关于深度学习方法和工具的调查,用于预测蛋白质结合部位
Alina Rohulia1, Yanli Wang2, Jianlin Cheng3
1Department of Chemical and Biomedical Engineering, University of Missouri, Columbia, MO, USA.
Methods in molecular biology (Clifton, N.J.)
|July 29, 2025
概括
深度学习 (DL) 显著提高了蛋白质结合部位预测的准确性. 本综述详细介绍了最先进的DL方法,指导研究人员开发人工智能驱动的生物相互作用分析工具.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 人工智能的人工智能
背景情况:
- 蛋白质与多种连接体相互作用,对细胞功能至关重要.
- 准确预测蛋白质结合部位对于理解生物相互作用至关重要.
- 深度学习 (DL) 已经成为增强绑定站点预测准确性的强大工具.
研究的目的:
- 为预测蛋白质结合部位提供最先进的深度学习方法的全面审查.
- 为开发和实施DL模型在这个领域提供详细的指南.
- 编译和评估最近的DL方法,包括模型类型,数据和指标.
主要方法:
- 编译和评估最近关于DL的文献,以预测蛋白质结合部位.
- 讨论数据收集,预处理和数据库的使用 (UniProt,Dockground,PDBbind).
- 探索著名的DL架构,如卷积神经网络 (CNN) 和图形神经网络 (GNN).
主要成果:
- 确定DL模型开发的关键细节:模型类型,输入/输出数据,数据库和评估指标.
- 展示各种数据库用于训练DL模型的实用性.
- 对CNN和GNN用于绑定站点预测的进步的概述.
结论:
- 深度学习方法在预测蛋白质结合部位方面取得了重大进展.
- 这篇评论为研究人员提供了DL技术和资源的知识,以推进人工智能驱动的预测.
- 通过准确的结合部位预测来了解蛋白质-连接体相互作用对于生物研究至关重要.
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