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
影响因子:
4.6
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中科院分区:
文献类型:
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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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DOI:
10.1016/j.gpb.2019.11.010
发表时间:
2020-04
期刊:
Genomics, proteomics & bioinformatics
影响因子:
--
作者:
Ning W;Xu H;Jiang P;Cheng H;Deng W;Guo Y;Xue Y
通讯作者:
Xue Y
影响因子:
4.6
作者:
通讯作者:
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DOI:
10.1007/978-1-0716-2317-6_15
发表时间:
2022-01-01
期刊:
Methods in molecular biology (Clifton, N.J.)
影响因子:
--
作者:
Pakhrin, Subash C;Pokharel, Suresh;Kc, Dukka B
通讯作者:
Kc, Dukka B
影响因子:
--
作者:
Hasan, Md. Mehedi;Yang, Shiping;Mollah, Md. Nurul Haque
通讯作者:
Mollah, Md. Nurul Haque
影响因子:
48
作者:
Alley, Ethan C.;Khimulya, Grigory;Church, George M.
通讯作者:
Church, George M.