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A novel method for predicting antioxidant activity based on amino acid structure
•We examine the structural characteristics that produce potent antioxidants.•Amino acids were selected as a simplest-case model for antioxidant analysis.•We try to predict antioxidant activity of non-amino acid compounds.•Antioxidant activity predictions are made based on amino acid antioxidant data...
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Published in: | Food chemistry 2014-09, Vol.158, p.490-496 |
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Main Authors: | , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | •We examine the structural characteristics that produce potent antioxidants.•Amino acids were selected as a simplest-case model for antioxidant analysis.•We try to predict antioxidant activity of non-amino acid compounds.•Antioxidant activity predictions are made based on amino acid antioxidant data.•sp2-Hybridized carbon number is the most consistent predictor of antioxidant activity.
Epidemiological studies show a positive correlation between oxidative stress and chronic disease development such as heart disease and cancer. While several antioxidant compounds with varying physical and chemical characteristics are able to reduce oxidative stress in biological systems, relatively few studies have been performed to examine the structural characteristics that produce potent antioxidants. We examined 20 essential and non-essential amino acids using the ORAC assay and used a simplest-case amino acid model to gather data to make predictions regarding the antioxidant activity of non-amino acid compounds; we also tested our findings on chalcone and nitrone data from the current literature. We observed that the sp2-hybridized carbons were the most consistent predictors of antioxidant activity in all groups. Valence electron to carbon ratio and length of conjugated double bond groups also emerged as important structural characteristics. Further testing may help to elucidate more accurate trends, as well as nonlinear relationships. |
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ISSN: | 0308-8146 1873-7072 |
DOI: | 10.1016/j.foodchem.2014.02.102 |