在蛋白质结构预测中使用人工智能驱动的深度学习技术
Lingtao Chen1, Qiaomu Li1, Kazi Fahim Ahmad Nasif1
1College of Computing and Software Engineering, Kennesaw State University, Marietta, GA 30060, USA.
International journal of molecular sciences
|August 10, 2024
概括
这篇评论探讨了计算式蛋白质结构预测,强调了从传统方法到尖端人工智能 (AI) 模型 (如AlphaFold) 的进步. 它评估了AI.
科学领域:
- 计算生物学是一种计算生物学.
- 结构生物信息学 结构生物信息学
- 生命科学中的人工智能
背景情况:
- 蛋白质结构预测对于理解蛋白质的功能和行为至关重要.
- 传统的方法包括同质模型,ab initio模型和线程.
- 这一领域随着人工智能 (AI) 的整合而迅速发展.
研究的目的:
- 为蛋白质结构预测提供计算模型的全面审查.
- 涵盖从已建立的蛋白质建模技术到最先进的AI框架的演变.
- 讨论这些模型的性能和应用.
主要方法:
- 审查已建立的蛋白质建模技术 (同质,初始,线程).
- 基于深度学习的人工智能模型的分析 (AlphaFold,RoseTTAFold,ProteinBERT).
- 使用CASP和CAMEO排名和指标 (TM-score,GDT_TS,lDDT) 的模型性能比较.
主要成果:
- 人工智能模型,特别是深度学习方法,显著提高了蛋白质结构预测的准确性.
- 已建立的框架已经整合了人工智能技术,提高了他们的能力.
- 性能比较显示,通过CASP和CAMEO评估,预测准确度取得了实质性的进展.
结论:
- 人工智能已经彻底改变了蛋白质结构预测,实现了前所未有的准确性.
- 在预测动态蛋白质行为,结构变化和相互作用方面仍然存在挑战.
- 未来的研究方向包括探索这些复杂的动态,并扩大药物设计和开发中的应用.
相关概念视频
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