通过蛋白质序列嵌入1D卷积神经网络来预测药物发现的热点
Youzhi Zhang1,2,3, Sijie Yao3, Peng Chen1,3
1School of Computer and Information, Anqing Normal University, Anqing, China.
PloS one
|September 18, 2023
概括
本研究介绍了Embed-1dCNN,这是一种使用预训练的蛋白质序列嵌入和1D卷积神经网络来预测蛋白质热点残留的新方法. 这种方法提供了一种更有效的方式来识别蛋白质相互作用和药物向设计的关键部位.
科学领域:
- 计算生物学 计算生物学
- 生物化学 生化学
- 生物信息学是一种生物信息学.
背景情况:
- 蛋白质热点残留物对于调解蛋白质与蛋白质相互作用至关重要.
- 准确识别这些残留物对于理解蛋白质功能和设计药物点至关重要.
- 现有的机器学习方法用于热点预测通常是繁和耗时的.
研究的目的:
- 开发一种新高效的方法来预测蛋白质热点残留物.
- 为了实现这一任务,利用预训练的蛋白序列嵌入和1D卷积神经网络.
- 改进现有的热点预测方法.
主要方法:
- 开发了一个名为Embed-1dCNN的模型,将预训练的蛋白质序列嵌入模型与1D卷积神经网络结合起来.
- 从多个已建立的数据集 (ASEdb,BID,SKEMPI,dbMPIKT) 整合数据,以创建一个全面的数据集.
- 使用SMOTE算法通过扩大正样本来增加培训数据集.
主要成果:
- 在测试组中,Embed-1dCNN模型获得了0.82的显著F1得分.
- 与其他现有的热点预测方法相比,证明了优越的预测性能.
- 这种新方法在识别蛋白质热点残留物方面被证明是有效的.
结论:
- 嵌入-1dCNN模型为预测蛋白质热点残留物提供了一个有希望和高效的方法.
- 这种方法通过使用预先训练的嵌入来简化预测过程,减少了对广泛功能工程的需求.
- 这些发现有助于推进蛋白相互作用分析和药物发现领域.
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