MGPPI:用于可解释的蛋白质-蛋白质相互作用预测的多尺度图形神经网络
Shiwei Zhao1, Zhenyu Cui1, Gonglei Zhang1
1College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China.
Frontiers in genetics
|July 30, 2024
概括
我们开发了MGPPI,这是一种用于预测蛋白质与蛋白质相互作用 (PPI) 的新型深度学习模型. 该方法准确地识别了关键的结合部位,有助于癌症诊断,药物开发和个性化癌症治疗策略.
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.
- 结构生物学中的机器学习
背景情况:
- 蛋白与蛋白相互作用 (PPI) 对生物过程至关重要,影响癌症诊断和药物开发.
- 现有的计算PPI预测方法难以提取全面的结构信息,缺乏可解释性.
- 需要准确,可解释的计算模型来预测PPI,以推进生物医学应用.
研究的目的:
- 引入MGPPI,一个多尺度图形卷积神经网络,用于增强蛋白质-蛋白质相互作用预测.
- 改进利用多尺度模块和多个卷积层提取本地和全球蛋白质结构信息.
- 通过引入梯度加权交互激活映射 (Grad-WAM) 来提高模型的解释性,以确定关键的结合残留点.
主要方法:
- 开发了MGPPI,一个多尺度图形卷积神经网络模型,包含一个多尺度模块和多个卷积层.
- 实施了Grad-WAM,一种新的视觉解释方法,以突出显示参与蛋白质相互作用的关键残留部位.
- 对各种数据集的最先进方法进行MGPPI性能评估,包括多种数据和癌症患者生存数据集.
主要成果:
- 在PPI预测方面,MGPPI显著优于现有的方法,在物种之间表现出强大的概括能力.
- 该Grad-WAM方法成功地确定了SARS-COV-2尖端蛋白和人类ACE2受体的关键结合部位.
- 通过Grad-WAM识别的残留物显示出潜在的生物标志物,用于预测各种癌症类型的患者存活率.
结论:
- MGPPI为PPI预测提供了一个高度准确和可解释的方法,克服了以前方法的局限性.
- 该模型有助于识别潜在的药物点,并指导个性化癌症治疗.
- 格拉德-WAM为蛋白质结合机制和临床相关性提供了宝贵的见解,支持生物标志物发现.
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