一个具有结构信息的人类蛋白质-蛋白质相互作用体揭示了由疾病突变引起的全蛋白质体乱
Dapeng Xiong1,2,3, Yunguang Qiu4,5, Junfei Zhao6
1Department of Computational Biology, Cornell University, Ithaca, NY, USA.
Nature biotechnology
|October 25, 2024
概括
我们开发了PIONEER,这是一种深度学习工具,可以预测蛋白质-蛋白质接口,以了解疾病. 开拓者通过分析蛋白质相互作用来识别疾病突变,并帮助精准医学.
科学领域:
- 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.
- 基因组学就是基因组学.
- 系统生物学 系统生物学
背景情况:
- 将遗传发现转化为疾病病理生物学和治疗发现需要理解蛋白质相互作用.
- 预测蛋白质-蛋白质接口的现有方法在范围和准确性上是有限的.
- 综合性,结构上有信息的蛋白质互动体对生物和医学研究至关重要.
研究的目的:
- 引入PIONEER (蛋白质-蛋白质交互 iNtErfacE pRediction),一个集体深度学习框架.
- 预测人类和模型生物体蛋白质相互作用的蛋白质结合伙伴特定接口.
- 为疾病研究和药物发现产生全面的,结构上有信息的蛋白质相互作用体.
主要方法:
- 开发了一个集体深度学习框架,PIONEER,用于预测蛋白质-蛋白质相互作用接口.
- 应用了PIONEER来生成人类和七个模型生物体的互动体.
- 分析了33种癌症类型的整体外基因组测序数据,以确定蛋白质-蛋白质接口中的体质突变.
主要成果:
- 开拓者显著优于现有的最先进的界面预测方法,具有实验验证.
- 与疾病相关的突变丰富于PIONEER预测的蛋白质-蛋白质接口,影响疾病预后和药物反应.
- 鉴定了586种与癌症相关的蛋白质-蛋白质相互作用 (oncoPPIs),这些相互作用富含了接口突变,与患者的生存和药物反应有关.
结论:
- 开拓者提供了一个强大的深度学习工具,用于预测蛋白质-蛋白质接口和生成相互作用体.
- 该框架有助于识别与疾病相关的等位基因的功能后果.
- 开拓者通过提供对疾病中多层次互原子网络改变的见解,使精准医学成为可能.
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