预测金属蛋白结合点中的人工智能:桥梁生物信息学和生物技术的系统性审查
Fereshteh Noroozi Tiyoula1, Fatemeh Vafaee Sharbaf1, Karim Rahimian1
1Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Iran.
International journal of biological macromolecules
|August 30, 2025
概括
机器学习可以准确地预测金属蛋白中的金属结合点. 这项审查指导研究人员选择人工智能模型来推进生物技术和制药创新.
科学领域:
- 生物化学和生物信息学
- 计算生物学
- 蛋白质工程
背景情况:
- 金属蛋白对生物功能至关重要,对生物技术和制药进步至关重要.
- 传统的预测方法面临金属蛋白复杂性的挑战,需要先进的计算方法.
- 机器学习 (ML) 和深度学习 (DL) 为分析复杂的生物数据提供了强大的解决方案.
研究的目的:
- 系统地审查和评估机器学习和深度学习模型,用于预测金属蛋白中的金属结合点.
- 确定当前人工智能驱动的预测方法的优点和局限性.
- 根据数据特征和研究目标提出选择适当模型的框架.
主要方法:
- 系统性文献审查遵循系统性审查的首选报告项目 (PRISMA) 和元分析指南.
- 基于序列和基于结构的机器学习模型的分析,包括随机森林和卷积神经网络 (CNN).
- 评估模型性能指标,并确定数据不平衡和异质性等挑战.
主要成果:
- 随机森林模型在基于序列的预测中达到高达99%的准确性.
- 卷积神经网络 (CNN) 在基于结构的预测中表现出高达96%的准确性.
- 发现的挑战包括数据不平衡,金属离子的不足以及结构异质性,限制了模型的通用性.
结论:
- 机器学习和深度学习模型对精确的金属蛋白金属结合部位预测具有显著的前景.
- 解决数据的局限性对于改善模型的通用性和实际应用至关重要.
- 一个结构化的决策框架可以指导研究人员有效地应用人工智能用于金属蛋白研究和设计.
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