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在Pixian douban中基于机器学习的Umami的探索:从虚拟选,分子对接和翻译后修改中获得的见解

Sen Mei¹, Liangyu Zhang¹, Yajie Li¹

¹Food Microbiology Key Laboratory of Sichuan Province, Chongqing Key Laboratory of Speciality Food Co-Built by Sichuan and Chongqing, Xihua University, No.999 Guangchang Road, Chengdu 610039, China.

|March 9, 2025

概括

这项研究使用机器学习在Pixian Doubanjiang (PXDB) 中找到乌玛米. VEGGLR具有非常低的乌玛米值,通过受体相互作用增强了味道.

科学领域:

食品化学食品化学
分子美食学分子美食学
发酵科学发酵科学

背景情况:

毕生杜班江 (PXDB) 味道是关键,但其乌玛米的含量尚未得到充分了解.
在PXDB中,衰老过程和生物合成需要进一步研究.
了解乌玛米的结构和功能对于口味的发展至关重要.

研究的目的:

通过机器学习在老年PXDB中识别和描述umami.
阐明关键乌玛米的感觉机制和分子相互作用.
探索在PXDB衰老期间的乌玛米生物合成中的潜在调节途径.

主要方法:

在1-2岁的PXDB中用于标识的机器学习.
感官分析以确定已识别的的umami值.
分子对接用于研究受体相互作用 (T1R1/T1R3).
对特定蛋白质的翻译后修改 (PTM) 的分析.

主要成果:

鉴定了117种,预测69种具有乌玛米潜力.
VEGGLR显示出显著低的乌玛米值 (0.22 mmol/L).
分子对接揭示了VEGGLR通过盐桥和键与T1R1/T1R3结合.
在N6U2M1/N6UWT4.4上观察到的翻译后修饰 (酸化,乙化).

结论:

机器学习有效地识别了PXDB umami,包括强大的VEGGLR.
VEGGLR与T1R1/T1R3受体的相互作用解释了其强大的乌玛米贡献.
观察到的PTM表明了PXDB umami生物合成中的新型调节机制.
这项研究为老年PXDB.的风味化学提供了关键的分子见解.

关键词:

机器学习机器学习分子对接是分子对接.皮克西安·杜班 (Pixian Douban) 是一个古老的城市.乌玛米酸是乌玛米酸的一种.翻译后的修改翻译后的修改

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