使用AlphaFold高精度的蛋白质结构预测
John Jumper1, Richard Evans2, Alexander Pritzel2
1DeepMind, London, UK. jumper@deepmind.com.
Nature
|July 15, 2021
概括
一种新的计算方法AlphaFold现在可以准确地从氨基酸序列中预测蛋白质结构,即使没有已知的类似结构. 这一突破解决了结构生物信息学和蛋白质研究中的一个主要瓶.
科学领域:
- 结构生物学
- 计算生物学
- 生物信息学
背景情况:
- 了解蛋白质结构对于阐明蛋白质功能至关重要.
- 实验性结构确定耗时,限制已知的蛋白质序列的覆盖范围.
- 大规模的结构生物信息学需要精确的计算方法.
研究的目的:
- 开发一种计算方法以原子精度预测蛋白质结构.
- 克服现有的蛋白质结构预测方法的局限性,特别是对于没有同类结构的蛋白质.
- 实现大规模的结构生物信息学并加速对蛋白质功能的理解.
主要方法:
- 一个基于神经网络的重新设计模型AlphaFold的开发.
- 将蛋白质结构的物理和生物知识纳入深度学习算法.
- 在机器学习方法中利用多序列对齐.
主要成果:
- 即使没有已知的同类结构,AlphaFold也能够以原子精度定期预测蛋白质结构.
- 在蛋白质结构预测的批判性评估 (CASP14) 中的验证显示,在大多数情况下,准确性与实验结构相比较.
- 这种方法显著优于现有的蛋白质结构预测技术.
结论:
- 开发的AlphaFold模型代表了计算蛋白质结构预测的重大进步.
- 这种方法可以解决结构覆盖的差距,并促进蛋白质功能的机制理解.
- 能够使用前所未有的精度进行大规模的结构生物信息学.
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