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在中发现和发酵香肠中乌玛米的感官验证:一项整合深度学习和分子建模的研究

Haochen Geng¹, Chunming Xu², Huijun Ma¹

¹School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China.

Foods (Basel, Switzerland)

|July 29, 2025

概括

研究人员结合了元基因组学和深度学习,在发酵香肠中发现了新的乌玛米. 三个经过验证的酸具有较低的值,显示出显著的乌玛米味道,提供了一个新的高通量功能性酸发现策略.

科学领域:

食品科学食品科学食品科学
生物技术是生物技术.
计算生物学计算生物学

背景情况:

功能性,特别是那些赋予乌玛米味道的,在食品应用中非常有价值.
发现功能性的传统方法往往耗时且效率低下.

研究的目的:

开发一种高吞吐量策略,使用元基因组学和深度学习来发现和验证发酵香肠中的新型乌玛米.
识别关键的氨基酸特征,有助于乌玛米的预测,并验证它们的感官特性.

主要方法:

来自发酵香肠的元基因组数据被用来生成一个候选库.
一个集成的深度学习模型,结合SHAP (SHapley添加式扩展) 进行解释性,被开发用于乌玛米的预测.
使用分子对接和分子动力学模拟来评估T1R1/T1R3.3的受体结合稳定性.
进行了感官评估,以确认umami特征,并确定已识别的的umami值.

主要成果:

该研究使用深度学习模型成功选了10种高概率的乌玛米.
三种 (DDSMAATGL,DGEEDASM,DEEEVDI) 显示出与乌玛米受体T1R1/T1R3.3最稳定的结合.
分子对接确定了关键受体残留物 (Glu301,Arg277,Lys328,His71) 对于键形成至关重要.
感官评估证实了三种选定的的显著乌玛米特征,低的乌玛米值在0.11至0.44毫克/毫升之间.

结论:

大基因组学和深度学习的整合为发现功能性提供了一种高效和高通量方法.
鉴定到的乌玛米表明,它有可能被用作食品中的味道增强剂.
这一策略促进了对受体相互作用的理解和功能性的发现.

关键词:

深度学习是一种深度学习.分子对接的分子对接.类预测的预测.乌玛米酸是一种酸.

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