QSAR Study on Antioxidant Tripeptides and the Antioxidant Activity of the Designed Tripeptides in Free Radical Systems.

Nan Chen,Ji Chen,Zhengguo Li,Bo Yao

doi:10.3390/molecules23061407

Abstract

In this study, quantitative structure-activity relationship (QSAR) models were determined based on 91 antioxidant tripeptides. We firstly adopted the stepwise regression (SWR) method for selecting key variables without autocorrelation and then utilized multiple linear regression (MLR), support vector machine (SVM), random forest (RF), and partial least square regression (PLS) to develop predictive QSAR models based on the screened variables. The results demonstrated that all the established models have good reliability (R2train > 0.86, Q2train > 0.70) and relatively good predictability (R2test > 0.88). The contribution of amino acid residues was calculated from the stepwise regression combined with multiple linear regression (SWR-MLR) method model that shows Trp, Tyr, or Cys at C-terminus is favorable for antioxidant activity of tripeptides. Nineteen antioxidant tripeptides were designed based on SWR-MLR models, and the antioxidant activity of these tripeptides were evaluated using three antioxidant assays in free radical systems (1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging capacity, trolox equivalent antioxidant capacity assay, and the ferric reducing antioxidant power assay). The experimental antioxidant activities of these tripeptides were higher than the calculated/predicted activity values of the QSAR models. The QSAR models established can be used to identify and screen novel antioxidant tripeptides with high activity.

Highlights

Reactive oxygen species (ROS), such as hydroxyl radicals, superoxide anions, and hydrogen peroxide can cause oxidative damages to the cellular biomolecules and lipid peroxidation in food processing when ROS are generated in excess or are not eliminated after formation [1,2,3]
Li et al used the partial least square (PLS) method to construct a quantitative structure-activity relationships (QSAR) model of antioxidant tripeptides with six sets of amino acid descriptors; they concluded that divided physicochemical property score (DPPS) descriptors as the amino acid descriptors are better than hydrophobic, electronic, steric, and hydrogen (HESH), vectors of hydrophobic, steric, and electronic properties (VHSE), molecular surface weighted holistic invariant molecular (MS-WHIM), isotropic surface area–electronic charge index (ISA-ECI), and Z-scale
The key variables were screened by the stepwise regression (SWR), and their autocorrelations were validated by statistical methods

Summary

Introduction

Reactive oxygen species (ROS), such as hydroxyl radicals, superoxide anions, and hydrogen peroxide can cause oxidative damages to the cellular biomolecules and lipid peroxidation in food processing when ROS are generated in excess or are not eliminated after formation [1,2,3]. The antioxidant peptides protect cells against oxidative stress by scavenging intracellular ROS [6], quenching free radicals [7], chelating transition metals [8,9] directly, and improving activities of intracellular antioxidant enzymes, such as superoxide dismutase (SOD), catalase (CAT), glutathione reductase (GR), glutathione peroxidase (GP), and so on [10,11,12]. Researchers generally considered that some reducing amino acid residues in the peptides, such as His, Met, Tyr, Cys, or Trp can directly scavenge. Saito et al believed that tripeptides either containing Trp or Tyr residues at the C-terminus had strong radical scavenging activities [18]. Li et al used the partial least square (PLS) method to construct a QSAR model of antioxidant tripeptides with six sets of amino acid descriptors; they concluded that divided physicochemical property score (DPPS) descriptors as the amino acid descriptors are better than hydrophobic, electronic, steric, and hydrogen (HESH), vectors of hydrophobic, steric, and electronic properties (VHSE), molecular surface weighted holistic invariant molecular (MS-WHIM), isotropic surface area–electronic charge index (ISA-ECI), and Z-scale

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Methods

Results

Conclusion