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Article

GPS-pPLM: A Language Model for Prediction of Prokaryotic Phosphorylation Sites

by
Chi Zhang
1,†,
Dachao Tang
1,†,
Cheng Han
1,
Yujie Gou
1,
Miaomiao Chen
1,
Xinhe Huang
1,
Dan Liu
1,
Miaoying Zhao
1,
Leming Xiao
1,
Qiang Xiao
2,
Di Peng
1,* and
Yu Xue
1,*
1
Department of Bioinformatics and Systems Biology, MOE Key Laboratory of Molecular Biophysics, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
2
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Cells 2024, 13(22), 1854; https://doi.org/10.3390/cells13221854
Submission received: 23 September 2024 / Revised: 6 November 2024 / Accepted: 7 November 2024 / Published: 8 November 2024
(This article belongs to the Section Cell Methods)
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Abstract

In the prokaryotic kingdom, protein phosphorylation serves as one of the most important posttranslational modifications (PTMs) and is involved in orchestrating a broad spectrum of biological processes. Here, we report an updated online server named the group-based prediction system for prokaryotic phosphorylation language model (GPS-pPLM), used for predicting phosphorylation sites (p-sites) in prokaryotes. For model training, two deep learning methods, a transformer and a deep neural network, were employed, and a total of 10 sequence features and contextual features were integrated. Using 44,839 nonredundant p-sites in 16,041 proteins from 95 prokaryotes, two general models for the prediction of O-phosphorylation and N-phosphorylation were first pretrained and then fine-tuned to construct 6 predictors specific for each phosphorylatable residue type as well as 134 species-specific predictors. Compared with other existing tools, the GPS-pPLM exhibits higher accuracy in predicting prokaryotic O-phosphorylation p-sites. Protein sequences in FASTA format or UniProt accession numbers can be submitted by users, and the predicted results are displayed in tabular form. In addition, we annotate the predicted p-sites with knowledge from 22 public resources, including experimental evidence, 3D structures, and disorder tendencies. The online service of the GPS-pPLM is freely accessible for academic research.
Keywords: posttranslational modification; phosphorylation; language model; deep learning; prokaryote posttranslational modification; phosphorylation; language model; deep learning; prokaryote

Share and Cite

MDPI and ACS Style

Zhang, C.; Tang, D.; Han, C.; Gou, Y.; Chen, M.; Huang, X.; Liu, D.; Zhao, M.; Xiao, L.; Xiao, Q.; et al. GPS-pPLM: A Language Model for Prediction of Prokaryotic Phosphorylation Sites. Cells 2024, 13, 1854. https://doi.org/10.3390/cells13221854

AMA Style

Zhang C, Tang D, Han C, Gou Y, Chen M, Huang X, Liu D, Zhao M, Xiao L, Xiao Q, et al. GPS-pPLM: A Language Model for Prediction of Prokaryotic Phosphorylation Sites. Cells. 2024; 13(22):1854. https://doi.org/10.3390/cells13221854

Chicago/Turabian Style

Zhang, Chi, Dachao Tang, Cheng Han, Yujie Gou, Miaomiao Chen, Xinhe Huang, Dan Liu, Miaoying Zhao, Leming Xiao, Qiang Xiao, and et al. 2024. "GPS-pPLM: A Language Model for Prediction of Prokaryotic Phosphorylation Sites" Cells 13, no. 22: 1854. https://doi.org/10.3390/cells13221854

APA Style

Zhang, C., Tang, D., Han, C., Gou, Y., Chen, M., Huang, X., Liu, D., Zhao, M., Xiao, L., Xiao, Q., Peng, D., & Xue, Y. (2024). GPS-pPLM: A Language Model for Prediction of Prokaryotic Phosphorylation Sites. Cells, 13(22), 1854. https://doi.org/10.3390/cells13221854

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