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机器学习方法可以预测苦的TAS2R受体
Francesco Ferri1, Marco Cannariato1, Marco Agostino Deriu1
1PolitoBIOMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy.
Biotechnology and bioengineering
|April 8, 2024
概括
机器学习模型可以预测哪些苦分子准特定的味道受体2型 (TAS2R) 受体. 本综述分析了比特X,比特Sweet和比特Match等现有模型,以指导未来对苦-TAS2R相互作用的研究.
科学领域:
- * 感官科学和计算生物学.
- * 探索味觉受体研究和人工智能的交叉点.
背景情况:
- * 苦味的感知依赖于味道受体2型 (TAS2R) G蛋白合受体,涉及毒素检测,葡萄糖平衡和免疫反应.
- *人类TAS2Rs表现出多态性,其局部和功能各异,导致基于组织和连接体的多样化信号通路激活.
- * 在体外查TAS2R配体是资源密集型的,强调了需要高效的in silico预测方法.
研究的目的:
- * 提供基于的机器学习 (ML) 方法的概述,用于预测苦药-TAS2R相互作用.
- *对现有的ML模型 (BitterX,BitterSweet,BitterMatch) 进行批判性评估,以评估它们预测特定受体-连接体关联的能力.
- * 确定局限性,并为该领域的未来研究奠定基础.
主要方法:
- *对基于ML的最新模型进行文献综述和分析,以预测苦药-TAS2R相互作用.
- *专注于三个特定的ML模型:比特X (2016年),比特甜 (2019年) 和比特匹配 (2022年).
- *评估数据可用性和训练受体-连接体关联模型的挑战.
主要成果:
- *有许多基于ML的口味分类器存在,主要用于苦/不苦或苦/甜的分类.
- *由于数据稀缺和不完整,很少有ML模型专门预测哪些TAS2R受体是苦分子的目标.
- * 审查的模型 (BitterX,BitterSweet,BitterMatch) 代表了关键的进步,但也有局限性.
结论:
- *在实验研究中,对苦剂-TAS2R相互作用的in silico预测对于高效的配体和标选择至关重要.
- *现有的ML模型提供了有价值的工具,但需要进一步开发以克服数据限制.
- *本次审查为未来的研究奠定了基础,旨在提高苦-TAS2R预测模型的准确性和范围.
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