Document Type
Article
Publication Date
10-8-2022
Department
Department of Computer Science
Abstract
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.
Publication Title
Scientific Reports
Recommended Citation
Pokharel, S.,
Pratyush, P.,
Heinzinger, M.,
Newman, R.,
&
KC, D.
(2022).
Improving protein succinylation sites prediction using embeddings from protein language model.
Scientific Reports,
12(1).
http://doi.org/10.1038/s41598-022-21366-2
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/16462
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
© The Author(s) 2022. Publisher’s version of record: https://doi.org/10.1038/s41598-022-21366-2