对人类蛋白质组的高度准确的蛋白质结构预测
Kathryn Tunyasuvunakool1, Jonas Adler2, Zachary Wu2
1DeepMind, London, UK. ktkool@deepmind.com.
Nature
|July 22, 2021
概括
研究人员使用AlphaFold来预测几乎所有人类蛋白质的结构,显著增加了蛋白质结构数据. 这种资源有助于生物学理解和基于结构的药物开发.
科学领域:
- 结构生物学
- 计算生物学
- 基因组学
背景情况:
- 实验中的蛋白质结构只覆盖了17%的人类蛋白质序列.
- 了解蛋白质结构对于生物学见解和治疗开发至关重要.
研究的目的:
- 通过先进的机器学习扩大人类蛋白质组的结构覆盖范围.
- 为研究提供预测人类蛋白质结构的综合数据集.
主要方法:
- 应用AlphaFold机器学习模型来预测98.5%的人类蛋白质的结构.
- 开发和应用新的指标来评估预测信心和解释结构特征.
主要成果:
- 对98.5%的人类蛋白质的预测结构,覆盖58%的残留物,具有可靠的预测.
- 确定了高度可信度的区域 (36%的残留物) 和可能出现混乱的区域.
- 通过案例研究来证明预测的实用性,以产生生物学假设.
结论:
- 现在可以进行大规模,高精度的蛋白质结构预测.
- 这样生成的数据集显著增强了结构覆盖,从而开辟了新的研究途径.
- 免费可用的预测将使科学界在基于结构的研究中获得权力.
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