Improving protein succinylation sites prediction using embeddings from protein language model.

Improving protein succinylation sites prediction using embeddings from protein language model.
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DOI:
10.1038/s41598-022-21366-2
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发表时间:
2022-10-08
期刊:
影响因子:
4.6
通讯作者:
--
中科院分区:
综合性期刊3区
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--
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蛋白质琥珀酰化是一种重要的翻译后修饰(PTM),负责细胞中许多重要的代谢活动,包括细胞呼吸,调节和修复。在这里,我们提出了一种新的方法,将监督词嵌入的特征与深度学习框架中称为ProtT 5-XL-UniRef 50(以下称为ProtT 5)的蛋白质语言模型的嵌入相结合,以预测蛋白质琥珀酰化位点。据我们所知,这是利用预训练的蛋白质语言模型嵌入来预测蛋白质琥珀酰化位点的首次尝试之一。与现有方法相比,所提出的模型(称为LMSuccSite)实现了最先进的结果,MCC,灵敏度和特异性的性能评分分别为0.36,0.79,0.79。LMCusuSite可能是探索琥珀酰化及其在细胞生理学和疾病中的作用的宝贵资源。
Protein succinylation is an important post-translational modification (PTM) responsible for many vital metabolic activities in cells, including cellular respiration, regulation, and repair. Here, we present a novel approach that combines features from supervised word embedding with embedding from a protein language model called ProtT5-XL-UniRef50 (hereafter termed, ProtT5) in a deep learning framework to predict protein succinylation sites. To our knowledge, this is one of the first attempts to employ embedding from a pre-trained protein language model to predict protein succinylation sites. The proposed model, dubbed LMSuccSite, achieves state-of-the-art results compared to existing methods, with performance scores of 0.36, 0.79, 0.79 for MCC, sensitivity, and specificity, respectively. LMSuccSite is likely to serve as a valuable resource for exploration of succinylation and its role in cellular physiology and disease.
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