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| Analysis of the Effect of Different Reducing Sugars on Ara h2 Glycation Based on Spectral Technology |
| YANG Ping1, LI Xue2, WANG Hui1, LIU Guang-xian2* |
1. State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang 330047, China
2. Institute of Agricultural Processing, Jiangxi Academy of Agricultural Sciences, Nanchang 330200, China
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Abstract Glycation reaction can induce the structural change of protein in food stuff; Ara h2 is one of the main proteins in peanuts, and it can be used as a model protein to study the structural change of the glycation products of peanut protein. However, the effects of different reducing sugars on Ara h2 glycation have not been reported. Therefore, this article took Ara h2 as the research object to study the changes in the molecular weight, the secondary and tertiary structure and the functional groups of Ara h2 before and after glycation by SDS-PAGE, endogenous fluorescence, synchronous fluorescence, ultraviolet spectrum, circular dichroism, Fourier transform infrared spectroscopy and other spectroscopic techniques. The effects of six reducing sugars (ribose, xylose, galactose, glucose, fructose and lactose) on Ara h2 were analyzed to clarify the structural change of different Ara h2 glycation products. The results of SDS-PAGE showed that these electrophoretic bands of Ara h2 modified by xylose and ribose moved up significantly, and their glycation degree was the largest, compared with other reducing sugars. Ultraviolet spectrum analysis showed that glycation reaction would change the absorption peak intensity of Ara h2, and modification with pentoses had the strongest absorption intensity (absorption peak intensity of xylose was the largest). The results of endogenous fluorescence, synchronous fluorescence and three-dimensional spectral scanning showed that glycation reduced the fluorescence intensity of Ara h2 and pentose modified Ara h2 had the lowest fluorescence intensity. It might be due to the structural unfolding of Ara h2 caused by glycation, which exposes aromatic amino acids to the water environment and leads to fluorescence quenching. Circular dichroism analysis showed that the content of α-helix increased after Ara h2 was modified by different reducing sugars, among which modified by xylose showed the highest helix content (15.6%). Fourier transform infrared spectroscopy showed that the absorption peaks of Ara h2 (modified by xylose and ribose) shifted from 3 327.41 to 3 318.43 and 3 321.09 cm-1, respectively; At 1 700~1 600 cm-1, the absorption peak intensity of Ara h2 modified by xylose and ribose was slightly higher than that modified by other reducing sugars. Therefore, different reducing sugars have different effects on the structure of Ara h2 glycation products; The shorter carbon chain and the less steric hindrance of reducing sugars led to a higher glycation degree and a greater impact on the structure of Ara h2.
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Received: 2022-02-13
Accepted: 2022-06-13
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Corresponding Authors:
LIU Guang-xian
E-mail: liugx178@163.com
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[1] Clarke R E, Dordevic A L, Tan S M, et al. Nutrients, 2016, 8(3): 125.
[2] Xu Y, Zhao X, Bian G, et al. LWT-Food Science and Technology, 2018, 95: 209.
[3] Chen F, Huang G. European Journal of Medicinal Chemistry, 2019, 182: 111612.
[4] MAO Ji-hua, WANG Hui, TU Zong-cai, et al(毛积华，王 辉，涂总财，等). Food & Machinery(食品与机械), 2020, 36(6): 11.
[5] Yuan F, Lv L, Li Z, et al. Food Chemistry, 2017, 219: 215.
[6] Toomer O T. Critical Reviews in Food Science and Nutrition, 2018, 58(17): 3042.
[7] Mingrou L, Guo S, Ho C T, et al. Journal of Food Biochemistry, 2022, 46(7): e14119.
[8] Bonku R, Yu J. Food Science and Human Wellness, 2020, 9(1): 21.
[9] Chang X, Zhou X, Tang Y, et al. Journal of Agricultural and Food Chemistry, 2022, 70: 626.
[10] Pi X W, Wan Y, Yang Y, et al. Trends in Food Science & Technology, 2019, 93: 212.
[11] Senthilkumaran A, Babaei-Ghazvini A, Nickerson M T, et al. Polymers, 2022, 14(5): 1065.
[12] Zhang Q, Li L, Lan Q, et al. Critical Reviews in Food Science and Nutrition, 2019, 59(15): 2506.
[13] Chang X, Zhou X, Tang Y, et al. Journal of Agricultural and Food Chemistry, 2022, 70：626.
[14] Tang C H, Sun X, Foegeding E A. Journal of Agricultural and Food Chemistry, 2011, 59(18): 10114.
[15] TIAN Yang, RAO Huan, TAO Sha, et al(田 阳，饶 欢，陶 莎，等). Cereals & Oils(粮食与油脂), 2016, 29(12): 29.
[16] Divsalar A, Saboury A A, Ahmad F, et al. Journal of the Brazilian Chemical Society, 2009, 20(10): 245.
[17] Vissers Y M, Blanc F, Skov P S, et al. PLOS ONE, 2011, 6(8): e23998.
[18] Jing H, Kitts D D. Archives of Biochemistry and Biophysics, 2004, 429(2): 154.
[19] Antosiewicz J M, Shugar D. Biophysical Reviews, 2016, 8(2): 163.
[20] Huang X Q, Tu Z C, Xiao H, et al. Food Research International, 2012, 48(2): 866.
[21] Zhang L, Lu Y, Ye Y, et al. Journal of Agricultural and Food Chemistry, 2018, 67(1): 236.
[22] Liu Y, Zhao G, Zhao M, et al. Food Chemistry, 2012, 131(3): 901.
[23] Yang W, Tu Z, Wang H, et al. Journal of Agricultural and Food Chemistry, 2017, 65(36): 8018.
[24] Savadkoohi S, Bannikova A, Mantri N, et al. Food Hydrocolloids, 2016, 53: 104.
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