使用机器学习和深度学习进行一维蛋白质结构预测的进展
Wafa Alanazi1,2, Di Meng1, Gianluca Pollastri1
1School of Computer Science, University College Dublin, Belfield, Dublin D04 C1P1, Ireland.
Computational and structural biotechnology journal
|April 17, 2025
概括
机器学习和深度学习,包括蛋白质语言模型,正在彻底改变1D蛋白质结构预测. 这些先进的方法显著提高了理解蛋白质序列结构关系和功能的准确性.
科学领域:
- 结构生物信息学 结构生物信息学
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- 准确的蛋白质结构预测对于理解蛋白质功能至关重要.
- 传统方法在预测1D蛋白质结构注释方面存在局限性.
- 机器学习 (ML) 和深度学习 (DL) 提供先进的解决方案.
研究的目的:
- 审查ML和DL在1D蛋白质结构预测中的演变.
- 要突出像AlphaFold和蛋白质语言模型 (PLMs) 这样的关键进步.
- 讨论该领域的挑战和未来趋势.
主要方法:
- 从早期的ML到现代DL框架的预测方法的审查.
- 序列嵌入和预训练语言模型的集成.
- 专业数据集的探索和对比测试竞赛.
主要成果:
- DL框架和PLM在1D蛋白质预测方面取得了前所未有的准确性.
- AlphaFold显著影响了蛋白质结构预测.
- 多模式集成增强了预测模型的能力.
结论:
- ML,DL和PLM已经改变了1D蛋白质预测.
- 解决数据质量,可扩展性和可解释性方面的挑战是关键.
- 该领域正在迅速发展,有前途的未来方向.
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