用MutPred-PPI预测误解变异的相互作用特异性蛋白质-蛋白质相互作用干扰
Ross Stewart1, Florent Laval2,3,4,5,6,7,8, Georges Coppin2,3,4,6
1Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA.
bioRxiv : the preprint server for biology
|January 7, 2026
概括
MutPred-PPI预测了遗传变异如何破坏蛋白质与蛋白质相互作用 (PPI),超过现有的工具. 这种计算方法有助于通过识别误解变体的特定相互作用效应来理解疾病机制.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 蛋白与蛋白相互作用 (PPI) 对细胞功能至关重要,它们因遗传变异而被破坏可能导致疾病.
- 现有的计算工具往往无法预测变体的相互作用特异性 (边缘性) 影响,特别是当稳定性不受影响时.
- 需要可扩展的计算方法来评估变体对PPI的影响.
研究的目的:
- 开发和验证MutPred-PPI,这是一个新的图表注意力网络,用于预测误解变体的边缘效应.
- 评估MutPred-PPI的概括性和性能,与各种数据集上的现有方法进行比较.
- 通过分析来自临床和人口数据库的变异来证明MutPred-PPI的生物医学相关性.
主要方法:
- 使用基于AlphaFold3的蛋白质复合体接触图的图表注意力网络架构.
- 集成的蛋白质语言模型嵌入到图节点中.
- 使用IGVF联盟的组交叉验证和基准数据集进行了严格的评估.
主要成果:
- 在交叉验证中,MutPred-PPI实现了卓越的性能,在可见蛋白质上AUC为0.85,在不可见蛋白质上为0.72.
- 该模型表现出强大的通用性,超过了所有基线方法.
- 对临床变异的分析揭示了不同疾病类型的PPI扰动机制,MutPred-PPI捕获了功能相关的效应.
结论:
- MutPred-PPI是预测相互作用特异性变异效应的强大工具,促进了对基因疾病背后的分子机制的理解.
- 这项研究突出了从癌症到神经发育障碍等各种疾病中明显的PPI破坏模式.
- MutPred-PPI对未见的蛋白质进行概括的能力强调了其在变异解释和精确医学中广泛应用的潜力.
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