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解码多蛋白相互作用与深度学习:从分子机制到食品应用

Qiang Liu¹, Tiantian Wang¹, Binbin Nian²

¹College of Food Science and Engineering, Nanjing University of Finance and Economics/ Collaborative Innovation Center for Modern Grain Circulation and Safety, Nanjing, 210023, Jiangsu Province, PR China.

Biotechnology advances

|January 16, 2026

概括

深度学习 (DL) 推进了对多 - 蛋白相互作用 (PhPIs) 的研究,这些相互作用对营养素的生物可用性和健康至关重要. DL提高了预测准确性,但需要更多高质量的数据,特别是对于天然产品,以加强营养科学和治疗开发.

科学领域:

生物化学生物化学
计算生物学计算生物学
食品科学食品科学食品科学

背景情况:

多蛋白相互作用 (PhPIs) 对食物功能,营养物质生物可用性,抗氧化活性和治疗疗效至关重要.
由于多的结构多样性和蛋白质结合的动态性质,研究PhPIs是复杂的.
传统的实验 (NMR,MS) 和计算 (对接,MD) 方法提供了洞察力,但在可扩展性,吞吐量和可重现性方面存在局限性.

研究的目的:

审查深度学习 (DL) 在研究PhPIs中的应用.
探索DL如何使用生物和化学信息学数据有效预测结合点,相互作用亲和关系和分子动态.
为了确定目前的局限性和未来的方向在PhPIs研究中DL.

主要方法:

对现有DL应用在PhPI分析中的文献的审查.
对DL框架进行评估,以预测结合点,亲缘关系和分子动态.
对数据要求和模型通用性的批判性评估.

主要成果:

DL显著提高了预测准确度,并减少了PhPI研究中的实验冗余.
DL利用高维生物和化学信息数据进行高效的分析.
目前DL的有效性受到数据的可用性,质量和代表性,特别是天然产品的限制.

结论:

DL是理解PhPI的变革性工具,加速营养科学和治疗开发方面的发现.
未来的研究应该专注于多式联运数据集成,提高模型通用性,并创建特定领域的基准数据集.
解决数据的局限性对于释放PhPI研究中DL的全部潜力至关重要.

关键词:

人工智能的人工智能是人工智能.深度学习是一种深度学习.机器学习是机器学习.聚醇与蛋白质的相互作用蛋白质的设计蛋白质的设计蛋白质相互作用的相互作用

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