TCNeKP:一种用于预测酶催化活动的新型深度学习架构
Yuanyuan Lei1,2, Rui Liu2, Hanxi Yu2
1Key Laboratory of Biorheological Science and Technology (Ministry of Education), College of Bioengineering, Chongqing University, Chongqing 400044, China.
Journal of chemical information and modeling
|October 15, 2025
概括
新的TCNeKP模型准确地预测了酶动力学参数 (Kcat和Km) 用于酶工程. 这些模型的性能优于现有方法,增强了酶设计和催化剂研究.
科学领域:
- 生物化学 生化学
- 计算生物学 计算生物学
- 酶动力学 酶动力学
背景情况:
- 精确预测酶动力学参数,包括Kcat (催化周转率) 和Km (迈克利斯常数),对于酶的理性设计和工程至关重要.
- 现有的计算模型面临的挑战是有效地整合各种数据类型,如酶序列,基质信息和反应条件.
研究的目的:
- 开发新的计算模型,TCNeKP,用于预测酶运动参数 (Kcat和Km).
- 为了提高各种酶类和基质的野生类型和突变酶的酶动态参数预测的准确性和稳定性.
主要方法:
- 酶序列被自动嵌入并使用时间卷积网络 (TCN) 进行处理以提取特征.
- 基质使用预训练的SMILES-Transformer语言模型进行编码,并通过辐射基函数 (RBF) 编码催化条件 (pH,温度).
- 一个完全连接的网络整合了这些功能,用于单任务预测,并开发了一个多任务TCNeKP模型,具有跨任务动态参数共享模块和注意力机制.
主要成果:
- 在7个EC类中,TCNeKP模型在预测Kcat和Km方面表现强,优于MPEK,UniKP和DLKcat等最先进的模型.
- 与基准模型相比,多任务TCNeKP模型实现了较高的R平方值 (0.677为Kcat,0.657为Km).
- 在Kcat和Km预测任务之间的协作学习显著改善了酶基质相互作用和催化作用的特征提取.
结论:
- 开发的TCNeKP模型为准确预测酶动力学参数提供了强大的工具.
- 多任务学习通过利用Kcat和Km预测之间的共享信息来提高预测性能.
- 这些发现有助于通过改进的计算预测来推进酶工程和合理的酶设计.
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