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Article

Quantitative Structure-Activity Relationship Study of Antioxidant Tripeptides Based on Model Population Analysis

1
Guangdong Provincial Key Laboratory of Animal Nutrition Control, National Engineering Research Center for Breeding Swine Industry, Subtropical Institute of Animal Nutrition and Feed, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
2
Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410083, China
3
State Key Laboratory of Food Science and Technology, International Joint Laboratory on Food Safety, Jiangnan University, Wuxi 214122, China
4
Yunnan Food Safety Research Institute, Kunming University of Science and Technology, Kunming 650500, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Int. J. Mol. Sci. 2019, 20(4), 995; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms20040995
Received: 1 January 2019 / Revised: 13 February 2019 / Accepted: 18 February 2019 / Published: 25 February 2019
(This article belongs to the Special Issue QSAR and Chemoinformatics Tools for Modeling)
Due to their beneficial effects on human health, antioxidant peptides have attracted much attention from researchers. However, the structure-activity relationships of antioxidant peptides have not been fully understood. In this paper, quantitative structure-activity relationships (QSAR) models were built on two datasets, i.e., the ferric thiocyanate (FTC) dataset and ferric-reducing antioxidant power (FRAP) dataset, containing 214 and 172 unique antioxidant tripeptides, respectively. Sixteen amino acid descriptors were used and model population analysis (MPA) was then applied to improve the QSAR models for better prediction performance. The results showed that, by applying MPA, the cross-validated coefficient of determination (Q2) was increased from 0.6170 to 0.7471 for the FTC dataset and from 0.4878 to 0.6088 for the FRAP dataset, respectively. These findings indicate that the integration of different amino acid descriptors provide additional information for model building and MPA can efficiently extract the information for better prediction performance. View Full-Text
Keywords: quantitative structure-activity relationship; QSAR; antioxidant tripeptides; model population analysis; amino acid descriptors quantitative structure-activity relationship; QSAR; antioxidant tripeptides; model population analysis; amino acid descriptors
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MDPI and ACS Style

Deng, B.; Long, H.; Tang, T.; Ni, X.; Chen, J.; Yang, G.; Zhang, F.; Cao, R.; Cao, D.; Zeng, M.; Yi, L. Quantitative Structure-Activity Relationship Study of Antioxidant Tripeptides Based on Model Population Analysis. Int. J. Mol. Sci. 2019, 20, 995. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms20040995

AMA Style

Deng B, Long H, Tang T, Ni X, Chen J, Yang G, Zhang F, Cao R, Cao D, Zeng M, Yi L. Quantitative Structure-Activity Relationship Study of Antioxidant Tripeptides Based on Model Population Analysis. International Journal of Molecular Sciences. 2019; 20(4):995. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms20040995

Chicago/Turabian Style

Deng, Baichuan, Hongrong Long, Tianyue Tang, Xiaojun Ni, Jialuo Chen, Guangming Yang, Fan Zhang, Ruihua Cao, Dongsheng Cao, Maomao Zeng, and Lunzhao Yi. 2019. "Quantitative Structure-Activity Relationship Study of Antioxidant Tripeptides Based on Model Population Analysis" International Journal of Molecular Sciences 20, no. 4: 995. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms20040995

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