 natural amino acids and their antioxidant activity was measured using the Trolox equivalent antioxidant capacity (TEAC) assay system. The tripeptides were divided into two data sets: a training set comprising 50 compounds and a validated set comprising five compounds. The structure-activity relationship of the training set was then analyzed using classical quantitative structure-activity relationship (QSAR) analysis. The study findings demonstrate that the presence of a cysteine residue at any position, an aromatic amino acid at the C-terminus, higher hydrophobicity of the N-terminal residue, and smaller HOMO-LUMO energy gap of the middle residue can significantly enhance the antioxidant activity. The activities of the five validated compounds were predicted using the constructed QSAR model, and a good correlation between measured and predicted activities was observed. The information obtained from the QSAR model could be useful for effective production of antioxidant peptides from food proteins such as egg white proteins.

A Novel Insight into Screening for Antioxidant Peptides from Hazelnut Protein: Based on the Properties of Amino Acid Residues

“…Experiments conducted by Wu et al also indicated that E HOMO and E-gap were feasible to describe the antioxidant behaviors of a set of man-made peptides, which were designed from the parent peptide “PMRGGGGYHY” [ 41 ]. Consistent with other studies reported [ 44 ], the presence of active residues Tyr, Trp, or Met as well as high E HOMO and low E-gap should be the characteristics of a peptide responsible for inhibiting the oxidation of linoleic acid. Amino acids, Tyr and Trp, act as active sites were also confirmed by Molecular docking.…”

Comprehensive Evaluation and Comparison of Machine Learning Methods in QSAR Modeling of Antioxidant Tripeptides

“…This implies that some features of amino acids can contribute to antioxidant activity at any position, even though their importance varied with positions. The theoretical conclusion derived from the selected features was also supported by the study of Uno et al 31 …”

“…Based on the variable importance values, almost all the feature selection methods showed that the C-terminal residues played the most important role in antioxidant activity, while the central amino acid contributed the least to the activity, which was partly consistent with previous results from wet-chemistry and QSAR studies, where there was no comparison between N- and C- terminals. 12 , 24 , 31 Previous studies were confined to amino acid physicochemical properties (with about 195 indices) or the AADs which could not take full advantage of all the amino acid indices to identify the most representative indices to characterize tripeptides. 8 , 11 , 32 Although some of the selected features, especially non-physicochemical properties (e.g., LIFS790103 stands for “Conformational preference for antiparallel beta-strands”), might be difficult to understand and explain, these selected features are much targeted and less redundant.…”

“…This also explained the poor model performance in most previous studies which were based on linear regression methods. 11 , 31 , 32 In addition, some studies subjectively removed the non-active tripeptides from the data set in order to improve the model fitting, which resulted in misleading models. 8 , 32 Further, improper data set division between the training data set and test data set increased the bias in the model and undermined the robustness of the models, 8 while model evaluation without the test data set was not complete because the performance in cross-validation could not represent the real performance of the model in the unknown data set.…”

Machine learning screening of bile acid-binding peptides in a peptide database derived from food proteins

“…In order to predict the bioactivity of peptides, quantitative analysis of the relationship between the structure and bioactivity of peptides has received much interest from many physical biochemists. In a recent study 25 , the hydrophobicity of the amino acid located at the N-terminal end was reported to be more hydrophilic than that of the same amino acid located at both the middle and C-terminal ends. Therefore, it is likely that 4-mer peptides are more hydrophilic than longer peptides, such as 5-, 6-, and 7-mer peptides.…”