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使用可解释的人工神经网络模型分析拉斯伯里多糖的结构-活性关系.

Jie Lu1, Yongjing Yang1, Eun-Kyung Hong2

  • 1School of Ecological and Environmental Engineering, Qinghai University, Xining 810016, China.

International journal of biological macromolecules
|February 25, 2024
PubMed
概括

研究人员利用人工神经网络 (ANN) 探索了拉斯伯里多糖的结构-活性关系. 关键的结构特征,如分子量和特定的糖含量显著影响免疫增强活性.

关键词:
可解释的人工神经网络拉斯伯里多糖是什么意思 拉斯伯里多糖是什么意思结构与活动的关系.

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科学领域:

  • 碳水化合物化学 碳水化合物化学
  • 免疫学 免疫学 免疫学
  • 计算生物学 计算生物学

背景情况:

  • 多糖的结构-活性关系 (SAR) 对于理解它们的生物功能至关重要.
  • 拉斯伯雷果汁是生物活性多糖的来源,具有潜在的增强免疫力的特性.
  • 预测建模和可解释性方法可以阐明复杂的SARs.

研究的目的:

  • 为了研究多糖的SAR及其碎片从树纸.
  • 开发和验证一个人工神经网络 (ANN) 模型,以基于多糖结构来预测增强免疫活性.
  • 使用梯度加权类激活映射 (Grad-CAM) 识别免疫增强活动的关键结构决定因素.

主要方法:

  • 提取和净化六种多糖和三种多糖碎片从拉斯伯里果粉.
  • 开发一个ANN模型,使用结构信息作为输入和免疫活动作为输出.
  • 应用Grad-CAM进行可解释性分析以确定关键结构特征.

主要成果:

  • 该ANN模型显示了强大的趋同,培训和测试错误很低 (MSE ~0.003-0.013,MAPE ~0.21%-0.98%).
  • 解释性分析显示,分子重量,阿拉比诺斯和银河糖含量,银河酸含量和特定的甘氨酸链接 (例如, →3) -Arap-(1→,Araf-(1→, →4) -Galp-(1→) 是免疫增强活性的主要驱动因素.
  • 格拉德-CAM算法成功地突出了对活动最有影响力的结构特征.

结论:

  • 人工神经网络有效地模拟了树多糖的SAR.
  • 特定的结构属性,包括糖的组成和结合模式,对于这些多糖的免疫增强作用至关重要.
  • 这项研究提供了一种新的计算方法来理解多糖SAR,可能引导开发新的免疫调节剂.