预先训练的蛋白质语言模型为预测Arabidopsis蛋白质-蛋白质相互作用提供了新的信息
Kewei Zhou1, Chenping Lei1, Jingyan Zheng1
1State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, 100193, China.
Plant methods
|December 7, 2023
概括
这项研究介绍了ESMAraPPI,一种使用ESM-1b蛋白嵌入和MLP的机器学习模型,以准确预测Arabidopsis中的蛋白质-蛋白质相互作用. ESMAraPPI表现出强烈的概括性,超过了目前用于Arabidopsis互动组预测的现有方法.
科学领域:
- 植物生物学 植物生物学
- 生物信息学是一种生物信息学.
- 计算生物学是一种计算生物学.
背景情况:
- 蛋白与蛋白相互作用 (PPI) 对植物生物过程,生长和发育至关重要.
- 鉴定Arabidopsis中的PPI对于了解植物科学和作物改进至关重要.
- 目前的阿拉比多普西斯互原子数据是不完整的,需要有效的预测工具.
研究的目的:
- 开发和评估一种用于预测Arabidopsis PPI的新型机器学习模型.
- 为了提高PPI预测,利用大规模预训练的蛋白质语言模型.
- 为了提供一个快速而准确的工具来扩展阿拉比多普西斯互动体.
主要方法:
- 利用ESM-1b蛋白语言模型生成高维蛋白序列嵌入.
- 采用多层感知器 (MLP) 模型来预测使用ESM-1b嵌入式的PPI.
- 实施了严格的培训和评估数据集,以防止高估绩效.
主要成果:
- 开发的ESMAraPPI模型,结合ESM-1b和MLP,在预测Arabidopsis PPI方面取得了卓越的准确性.
- ESMAraPPI表现出强大的概括和推断能力,在一个独立的测试集上,AUPR为0.810.
- 该模型的表现优于其他预先训练的语言模型和现有的PPI预测器.
结论:
- 来自ESM-1b的蛋白质序列嵌入捕获了用于PPI预测的重要语义信息.
- ESMAraPPI模型有效地预测了Arabidopsis的PPI,展示了ESM-1b与MLP结合的力量.
- ESMAraPPI是一个有前途的工具,可以加速发现阿拉比多普西斯互动体的发现.
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