PLNet:持续的拉普拉斯神经网络用于蛋白质-蛋白质结合自由能量预测
Xingjian Xu1, Chunmei Wang1, Guo-Wei Wei2,3,4
1Department of Mathematics, University of Florida, Gainesville, Florida, USA.
Protein science : a publication of the Protein Society
|November 20, 2025
概括
我们开发了一种新的持久拉普拉斯神经网络 (PLNet),用于预测蛋白质与蛋白质相互作用 (PPI) 结合自由能量. PLNet有效地使用拓特征,在一个新的基准数据集上实现0.80的相关性.
科学领域:
- 计算生物学 计算生物学
- 结构生物信息学 结构生物信息学
- 机器学习 机器学习
背景情况:
- 基于拓学的建模推进了分子预测,特别是蛋白质 - 连接体结合.
- 由于无效的拓特征使用和数据限制,预测蛋白质-蛋白质相互作用 (PPI) 结合自由能量具有挑战性.
研究的目的:
- 引入一种新的机器学习框架,用于预测具有约束力的自由能源的PPI.
- 解决PPI预测中捕获拓信息的当前方法的局限性.
主要方法:
- 开发了持久拉普拉斯神经网络 (PLNet) 框架.
- 使用持久的拉普拉斯特征和蛋白质语言模型嵌入的编码蛋白质链.
- 组建了一个新的基准数据集 (P2P),包含6886个蛋白质复合体.
主要成果:
- 在P2P数据集上使用离开-蛋白质-退出交叉验证实现了0.80的皮尔森相关性.
- 与梯度增强决策树基线相比,PLNet表现优越.
- 突出了PLNet在捕获PPI预测的复杂拓意识描述器方面的优势.
结论:
- PLNet框架为预测具有约束力的自由能源PPI提供了一个有希望的方法.
- 整合持久的拉普拉斯特征增强了复杂分子相互作用的预测.
- 对于未来的PPI研究,P2P数据集是一个有价值的资源.
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