CPI-Pred:一种深度学习框架,用于预测化合物-蛋白相互作用的功能参数
Zhiqing Xu1, Rana Ahmed Barghout1, Jinghao Wu1
1Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada.
bioRxiv : the preprint server for biology
|February 3, 2025
概括
深度学习模型CPI-Pred使用酶和化合物序列准确预测化合物-蛋白相互作用 (CPI). 这通过克服数据限制,推进了酶发现和代谢工程.
科学领域:
- 计算生物学 计算生物学
- 生物化学 生物化学
- 机器学习 机器学习
背景情况:
- 深度学习推进了基因组注释和酶发现.
- 由于数据的复杂性和稀疏性,预测化合物-蛋白相互作用 (CPI) 是一个挑战.
- 准确的CPI预测对于理解酶功能和调节至关重要.
研究的目的:
- 介绍CPI-Pred,这是一个通用的深度学习模型,用于预测化合物-蛋白质相互作用功能.
- 利用新的深度学习架构来增强CPI预测.
- 编制最大的酶动力学参数数据集,用于培训和评估.
主要方法:
- CPI-Pred集成了来自消息传递神经网络的化合物表示和来自蛋白质语言模型的酶表示.
- 使用创新的序列聚合和交叉注意力机制.
- 在一个全面的酶动力学参数数据集上进行训练和评估 (KM,kcat,kcat/KM,KI).
主要成果:
- CPI-Pred只使用酶氨基酸序列和化合物结构表示,准确地预测各种CPI类型.
- 在未见的化合物和结构不相似的酶上表现优于最先进的模型.
- 证明了模型在广泛的交互中对广泛的交互的概括能力.
结论:
- 对于代谢工程挑战,CPI-Pred提供了一种有价值的工具,包括新的酶和化合物设计.
- 精心策划的数据集作为机器学习模型在酶活性和乱交预测中的基准.
- 促进新酶和代谢物的发现,在各种生物环境中具有潜在的应用.
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