蛋白质-蛋白质接口的共同进化指导了通过de novo结构预测来推断寡合蛋白质复合体的固态度
Max Kilian1,2,3, Ilka B Bischofs1,2,3
1Max-Planck-Institute for Terrestrial Microbiology, Marburg, Germany.
Molecular microbiology
|September 30, 2023
概括
将像AlphaFold2-multimer这样的AI工具与共同进化分析相结合,可以更好地预测蛋白质复杂结构和固体测量. 这种协同作用有助于理解具有未知结构的细菌蛋白质复合体.
科学领域:
- 结构生物信息学 结构生物信息学
- 计算生物学是一种计算生物学.
- 蛋白质结构预测 蛋白质结构预测
背景情况:
- 蛋白质复杂的四级结构和固态度对于功能至关重要.
- 对蛋白质复杂结构的实验性确定具有挑战性.
- 人工智能和共同进化的方法为新的结构预测提供了潜力.
研究的目的:
- 开发一种结合的计算策略,用于预测蛋白质复杂的四级结构和石化学.
- 以共同进化的数据来增强基于AI的结构预测.
- 为了验证Bacillus subtilis子发芽蛋白复合物的方法.
主要方法:
- 使用AlphaFold2-multimer (AF2-multimer) 进行新的结构预测.
- 采用伪概率最大化直接合分析 (plmDCA) 来识别共同演变的残留物对.
- 集成的AF2-多重信任度指标,基于在接口上的共同演变的残留接触的新得分.
主要成果:
- 证明结合AF2-多重体和plmDCA可以改善四级结构和石化测量预测.
- 成功预测了Bacillus subtilis子发芽综合体的结构和非微不足道的固态度.
- 确定了在接口上共同进化对的本地接触的最佳部分.
结论:
- 人工智能和共同进化分析的协同作用组合对于预测蛋白质复合体的四级结构是有效的.
- 这种方法对于结构性无特征的细菌蛋白质复合体特别有价值.
- 接口的共同进化可以增强人工智能驱动的de novo结构预测.
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