机器学习模型的进步用于预测酶动力学参数
Ali Malli1, Denys Vasyutyn1, Jin Ryoun Kim1
1Department of Chemical and Biomolecular Engineering, New York University, 6 MetroTech Center, Brooklyn, New York 11201, United States.
Journal of chemical information and modeling
|December 17, 2025
概括
机器学习模型现在可以预测酶动力学参数,这对酶工程和合成生物学至关重要. 全球和本地模型的进步提供了强大的工具,尽管存在数据稀缺的挑战.
科学领域:
- 生物化学 生化学
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- 酶动力学参数 (kcat,Km,kcat/Km,Ki) 对酶工程,代谢建模和合成生物学至关重要.
- 实验性确定是昂贵和耗时的;传统的计算方法是不够的.
- 机器学习 (ML) 模型为这些参数的in silico预测提供了一个有希望的替代方案.
研究的目的:
- 审查基于ML的酶动力学参数预测的最新进展.
- 要突出当前ML模型的应用和局限性.
- 概述改善ML预测的未来机会.
主要方法:
- 全球ML模型的审查,这些模型在不同类型的酶上受过训练.
- 针对特定酶家族量身定制的本地ML模型的审查.
- 讨论ML模型在突变效应预测,酶挖矿和代谢建模中的应用.
主要成果:
- 全球和本地ML模型已经在预测酶动力学参数方面取得了成功.
- 这些模型有助于各种应用,包括蛋白质工程和系统生物学.
- 数据稀缺是主要的限制,影响模型性能和范围.
结论:
- 机器学习提供了一种强大的方法来预测酶动力学参数,加速了酶工程和合成生物学方面的研究.
- 通过高通量数据生成和半监督学习克服数据稀缺性是未来进步的关键.
- 精确的基于ML的预测可以为所需的功能提供更好的蛋白质序列注释.
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