使用深度学习预测人类乳头瘤病毒与宿主上蛋白相互作用
Sheila Santa1,2, Samuel Kojo Kwofie3, Kwasi Agyenkwa-Mawuli4
1Department of Biochemistry, Cell & Molecular Biology/West African Centre for Cell Biology of Infectious Pathogens (WACCBIP), College of Basic and Applied Sciences, University of Ghana, Accra, Ghana.
Bioinformatics and biology insights
|December 12, 2024
概括
这项研究开发了一个深度学习模型来预测人类乳头瘤病毒 (HPV) 和宿主蛋白相互作用,确定HPV相关癌症发展的关键联系. 该模型准确地预测了相互作用,有助于未来对病毒瘤蛋白和PI3K通路的研究.
科学领域:
- 计算生物学是一种计算生物学.
- 病毒学 病毒学
- 生物信息学是一种生物信息学.
背景情况:
- 人类乳头瘤病毒 (HPV) 通过复杂的病毒宿主蛋白相互作用驱动疾病,特别是涉及PI3K信号通路.
- AKT,IQGAP1和MMP16等蛋白质与HPV相关的癌症的发展有关.
- 研究蛋白质与蛋白质相互作用 (PPI) 的传统方法资源密集;计算方法提供更高的效率.
研究的目的:
- 开发和验证一种深度学习模型,用于预测人类乳头瘤病毒 (HPV) 和宿主蛋白之间的蛋白质-蛋白质相互作用 (PPI).
- 为了确定HPVcoproteins (E6和E7) 和参与癌症发展的宿主蛋白之间的特定相互作用.
主要方法:
- 使用可用的HPV-宿主蛋白相互作用数据训练了一种反复神经网络 (RNN) 算法.
- 该模型是在SPYDER平台上开发的,使用包括TensorFlow,Scikit-learn,Pandas和NumPy在内的Python库.
- 数据被分为训练,验证和测试集,比例为7:1:2.
主要成果:
- 深度学习模型表现出强的表现,MCC得分为0.7937,其他指标准确度超过88%.
- 预测表明HPV 31/18 E6和E7蛋白与宿主蛋白AKT之间的相互作用.
- HPV31 E7显示了与IQGAP1和MMP16的预测相互作用,具有高可信度评分.
结论:
- 开发的模型有效地预测了PI3K通路内的HPVcoproteins (E6,E7) 和宿主蛋白之间的相互作用.
- 这些预测的相互作用表明病毒蛋白在AKT激活中的作用,可能导致HPV相关的癌症.
- 该模型为预测交互体提供了一个强大的工具,促进实验验证,并促进对HPV病变的理解.
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