机器学习预测使用精选的酶特定数据集对芳香化合物的乳糖酶催化氧化
Yulia Kulagina1, Christian Goldhahn2, Ramon Weishaupt3
1WoodTec, Swiss Federal Laboratories for Materials Science and Technology (Empa), Dübendorf, Switzerland.
Journal of computational chemistry
|March 6, 2026
概括
机器学习模型预测了乳酶基质兼容性,加速了绿色化学生物催化剂的发现. 随机森林模型在识别酶氧化结果的关键分子特征方面表现稳定.
科学领域:
- 生物催化剂是一种生物催化剂.
- 绿色化学 绿色化学
- 酵素工程是什么意思 酵素工程
背景情况:
- 乳酶是多铜氧化酶酶,由于它们能够氧化各种基质,而水作为唯一的副产品,因此在绿色化学中被用作生物催化剂.
- 由于涉及酶结构,特性,氧化还原潜力和环境因素的复杂相互作用,预测乳酶基质兼容性是具有挑战性的.
研究的目的:
- 开发和评估机器学习模型,用于预测乳糖酶-基质相互作用.
- 简化实验工作流程,以确定适合生物催化剂的乳糖酶-基质组合.
主要方法:
- 应用四种经典机器学习分类器和基于变压器的模型 (ChemBERTa).
- 评估了三组精心策划的芳香基质数据集及其与特定乳酸酶的氧化概况.
- 对特征重要性 (随机森林) 和注意力机制 (ChemBERTa) 的分析,以确定氧化的主要分子决定因素.
主要成果:
- 机器学习模型在预测laccase-基质兼容性方面表现相似.
- 随机森林 (RFC) 在不同的数据分割和laccases中表现出卓越的稳定性.
- 特性重要性和注意力分析确定了影响氧化结果的关键分子特征.
- 开发了一个可通过将SMILES属性映射到分子图表上来可视化ChemBERTa预测的工具.
结论:
- 机器学习提供了一个强大的和可解释的框架,可以加速发现laccase-substrate对.
- RFC模型提供了稳定和可靠的预测,用于乳酸酶基质查.
- 了解分子特征有助于合理的酶工程和基质设计,以提高生物催化剂的性能.
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