在人工智能时代建模蛋白-蛋白相互作用的计算方法:当前状态和未来方向
Hao Li1, Chandran Nithin2, Sebastian Kmiecik2
1School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.
Drug discovery today
|May 21, 2025
概括
人工智能,特别是深度学习,现在在蛋白质与蛋白质相互作用 (PPI) 的建模方面处于领先地位. 人工智能工具为理解生物分子相互作用和设计新疗法提供了变革性的潜力.
科学领域:
- 结构生物学 结构生物学
- 计算生物学 计算生物学
- 人工智能在生物化学中的应用
背景情况:
- 蛋白与蛋白相互作用 (PPI) 是细胞过程的基础.
- 对PPI的准确建模对于理解生物功能和疾病至关重要.
- 传统的计算方法在预测复杂结构方面存在局限性.
研究的目的:
- 审查目前用于预测蛋白质复杂结构的计算环境.
- 突出人工智能 (AI) 对蛋白质-蛋白质相互作用建模的影响.
- 讨论该领域的挑战和最近的创新.
主要方法:
- 对传统的对接方法的调查.
- 专注于人工智能驱动方法的最新进展,包括深度学习和端到端框架.
- 讨论诸如蛋白质灵活性和内在无序区域 (IDRs) 等挑战.
主要成果:
- 人工智能驱动的方法,如AlphaFold,现在主导PPI结构预测.
- 关键的挑战包括对蛋白质灵活性,共同进化信号,大型组件和IDRs的建模.
- 创新重点是改进采样,整合实验数据,提高稳定性.
结论:
- 基于人工智能的工具正在彻底改变蛋白质复杂结构的预测.
- 经典方法仍然很重要,但人工智能提供了变革的潜力.
- 进步将加深对生物分子相互作用的理解,并加速治疗设计.
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