xCAPT5:使用深度和宽度多核聚合卷积神经网络与蛋白质语言模型进行蛋白质-蛋白质相互作用预测
1Faculty of Information Technology, VNU University of Engineering and Technology, 144 Xuan Thuy, Hanoi, 10000, Vietnam. hai.dang@vnu.edu.vn.
BMC bioinformatics
|March 9, 2024
概括
我们开发了xCAPT5,这是一个新的深度学习模型,它使用蛋白质语言模型嵌入来更准确地预测蛋白质-蛋白质相互作用 (PPI). 这种方法通过改善蛋白质相互作用的预测来增强计算生物学.
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.
- 在基因组学中的机器学习.
背景情况:
- 从序列数据中预测蛋白质与蛋白质相互作用 (PPI) 是一个重大挑战.
- 现有的计算方法还没有充分利用蛋白质语言模型嵌入中的丰富信息.
- 需要先进的神经网络来从蛋白质序列中提取多方面的表示.
研究的目的:
- 引入xCAPT5,一个设计用于预测PPI的混合分类器.
- 为了利用T5-XL-UniRef50产生全面的氨基酸嵌入.
- 开发一个高效的神经网络来提取复杂的交互特征.
主要方法:
- 使用T5-XL-UniRef50蛋白质大语言模型进行序列嵌入.
- 采用多核深卷积的罗神经网络来捕获交互特征.
- 集成了XGBoost算法,以提高分类性能.
- 应用了最大和平均的深度连接,以实现高效的学习.
主要成果:
- xCAPT5有效地提取关键功能,计算成本低.
- 该模型在二进制PPI预测中表现出卓越的性能.
- 在跨多个基准数据集的交叉验证中取得了出色的结果.
- 在各种物种和相似情境中展示了强大的概括能力.
结论:
- 这项研究开创了利用深 convolutional 网络从大型蛋白质语言模型中提取信息嵌入的先驱.
- xCAPT5的性能优于当前对二进制PPI预测的最先进方法.
- 拟议的方法为计算生物学和药物发现提供了一个强大的新工具.
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