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

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 (R²_train > 0.86, Q²_train > 0.70) and relatively good predictability (R²_test > 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.

SUBMITTER: Chen N

PROVIDER: S-EPMC6100293 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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

Chen Nan N Chen Ji J Yao Bo B Li Zhengguo Z

Molecules (Basel, Switzerland) 20180610 6

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 mode ...[more]

PMID: 29890782

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Project description:Due to their multiple beneficial effects, antioxidant peptides have attracted increasing interest. Currently, the screening and identification of bioactive peptides, including antioxidative peptides based on wet-chemistry methods are time-consuming and highly rely on many advanced instruments and trained personnel. Quantitative structure-activity relationship (QSAR) analysis as an in silico method can be more efficient and cost-effective. However, model performance of QSAR studies on antioxidant peptides was still poor due to limited attempts in model development approaches. The objective of this study was to compare popular machine learning methods for antioxidant activity modeling and screening of tripeptides and identify the critical amino acid features that determine the antioxidant activity. 533 numerical indices of amino acids were adopted to characterize 130 tripeptides with known antioxidant activity from the published literature, and then 7 feature selection strategies plus pairwise correlation were used to screen the most important indices for antioxidant activity and model building. 14 machine learning methods were used to build models based on the feature selection strategies, respectively. Among the 98 models, non-linear regression methods tended to perform better, and the best model with an R 2 Test of 0.847 and RMSETest of 0.627 for tripeptide antioxidants was obtained by combining random forest for feature selection and tree-based extreme gradient boost regression for model development. Based on the predicted antioxidant values of 7870 unknown tripeptides, potentially high antioxidant activity tripeptides all have a tyrosine, tryptophan, or cysteine residue at the C-terminal position. Furthermore, the predicted antioxidant activity of six synthesized tripeptides was confirmed through experimental determination, and for the first time, the cysteine or tyrosine residue at the C-terminal was found to be critical to the antioxidant activity based on both QSAR models and experimental observations.

Dataset Information

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

Publications

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

Similar Datasets

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