误解3D-PPI:一个网络资源,使用3D结构数据预测误解变体对蛋白质接口的影响
Cecilia Pennica1, Gordon Hanna1, Suhail A Islam1
1Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK.
Journal of molecular biology
|June 25, 2023
概括
我们开发了Missense3D-PPI,这是一个新的工具,用于预测蛋白质与蛋白质相互作用地点的有害误解变体. 该方法改进了分析蛋白质网络和变异效应的现有工具.
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.
- 基因组学就是基因组学.
背景情况:
- 错误的变异可以破坏蛋白质的结构和功能.
- 像Missense3D这样的现有工具分析单个蛋白质结构,但可能会错过影响蛋白质-蛋白质相互作用 (PPI) 的变异.
研究的目的:
- 开发和评估Missense3D-PPI,这是一个计算工具,用于预测Missense变体对PPI接口的影响.
- 评估Missense3D-PPI的性能与现有的变异效应预测方法相比.
主要方法:
- 开发了Missense3D-PPI以分析PPI站点上的Missense变体的结构特征.
- 利用了1,279个误解变异的数据集,跨越434个蛋白质和545个PPI复杂结构.
- 与Missense3D和其他最先进的工具 (BeAtMuSiC,mCSM-PPI2,MutaBind2) 进行基准 Missense3D-PPI.
主要成果:
- 与Missense3D相比,Missense3D-PPI表现出更高的灵敏度 (44%对8%) 和准确度 (58%对40%),用于预测PPI的有害变异 (p < 10^-16).
- 虽然特异性低于Missense3D (84%与98%相比),但Missense3D-PPI的准确性明显高于BeAtMuSiC,mCSM-PPI2和MutaBind2.2.
- 分析的重点是结构性破坏,如改变的链际债券和在接口上的不平衡的充电残留物.
结论:
- 误解3D-PPI是预测蛋白相互作用网络中误解变异的结构后果的宝贵工具.
- 该工具增强了对影响蛋白质-蛋白质相互作用的误解变体的分析,可通过网页门户访问.
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