通过随机对接的蛋白质-蛋白质相互作用模型的显著性得分
Katherine Maia McCoy1, Margaret E Ackerman1,2, Gevorg Grigoryan1,3
1Department of Biological Sciences, Dartmouth College, Hanover, New Hampshire, USA.
Protein science : a publication of the Protein Society
|December 11, 2023
概括
我们开发了一个新的指标,即随机对接 (RD) p值,用于比较蛋白质-蛋白质相互作用 (PPI) 模型. 这个分数使不同蛋白质复合体的模型准确性正常化,使全球比较和更好地评估预测方法.
科学领域:
- 结构生物学是结构生物学.
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
背景情况:
- 通过各种复合体对蛋白质-蛋白质相互作用 (PPI) 模型的准确性进行比较是具有挑战性的,因为它们的尺寸和形状各不相同.
- 现有的指标缺乏规范化,阻碍了全球范围内的绝对准确性比较.
研究的目的:
- 为评估蛋白质-蛋白质相互作用 (PPI) 对接模型开发一个统计学上可靠的指标.
- 为了能够对不同蛋白质复合体的PPI模型性能进行准确和可比的评估.
主要方法:
- 开发了一个随机对接 (RD) 的p值度量.
- 通过将其与随机对接生成的参考分布进行比较来评估模型准确性.
- 利用了来自互动预测临界评估 (CAPRI) 轮 (2017-2020) 的数据.
主要成果:
- RD p值使得得分得分正常化,使模型在不同尺寸和形状的复合体中具有全球可比性.
- 实现特定准确度指标 (RMSD,DOCKQ) 的易度在蛋白质目标之间有很大差异.
- RD p值可以包含先前的接口信息,以区分真正的预测改进与基本建模约束.
结论:
- RD p值为评估蛋白质与蛋白质相互作用 (PPI) 模型的准确性提供了一种标准化和可靠的方法.
- 这一指标有助于更有意义地比较结构生物学中的对接模型性能.
- 计算RDp值的高效代码是公开的.
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